bayesplot/0000755000176200001440000000000014563320543012261 5ustar liggesusersbayesplot/NAMESPACE0000644000176200001440000001257214556270424013513 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method("[",neff_ratio) S3method("[",rhat) S3method(apply_transformations,array) S3method(apply_transformations,matrix) S3method(diagnostic_factor,neff_ratio) S3method(diagnostic_factor,rhat) S3method(log_posterior,CmdStanMCMC) S3method(log_posterior,stanfit) S3method(log_posterior,stanreg) S3method(melt_mcmc,matrix) S3method(melt_mcmc,mcmc_array) S3method(neff_ratio,CmdStanMCMC) S3method(neff_ratio,stanfit) S3method(neff_ratio,stanreg) S3method(num_chains,data.frame) S3method(num_chains,mcmc_array) S3method(num_iters,data.frame) S3method(num_iters,mcmc_array) S3method(num_params,data.frame) S3method(num_params,mcmc_array) S3method(nuts_params,CmdStanMCMC) S3method(nuts_params,list) S3method(nuts_params,stanfit) S3method(nuts_params,stanreg) S3method(parameter_names,array) S3method(parameter_names,default) S3method(parameter_names,matrix) S3method(plot,bayesplot_grid) S3method(plot,bayesplot_scheme) S3method(pp_check,default) S3method(print,bayesplot_function_list) S3method(print,bayesplot_grid) S3method(print,bayesplot_scheme) S3method(rhat,CmdStanMCMC) S3method(rhat,stanfit) S3method(rhat,stanreg) export(abline_01) export(available_mcmc) export(available_ppc) export(available_ppd) export(bayesplot_grid) export(bayesplot_theme_get) export(bayesplot_theme_replace) export(bayesplot_theme_set) export(bayesplot_theme_update) export(color_scheme_get) export(color_scheme_set) export(color_scheme_view) export(example_group_data) export(example_mcmc_draws) export(example_x_data) export(example_y_data) export(example_yrep_draws) export(facet_bg) export(facet_text) export(grid_lines) export(hline_0) export(hline_at) export(lbub) export(legend_move) export(legend_none) export(legend_text) export(log_posterior) export(mcmc_acf) export(mcmc_acf_bar) export(mcmc_areas) export(mcmc_areas_data) export(mcmc_areas_ridges) export(mcmc_areas_ridges_data) export(mcmc_combo) export(mcmc_dens) export(mcmc_dens_chains) export(mcmc_dens_chains_data) export(mcmc_dens_overlay) export(mcmc_hex) export(mcmc_hist) export(mcmc_hist_by_chain) export(mcmc_intervals) export(mcmc_intervals_data) export(mcmc_neff) export(mcmc_neff_data) export(mcmc_neff_hist) export(mcmc_nuts_acceptance) export(mcmc_nuts_divergence) export(mcmc_nuts_energy) export(mcmc_nuts_stepsize) export(mcmc_nuts_treedepth) export(mcmc_pairs) export(mcmc_parcoord) export(mcmc_parcoord_data) export(mcmc_rank_ecdf) export(mcmc_rank_hist) export(mcmc_rank_overlay) export(mcmc_recover_hist) export(mcmc_recover_intervals) export(mcmc_recover_scatter) export(mcmc_rhat) export(mcmc_rhat_data) export(mcmc_rhat_hist) export(mcmc_scatter) export(mcmc_trace) export(mcmc_trace_data) export(mcmc_trace_highlight) export(mcmc_violin) export(neff_ratio) export(nuts_params) export(overlay_function) export(pairs_condition) export(pairs_style_np) export(panel_bg) export(param_glue) export(param_range) export(parcoord_style_np) export(plot_bg) export(pp_check) export(ppc_bars) export(ppc_bars_data) export(ppc_bars_grouped) export(ppc_boxplot) export(ppc_data) export(ppc_dens) export(ppc_dens_overlay) export(ppc_dens_overlay_grouped) export(ppc_ecdf_overlay) export(ppc_ecdf_overlay_grouped) export(ppc_error_binned) export(ppc_error_data) export(ppc_error_hist) export(ppc_error_hist_grouped) export(ppc_error_scatter) export(ppc_error_scatter_avg) export(ppc_error_scatter_avg_grouped) export(ppc_error_scatter_avg_vs_x) export(ppc_freqpoly) export(ppc_freqpoly_grouped) export(ppc_hist) export(ppc_intervals) export(ppc_intervals_data) export(ppc_intervals_grouped) export(ppc_km_overlay) export(ppc_km_overlay_grouped) export(ppc_loo_intervals) export(ppc_loo_pit) export(ppc_loo_pit_data) export(ppc_loo_pit_overlay) export(ppc_loo_pit_qq) export(ppc_loo_ribbon) export(ppc_pit_ecdf) export(ppc_pit_ecdf_grouped) export(ppc_ribbon) export(ppc_ribbon_data) export(ppc_ribbon_grouped) export(ppc_rootogram) export(ppc_scatter) export(ppc_scatter_avg) export(ppc_scatter_avg_data) export(ppc_scatter_avg_grouped) export(ppc_scatter_data) export(ppc_stat) export(ppc_stat_2d) export(ppc_stat_data) export(ppc_stat_freqpoly) export(ppc_stat_freqpoly_grouped) export(ppc_stat_grouped) export(ppc_violin_grouped) export(ppd_boxplot) export(ppd_data) export(ppd_dens) export(ppd_dens_overlay) export(ppd_ecdf_overlay) export(ppd_freqpoly) export(ppd_freqpoly_grouped) export(ppd_hist) export(ppd_intervals) export(ppd_intervals_data) export(ppd_intervals_grouped) export(ppd_ribbon) export(ppd_ribbon_data) export(ppd_ribbon_grouped) export(ppd_stat) export(ppd_stat_2d) export(ppd_stat_data) export(ppd_stat_freqpoly) export(ppd_stat_freqpoly_grouped) export(ppd_stat_grouped) export(rhat) export(scatter_style_np) export(theme_default) export(trace_style_np) export(vars) export(vline_0) export(vline_at) export(xaxis_text) export(xaxis_ticks) export(xaxis_title) export(yaxis_text) export(yaxis_ticks) export(yaxis_title) import(ggplot2) import(rlang) import(stats) importFrom(dplyr,"%>%") importFrom(dplyr,across) importFrom(dplyr,all_of) importFrom(dplyr,arrange) importFrom(dplyr,count) importFrom(dplyr,full_join) importFrom(dplyr,group_by) importFrom(dplyr,inner_join) importFrom(dplyr,left_join) importFrom(dplyr,mutate) importFrom(dplyr,n) importFrom(dplyr,one_of) importFrom(dplyr,pull) importFrom(dplyr,rename) importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(dplyr,top_n) importFrom(dplyr,ungroup) importFrom(dplyr,vars) importFrom(ggplot2,"%+replace%") bayesplot/man/0000755000176200001440000000000014560773547013051 5ustar liggesusersbayesplot/man/bayesplot_theme_get.Rd0000644000176200001440000000635714212422623017352 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot-ggplot-themes.R \name{bayesplot_theme_get} \alias{bayesplot_theme_get} \alias{bayesplot_theme_set} \alias{bayesplot_theme_update} \alias{bayesplot_theme_replace} \title{Get, set, and modify the active \strong{bayesplot} theme} \usage{ bayesplot_theme_get() bayesplot_theme_set(new = theme_default()) bayesplot_theme_update(...) bayesplot_theme_replace(...) } \arguments{ \item{new}{The new theme (list of theme elements) to use. This is analogous to the \code{new} argument to \code{\link[ggplot2:theme_get]{ggplot2::theme_set()}}.} \item{...}{A named list of theme settings.} } \value{ \code{bayesplot_theme_get()} returns the current theme. The other three functions (set, update, replace) invisibly return the \emph{previous} theme so it can be saved and easily restored later. This is the same behavior as the \strong{ggplot2} versions of these functions. } \description{ These functions are the \strong{bayesplot} equivalent to \strong{ggplot2}'s \code{\link[ggplot2:theme_get]{ggplot2::theme_set()}} and friends. They set, get, and update the active theme but only apply them to \code{bayesplots}. The current/active theme is automatically applied to every \code{bayesplot} you draw. Use \code{bayesplot_theme_get()} to get the current \strong{bayesplot} theme and \code{bayesplot_theme_set()} to set a new theme. \code{bayesplot_theme_update()} and \code{bayesplot_theme_replace()} are shorthands for changing individual elements. } \details{ \code{bayesplot_theme_set()} and friends only apply to \code{bayesplots}. However, \code{\link[ggplot2:theme_get]{ggplot2::theme_set()}} can also be used to change the \strong{bayesplot} theme. Currently, setting a theme with \code{ggplot2::theme_set()} (other than the \strong{ggplot2} default \code{\link[ggplot2:ggtheme]{ggplot2::theme_grey()}}) will override the \strong{bayesplot} theme. } \examples{ library(ggplot2) # plot using the current value of bayesplot_theme_get() # (the default is bayesplot::theme_default()) x <- example_mcmc_draws() mcmc_hist(x) # change the bayesplot theme to theme_minimal and save the old theme old <- bayesplot_theme_set(theme_minimal()) mcmc_hist(x) # change back to the previous theme bayesplot_theme_set(old) mcmc_hist(x) # change the default font size and family for bayesplots bayesplot_theme_update(text = element_text(size = 16, family = "sans")) mcmc_hist(x) # change back to the default bayesplot_theme_set() # same as bayesplot_theme_set(theme_default()) mcmc_hist(x) # updating theme elements color_scheme_set("brightblue") bayesplot_theme_set(theme_dark()) mcmc_hist(x) bayesplot_theme_update(panel.background = element_rect(fill = "black")) mcmc_hist(x) # to get the same plot without updating the theme we could also have # used the bayeplot convenience function panel_bg() bayesplot_theme_set(theme_dark()) mcmc_hist(x) + panel_bg(fill = "black") # reset bayesplot_theme_set() } \seealso{ \code{\link[=theme_default]{theme_default()}} for the default \strong{bayesplot} theme. \link{bayesplot-helpers} for a variety of convenience functions, many of which provide shortcuts for tweaking theme elements after creating a plot. \link{bayesplot-colors} to set or view the color scheme used for plotting. } bayesplot/man/PPD-overview.Rd0000644000176200001440000000453714335220171015614 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppd-overview.R \name{PPD-overview} \alias{PPD-overview} \alias{PPD} \title{Plots of posterior or prior predictive distributions} \description{ The \strong{bayesplot} PPD module provides various plotting functions for creating graphical displays of simulated data from the posterior or prior predictive distribution. These plots are essentially the same as the corresponding \link{PPC} plots but without showing any observed data. Because these are not "checks" compared to data we use PPD (for prior/posterior predictive distribution) instead of PPC (for prior/posterior predictive check). } \section{PPD plotting functions}{ The functions for plotting prior and posterior predictive distributions without observed data each have the prefix \code{ppd_} and all have a required argument \code{ypred} (a matrix of predictions). The plots are organized into several categories, each with its own documentation: \itemize{ \item \link{PPD-distributions}: Histograms, kernel density estimates, boxplots, and other plots of multiple simulated datasets (rows) in \code{ypred}. These are the same as the plots in \link{PPC-distributions} but without including any comparison to \code{y}. \item \link{PPD-intervals}: Interval estimates for each predicted observations (columns) in \code{ypred}. The x-axis variable can be optionally specified by the user (e.g. to plot against against a predictor variable or over time).These are the same as the plots in \link{PPC-intervals} but without including any comparison to \code{y}. \item \link{PPD-test-statistics}: The distribution of a statistic, or a pair of statistics, over the simulated datasets (rows) in \code{ypred}. These are the same as the plots in \link{PPC-test-statistics} but without including any comparison to \code{y}. } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) } \seealso{ Other PPDs: \code{\link{PPD-distributions}}, \code{\link{PPD-intervals}}, \code{\link{PPD-test-statistics}} } \concept{PPDs} bayesplot/man/MCMC-parcoord.Rd0000644000176200001440000001774114335220171015654 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-parcoord.R \name{MCMC-parcoord} \alias{MCMC-parcoord} \alias{mcmc_parcoord} \alias{mcmc_parcoord_data} \alias{parcoord_style_np} \title{Parallel coordinates plot of MCMC draws} \usage{ mcmc_parcoord( x, pars = character(), regex_pars = character(), transformations = list(), ..., size = 0.2, alpha = 0.3, np = NULL, np_style = parcoord_style_np() ) mcmc_parcoord_data( x, pars = character(), regex_pars = character(), transformations = list(), np = NULL ) parcoord_style_np(div_color = "red", div_size = 0.2, div_alpha = 0.2) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{pars}{An optional character vector of parameter names. If neither \code{pars} nor \code{regex_pars} is specified then the default is to use \emph{all} parameters. As of version \verb{1.7.0}, \strong{bayesplot} also supports 'tidy' parameter selection by specifying \code{pars = vars(...)}, where \code{...} is specified the same way as in \link[dplyr:select]{dplyr::select(...)} and similar functions. Examples of using \code{pars} in this way can be found on the \link[=tidy-params]{Tidy parameter selection} page.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} \item{transformations}{Optionally, transformations to apply to parameters before plotting. If \code{transformations} is a function or a single string naming a function then that function will be used to transform all parameters. To apply transformations to particular parameters, the \code{transformations} argument can be a named list with length equal to the number of parameters to be transformed. Currently only univariate transformations of scalar parameters can be specified (multivariate transformations will be implemented in a future release). If \code{transformations} is a list, the name of each list element should be a parameter name and the content of each list element should be a function (or any item to match as a function via \code{\link[=match.fun]{match.fun()}}, e.g. a string naming a function). If a function is specified by its name as a string (e.g. \code{"log"}), then it can be used to construct a new parameter label for the appropriate parameter (e.g. \code{"log(sigma)"}). If a function itself is specified (e.g. \code{log} or \code{function(x) log(x)}) then \code{"t"} is used in the new parameter label to indicate that the parameter is transformed (e.g. \code{"t(sigma)"}). Note: due to partial argument matching \code{transformations} can be abbreviated for convenience in interactive use (e.g., \code{transform}).} \item{...}{Currently ignored.} \item{size, alpha}{Arguments passed on to \code{\link[ggplot2:geom_path]{ggplot2::geom_line()}}.} \item{np}{For models fit using \link{NUTS} (more generally, any \href{https://en.wikipedia.org/wiki/Symplectic_integrator}{symplectic integrator}), an optional data frame providing NUTS diagnostic information. The data frame should be the object returned by \code{\link[=nuts_params]{nuts_params()}} or one with the same structure.} \item{np_style}{A call to the \code{parcoord_style_np()} helper function to specify arguments controlling the appearance of superimposed lines representing NUTS diagnostics (in this case divergences) if the \code{np} argument is specified.} \item{div_color, div_size, div_alpha}{Optional arguments to the \code{parcoord_style_np()} helper function that are eventually passed to \code{\link[ggplot2:geom_path]{ggplot2::geom_line()}} if the \code{np} argument is also specified. They control the color, size, and transparency specifications for showing divergences in the plot. The default values are displayed in the \strong{Usage} section above.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Parallel coordinates plot of MCMC draws (one dimension per parameter). See the \strong{Plot Descriptions} section below for details, and see \href{https://github.com/jgabry/bayes-vis-paper#readme}{Gabry et al. (2019)} for more background and a real example. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_parcoord()}}{ \href{https://en.wikipedia.org/wiki/Parallel_coordinates}{Parallel coordinates plot} of MCMC draws. There is one dimension per parameter along the horizontal axis and each set of connected line segments represents a single MCMC draw (i.e., a vector of length equal to the number of parameters). The parallel coordinates plot is most useful if the optional HMC/NUTS diagnostic information is provided via the \code{np} argument. In that case divergences are highlighted in the plot. The appearance of the divergences can be customized using the \code{np_style} argument and the \code{parcoord_style_np} helper function. This version of the plot is the same as the parallel coordinates plot described in Gabry et al. (2019). When the plotted model parameters are on very different scales the \code{transformations} argument can be useful. For example, to standardize all variables before plotting you could use function \code{(x - mean(x))/sd(x)} when specifying the \code{transformations} argument to \code{mcmc_parcoord}. See the \strong{Examples} section for how to do this. } } } \examples{ color_scheme_set("pink") x <- example_mcmc_draws(params = 5) mcmc_parcoord(x) mcmc_parcoord(x, regex_pars = "beta") \dontrun{ # Example using a Stan demo model library(rstan) fit <- stan_demo("eight_schools") draws <- as.array(fit, pars = c("mu", "tau", "theta", "lp__")) np <- nuts_params(fit) str(np) levels(np$Parameter) color_scheme_set("brightblue") mcmc_parcoord(draws, alpha = 0.05) mcmc_parcoord(draws, np = np) # customize appearance of divergences color_scheme_set("darkgray") div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) mcmc_parcoord(draws, size = 0.25, alpha = 0.1, np = np, np_style = div_style) # to use a transformation (e.g., standardizing all the variables can be helpful) # specify the 'transformations' argument (though partial argument name # matching means we can just use 'trans' or 'transform') mcmc_parcoord( draws, transform = function(x) {(x - mean(x)) / sd(x)}, size = 0.25, alpha = 0.1, np = np, np_style = div_style ) # mcmc_parcoord_data returns just the data in a conventient form for plotting d <- mcmc_parcoord_data(x, np = np) head(d) tail(d) } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) Hartikainen, A. (2017, Aug 23). Concentration of divergences (Msg 21). Message posted to The Stan Forums: \url{https://discourse.mc-stan.org/t/concentration-of-divergences/1590/21}. } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/PPC-test-statistics.Rd0000644000176200001440000001661214556270424017124 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-test-statistics.R \name{PPC-test-statistics} \alias{PPC-test-statistics} \alias{PPC-statistics} \alias{ppc_stat} \alias{ppc_stat_grouped} \alias{ppc_stat_freqpoly} \alias{ppc_stat_freqpoly_grouped} \alias{ppc_stat_2d} \alias{ppc_stat_data} \title{PPC test statistics} \usage{ ppc_stat( y, yrep, stat = "mean", ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppc_stat_grouped( y, yrep, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppc_stat_freqpoly( y, yrep, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE ) ppc_stat_freqpoly_grouped( y, yrep, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE ) ppc_stat_2d(y, yrep, stat = c("mean", "sd"), ..., size = 2.5, alpha = 0.7) ppc_stat_data(y, yrep, group = NULL, stat) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{stat}{A single function or a string naming a function, except for the 2D plot which requires a vector of exactly two names or functions. In all cases the function(s) should take a vector input and return a scalar statistic. If specified as a string (or strings) then the legend will display the function name(s). If specified as a function (or functions) then generic naming is used in the legend.} \item{...}{Currently unused.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{freq}{For histograms, \code{freq=TRUE} (the default) puts count on the y-axis. Setting \code{freq=FALSE} puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the \code{\link[=yaxis_text]{yaxis_text()}} convenience function.)} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{size, alpha}{For the 2D plot only, arguments passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the appearance of scatterplot points.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ The distribution of a (test) statistic \code{T(yrep)}, or a pair of (test) statistics, over the simulated datasets in \code{yrep}, compared to the observed value \code{T(y)} computed from the data \code{y}. See the \strong{Plot Descriptions} and \strong{Details} sections, below, as well as \href{https://github.com/jgabry/bayes-vis-paper#readme}{Gabry et al. (2019)}. } \details{ For Binomial data, the plots may be more useful if the input contains the "success" \emph{proportions} (not discrete "success" or "failure" counts). } \section{Plot Descriptions}{ \describe{ \item{\code{ppc_stat()}, \code{ppc_stat_freqpoly()}}{ A histogram or frequency polygon of the distribution of a statistic computed by applying \code{stat} to each dataset (row) in \code{yrep}. The value of the statistic in the observed data, \code{stat(y)}, is overlaid as a vertical line. More details and example usage of \code{ppc_stat()} can be found in Gabry et al. (2019). } \item{\code{ppc_stat_grouped()},\code{ppc_stat_freqpoly_grouped()}}{ The same as \code{ppc_stat()} and \code{ppc_stat_freqpoly()}, but a separate plot is generated for each level of a grouping variable. More details and example usage of \code{ppc_stat_grouped()} can be found in Gabry et al. (2019). } \item{\code{ppc_stat_2d()}}{ A scatterplot showing the joint distribution of two statistics computed over the datasets (rows) in \code{yrep}. The value of the statistics in the observed data is overlaid as large point. } } } \examples{ y <- example_y_data() yrep <- example_yrep_draws() ppc_stat(y, yrep) ppc_stat(y, yrep, stat = "sd") + legend_none() # use your own function for the 'stat' argument color_scheme_set("brightblue") q25 <- function(y) quantile(y, 0.25) ppc_stat(y, yrep, stat = "q25") # legend includes function name # can define the function in the 'stat' argument instead of # using its name but then the legend doesn't include the function name ppc_stat(y, yrep, stat = function(y) quantile(y, 0.25)) # plots by group color_scheme_set("teal") group <- example_group_data() ppc_stat_grouped(y, yrep, group) ppc_stat_grouped(y, yrep, group) + yaxis_text() # force y-axes to have same scales, allow x axis to vary ppc_stat_grouped(y, yrep, group, facet_args = list(scales = "free_x")) + yaxis_text() # the freqpoly plots use frequency polygons instead of histograms ppc_stat_freqpoly(y, yrep, stat = "median") ppc_stat_freqpoly_grouped(y, yrep, group, stat = "median", facet_args = list(nrow = 2)) # ppc_stat_2d allows 2 statistics and makes a scatterplot bayesplot_theme_set(ggplot2::theme_linedraw()) color_scheme_set("viridisE") ppc_stat_2d(y, yrep, stat = c("mean", "sd")) bayesplot_theme_set(ggplot2::theme_grey()) color_scheme_set("brewer-Paired") ppc_stat_2d(y, yrep, stat = c("median", "mad")) # reset aesthetics color_scheme_set() bayesplot_theme_set() } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}} } \concept{PPCs} bayesplot/man/MCMC-diagnostics.Rd0000644000176200001440000001674214556270424016365 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-diagnostics.R \name{MCMC-diagnostics} \alias{MCMC-diagnostics} \alias{mcmc_rhat} \alias{mcmc_rhat_hist} \alias{mcmc_rhat_data} \alias{mcmc_neff} \alias{mcmc_neff_hist} \alias{mcmc_neff_data} \alias{mcmc_acf} \alias{mcmc_acf_bar} \title{General MCMC diagnostics} \usage{ mcmc_rhat(rhat, ..., size = NULL) mcmc_rhat_hist(rhat, ..., binwidth = NULL, bins = NULL, breaks = NULL) mcmc_rhat_data(rhat, ...) mcmc_neff(ratio, ..., size = NULL) mcmc_neff_hist(ratio, ..., binwidth = NULL, bins = NULL, breaks = NULL) mcmc_neff_data(ratio, ...) mcmc_acf( x, pars = character(), regex_pars = character(), ..., facet_args = list(), lags = 20, size = NULL ) mcmc_acf_bar( x, pars = character(), regex_pars = character(), ..., facet_args = list(), lags = 20 ) } \arguments{ \item{rhat}{A vector of R-hat estimates.} \item{...}{Currently ignored.} \item{size}{Optional values to override \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}}'s default size (for \code{mcmc_rhat()}, \code{mcmc_neff()}) or \code{\link[ggplot2:geom_path]{ggplot2::geom_line()}}'s default line width (for \code{mcmc_acf()}).} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{ratio}{A vector of \emph{ratios} of effective sample size estimates to total sample size. See \code{\link[=neff_ratio]{neff_ratio()}}.} \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{pars}{An optional character vector of parameter names. If neither \code{pars} nor \code{regex_pars} is specified then the default is to use \emph{all} parameters. As of version \verb{1.7.0}, \strong{bayesplot} also supports 'tidy' parameter selection by specifying \code{pars = vars(...)}, where \code{...} is specified the same way as in \link[dplyr:select]{dplyr::select(...)} and similar functions. Examples of using \code{pars} in this way can be found on the \link[=tidy-params]{Tidy parameter selection} page.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{lags}{The number of lags to show in the autocorrelation plot.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Plots of Rhat statistics, ratios of effective sample size to total sample size, and autocorrelation of MCMC draws. See the \strong{Plot Descriptions} section, below, for details. For models fit using the No-U-Turn-Sampler, see also \link{MCMC-nuts} for additional MCMC diagnostic plots. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_rhat()}, \code{mcmc_rhat_hist()}}{ Rhat values as either points or a histogram. Values are colored using different shades (lighter is better). The chosen thresholds are somewhat arbitrary, but can be useful guidelines in practice. \itemize{ \item \emph{light}: below 1.05 (good) \item \emph{mid}: between 1.05 and 1.1 (ok) \item \emph{dark}: above 1.1 (too high) } } \item{\code{mcmc_neff()}, \code{mcmc_neff_hist()}}{ Ratios of effective sample size to total sample size as either points or a histogram. Values are colored using different shades (lighter is better). The chosen thresholds are somewhat arbitrary, but can be useful guidelines in practice. \itemize{ \item \emph{light}: between 0.5 and 1 (high) \item \emph{mid}: between 0.1 and 0.5 (good) \item \emph{dark}: below 0.1 (low) } } \item{\code{mcmc_acf()}, \code{mcmc_acf_bar()}}{ Grid of autocorrelation plots by chain and parameter. The \code{lags} argument gives the maximum number of lags at which to calculate the autocorrelation function. \code{mcmc_acf()} is a line plot whereas \code{mcmc_acf_bar()} is a barplot. } } } \examples{ # autocorrelation x <- example_mcmc_draws() dim(x) dimnames(x) color_scheme_set("green") mcmc_acf(x, pars = c("alpha", "beta[1]")) \donttest{ color_scheme_set("pink") (p <- mcmc_acf_bar(x, pars = c("alpha", "beta[1]"))) # add horiztonal dashed line at 0.5 p + hline_at(0.5, linetype = 2, size = 0.15, color = "gray") } # fake rhat values to use for demonstration rhat <- c(runif(100, 1, 1.15)) mcmc_rhat_hist(rhat) mcmc_rhat(rhat) # lollipops color_scheme_set("purple") mcmc_rhat(rhat[1:10], size = 5) color_scheme_set("blue") mcmc_rhat(runif(1000, 1, 1.07)) mcmc_rhat(runif(1000, 1, 1.3)) + legend_move("top") # add legend above plot # fake neff ratio values to use for demonstration ratio <- c(runif(100, 0, 1)) mcmc_neff_hist(ratio) mcmc_neff(ratio) \dontrun{ # Example using rstanarm model (requires rstanarm package) library(rstanarm) # intentionally use small 'iter' so there are some # problems with rhat and neff for demonstration fit <- stan_glm(mpg ~ ., data = mtcars, iter = 50, refresh = 0) rhats <- rhat(fit) ratios <- neff_ratio(fit) mcmc_rhat(rhats) mcmc_neff(ratios, size = 3) # there's a small enough number of parameters in the # model that we can display their names on the y-axis mcmc_neff(ratios) + yaxis_text(hjust = 1) # can also look at autocorrelation draws <- as.array(fit) mcmc_acf(draws, pars = c("wt", "cyl"), lags = 10) # increase number of iterations and plots look much better fit2 <- update(fit, iter = 500) mcmc_rhat(rhat(fit2)) mcmc_neff(neff_ratio(fit2)) mcmc_acf(as.array(fit2), pars = c("wt", "cyl"), lags = 10) } } \references{ Stan Development Team. \emph{Stan Modeling Language Users Guide and Reference Manual.} \url{https://mc-stan.org/users/documentation/} Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. \emph{Statistical Science}. 7(4), 457--472. } \seealso{ \itemize{ \item The \href{https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html}{Visual MCMC Diagnostics} vignette. \item \link{MCMC-nuts} for additional MCMC diagnostic plots for models fit using the No-U-Turn-Sampler. } Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/pp_check.Rd0000644000176200001440000000551014127610044015072 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pp_check.R \name{pp_check} \alias{pp_check} \alias{pp_check.default} \title{Posterior (or prior) predictive checks (S3 generic and default method)} \usage{ pp_check(object, ...) \method{pp_check}{default}(object, yrep, fun, ...) } \arguments{ \item{object}{Typically a fitted model object. The default method, however, takes \code{object} to be a \code{y} (outcome) vector.} \item{...}{For the generic, arguments passed to individual methods. For the default method, these are additional arguments to pass to \code{fun}.} \item{yrep}{For the default method, a \code{yrep} matrix passed to \code{fun}.} \item{fun}{For the default method, the plotting function to call. Can be any of the \link{PPC} functions. The \code{"ppc_"} prefix can optionally be dropped if \code{fun} is specified as a string.} } \value{ The exact form of the value returned by \code{pp_check()} may vary by the class of \code{object}, but for consistency we encourage authors of methods to return the ggplot object created by one of \strong{bayesplot}'s plotting functions. The default method returns the object returned by \code{fun}. } \description{ S3 generic with simple default method. The intent is to provide a generic so authors of other \R packages who wish to provide interfaces to the functions in \strong{bayesplot} will be encouraged to include \code{pp_check()} methods in their package, preserving the same naming conventions for posterior (and prior) predictive checking across many \R packages for Bayesian inference. This is for the convenience of both users and developers. See the \strong{Details} and \strong{Examples} sections, below, and the package vignettes for examples of defining \code{pp_check()} methods. } \details{ A package that creates fitted model objects of class \code{"foo"} can include a method \code{pp_check.foo()} that prepares the appropriate inputs (\code{y}, \code{yrep}, etc.) for the \strong{bayesplot} functions. The \code{pp_check.foo()} method may, for example, let the user choose between various plots, calling the functions from \strong{bayesplot} internally as needed. See \strong{Examples}, below, and the package vignettes. } \examples{ # default method y <- example_y_data() yrep <- example_yrep_draws() pp_check(y, yrep[1:50,], ppc_dens_overlay) g <- example_group_data() pp_check(y, yrep, fun = "stat_grouped", group = g, stat = "median") # defining a method x <- list(y = rnorm(50), yrep = matrix(rnorm(5000), nrow = 100, ncol = 50)) class(x) <- "foo" pp_check.foo <- function(object, ..., type = c("multiple", "overlaid")) { y <- object[["y"]] yrep <- object[["yrep"]] switch(match.arg(type), multiple = ppc_hist(y, yrep[1:min(8, nrow(yrep)),, drop = FALSE]), overlaid = ppc_dens_overlay(y, yrep)) } pp_check(x) pp_check(x, type = "overlaid") } bayesplot/man/MCMC-nuts.Rd0000644000176200001440000001552414556270424015044 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-diagnostics-nuts.R \name{MCMC-nuts} \alias{MCMC-nuts} \alias{NUTS} \alias{mcmc_nuts_acceptance} \alias{mcmc_nuts_divergence} \alias{mcmc_nuts_stepsize} \alias{mcmc_nuts_treedepth} \alias{mcmc_nuts_energy} \title{Diagnostic plots for the No-U-Turn-Sampler (NUTS)} \usage{ mcmc_nuts_acceptance( x, lp, chain = NULL, ..., binwidth = NULL, bins = NULL, breaks = NULL ) mcmc_nuts_divergence(x, lp, chain = NULL, ...) mcmc_nuts_stepsize(x, lp, chain = NULL, ...) mcmc_nuts_treedepth(x, lp, chain = NULL, ...) mcmc_nuts_energy( x, ..., binwidth = NULL, bins = NULL, breaks = NULL, alpha = 0.5, merge_chains = FALSE ) } \arguments{ \item{x}{A molten data frame of NUTS sampler parameters, either created by \code{\link[=nuts_params]{nuts_params()}} or in the same form as the object returned by \code{\link[=nuts_params]{nuts_params()}}.} \item{lp}{A molten data frame of draws of the log-posterior or, more commonly, of a quantity equal to the log-posterior up to a constant. \code{lp} should either be created via \code{\link[=log_posterior]{log_posterior()}} or be an object with the same form as the object returned by \code{\link[=log_posterior]{log_posterior()}}.} \item{chain}{A positive integer for selecting a particular chain. The default (\code{NULL}) is to merge the chains before plotting. If \code{chain = k} then the plot for chain \code{k} is overlaid (in a darker shade but with transparency) on top of the plot for all chains. The \code{chain} argument is not used by \code{mcmc_nuts_energy()}.} \item{...}{Currently ignored.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{alpha}{For \code{mcmc_nuts_energy()} only, the transparency (alpha) level in \verb{[0,1]} used for the overlaid histogram.} \item{merge_chains}{For \code{mcmc_nuts_energy()} only, should all chains be merged or displayed separately? The default is \code{FALSE}, i.e., to show the chains separately.} } \value{ A gtable object (the result of calling \code{\link[gridExtra:arrangeGrob]{gridExtra::arrangeGrob()}}) created from several ggplot objects, except for \code{mcmc_nuts_energy()}, which returns a ggplot object. } \description{ Diagnostic plots for the No-U-Turn-Sampler (NUTS), the default MCMC algorithm used by \href{https://mc-stan.org}{Stan}. See the \strong{Plot Descriptions} section, below. } \section{Quick Definitions}{ For more details see Stan Development Team (2016) and Betancourt (2017). \itemize{ \item \code{accept_stat__}: the average acceptance probabilities of all possible samples in the proposed tree. \item \code{divergent__}: the number of leapfrog transitions with diverging error. Because NUTS terminates at the first divergence this will be either 0 or 1 for each iteration. \item \code{stepsize__}: the step size used by NUTS in its Hamiltonian simulation. \item \code{treedepth__}: the depth of tree used by NUTS, which is the log (base 2) of the number of leapfrog steps taken during the Hamiltonian simulation. \item \code{energy__}: the value of the Hamiltonian (up to an additive constant) at each iteration. } } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_nuts_acceptance()}}{ Three plots: \itemize{ \item Histogram of \code{accept_stat__} with vertical lines indicating the mean (solid line) and median (dashed line). \item Histogram of \code{lp__} with vertical lines indicating the mean (solid line) and median (dashed line). \item Scatterplot of \code{accept_stat__} vs \code{lp__}. } } \item{\code{mcmc_nuts_divergence()}}{ Two plots: \itemize{ \item Violin plots of \code{lp__|divergent__=1} and \code{lp__|divergent__=0}. \item Violin plots of \code{accept_stat__|divergent__=1} and \code{accept_stat__|divergent__=0}. } } \item{\code{mcmc_nuts_stepsize()}}{ Two plots: \itemize{ \item Violin plots of \code{lp__} by chain ordered by \code{stepsize__} value. \item Violin plots of \code{accept_stat__} by chain ordered by \code{stepsize__} value. } } \item{\code{mcmc_nuts_treedepth()}}{ Three plots: \itemize{ \item Violin plots of \code{lp__} by value of \code{treedepth__}. \item Violin plots of \code{accept_stat__} by value of \code{treedepth__}. \item Histogram of \code{treedepth__}. } } \item{\code{mcmc_nuts_energy()}}{ Overlaid histograms showing \code{energy__} vs the change in \code{energy__}. See Betancourt (2016) for details. } } } \examples{ \dontrun{ library(ggplot2) library(rstanarm) fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000, refresh = 0) np <- nuts_params(fit) lp <- log_posterior(fit) color_scheme_set("brightblue") mcmc_nuts_acceptance(np, lp) mcmc_nuts_acceptance(np, lp, chain = 2) mcmc_nuts_divergence(np, lp) mcmc_nuts_stepsize(np, lp) mcmc_nuts_treedepth(np, lp) color_scheme_set("red") mcmc_nuts_energy(np) mcmc_nuts_energy(np, merge_chains = TRUE, binwidth = .15) mcmc_nuts_energy(np) + facet_wrap(vars(Chain), nrow = 1) + coord_fixed(ratio = 150) + ggtitle("NUTS Energy Diagnostic") } } \references{ Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. \url{https://arxiv.org/abs/1701.02434} Betancourt, M. and Girolami, M. (2013). Hamiltonian Monte Carlo for hierarchical models. \url{https://arxiv.org/abs/1312.0906} Hoffman, M. D. and Gelman, A. (2014). The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. \emph{Journal of Machine Learning Research}. 15:1593--1623. Stan Development Team. \emph{Stan Modeling Language Users Guide and Reference Manual.} \url{https://mc-stan.org/users/documentation/} } \seealso{ \itemize{ \item The \href{https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html}{Visual MCMC Diagnostics} vignette. \item Several other plotting functions are not NUTS-specific but take optional extra arguments if the model was fit using NUTS: \itemize{ \item \code{\link[=mcmc_trace]{mcmc_trace()}}: show divergences as tick marks below the trace plot. \item \code{\link[=mcmc_parcoord]{mcmc_parcoord()}}: change the color/size/transparency of lines corresponding to divergences. \item \code{\link[=mcmc_scatter]{mcmc_scatter()}}: change the color/size/shape of points corresponding to divergences. \item \code{\link[=mcmc_pairs]{mcmc_pairs()}}: change the color/size/shape of points corresponding divergences and/or max treedepth saturation. } } Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/PPC-intervals.Rd0000644000176200001440000001712314335264324015757 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-intervals.R \name{PPC-intervals} \alias{PPC-intervals} \alias{ppc_intervals} \alias{ppc_intervals_grouped} \alias{ppc_ribbon} \alias{ppc_ribbon_grouped} \alias{ppc_intervals_data} \alias{ppc_ribbon_data} \title{PPC intervals} \usage{ ppc_intervals( y, yrep, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1 ) ppc_intervals_grouped( y, yrep, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1 ) ppc_ribbon( y, yrep, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25, y_draw = c("line", "points", "both") ) ppc_ribbon_grouped( y, yrep, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25, y_draw = c("line", "points", "both") ) ppc_intervals_data( y, yrep, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9 ) ppc_ribbon_data( y, yrep, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9 ) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{x}{A numeric vector to use as the x-axis variable. For example, \code{x} could be a predictor variable from a regression model, a time variable for time-series models, etc. If \code{x} is missing or \code{NULL} then the observation index is used for the x-axis.} \item{...}{Currently unused.} \item{prob, prob_outer}{Values between \code{0} and \code{1} indicating the desired probability mass to include in the inner and outer intervals. The defaults are \code{prob=0.5} and \code{prob_outer=0.9}.} \item{alpha, size, fatten, linewidth}{Arguments passed to geoms. For ribbon plots \code{alpha} is passed to \code{\link[ggplot2:geom_ribbon]{ggplot2::geom_ribbon()}} to control the opacity of the outer ribbon and \code{size} is passed to \code{\link[ggplot2:geom_path]{ggplot2::geom_line()}} to control the size of the line representing the median prediction (\code{size=0} will remove the line). For interval plots \code{alpha}, \code{size}, \code{fatten}, and \code{linewidth} are passed to \code{\link[ggplot2:geom_linerange]{ggplot2::geom_pointrange()}} (\code{fatten=0} will remove the point estimates).} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{y_draw}{For ribbon plots only, a string specifying how to draw \code{y}. Can be \code{"line"} (the default), \code{"points"}, or \code{"both"}.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Medians and central interval estimates of \code{yrep} with \code{y} overlaid. See the \strong{Plot Descriptions} section, below. } \section{Plot Descriptions}{ \describe{ \item{\verb{ppc_intervals(), ppc_ribbon()}}{ \code{100*prob}\% central intervals for \code{yrep} at each \code{x} value. \code{ppc_intervals()} plots intervals as vertical bars with points indicating \code{yrep} medians and darker points indicating observed \code{y} values. \code{ppc_ribbon()} plots a ribbon of connected intervals with a line through the median of \code{yrep} and a darker line connecting observed \code{y} values. In both cases an optional \code{x} variable can also be specified for the x-axis variable. Depending on the number of observations and the variability in the predictions at different values of \code{x}, one of these plots may be easier to read than the other. } \item{\verb{ppc_intervals_grouped(), ppc_ribbon_grouped()}}{ Same as \code{ppc_intervals()} and \code{ppc_ribbon()}, respectively, but a separate plot (facet) is generated for each level of a grouping variable. } } } \examples{ y <- rnorm(50) yrep <- matrix(rnorm(5000, 0, 2), ncol = 50) color_scheme_set("brightblue") ppc_intervals(y, yrep) ppc_ribbon(y, yrep) ppc_ribbon(y, yrep, y_draw = "points") \dontrun{ ppc_ribbon(y, yrep, y_draw = "both") } ppc_intervals(y, yrep, size = 1.5, fatten = 0) # remove the yrep point estimates color_scheme_set("teal") year <- 1950:1999 ppc_intervals(y, yrep, x = year, fatten = 1) + ggplot2::xlab("Year") ppc_ribbon(y, yrep, x = year) + ggplot2::xlab("Year") color_scheme_set("pink") year <- rep(2000:2009, each = 5) group <- gl(5, 1, length = 50, labels = LETTERS[1:5]) ppc_ribbon_grouped(y, yrep, x = year, group, y_draw = "both") + ggplot2::scale_x_continuous(breaks = pretty) ppc_ribbon_grouped(y, yrep, x = year, group, facet_args = list(scales = "fixed")) + xaxis_text(FALSE) + xaxis_ticks(FALSE) + panel_bg(fill = "gray20") # get the data frames used to make the ggplots ppc_dat <- ppc_intervals_data(y, yrep, x = year, prob = 0.5) ppc_group_dat <- ppc_intervals_data(y, yrep, x = year, group = group, prob = 0.5) \dontrun{ library("rstanarm") fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars, refresh = 0) yrep <- posterior_predict(fit) color_scheme_set("purple") ppc_intervals(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, prob = 0.8) + panel_bg(fill="gray90", color = NA) + grid_lines(color = "white") ppc_ribbon(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, prob = 0.6, prob_outer = 0.8) ppc_ribbon_grouped(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, group = mtcars$cyl) color_scheme_set("gray") ppc_intervals(mtcars$mpg, yrep, prob = 0.5) + ggplot2::scale_x_continuous( labels = rownames(mtcars), breaks = 1:nrow(mtcars) ) + xaxis_text(angle = -70, vjust = 1, hjust = 0) + xaxis_title(FALSE) } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/bayesplot-package.Rd0000644000176200001440000001107414556270424016725 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot-package.R \docType{package} \name{bayesplot-package} \alias{bayesplot-package} \alias{bayesplot} \title{\strong{bayesplot}: Plotting for Bayesian Models} \description{ \if{html}{ \figure{stanlogo.png}{options: width="50" alt="mc-stan.org"} } \emph{Stan Development Team} The \strong{bayesplot} package provides a variety of \strong{ggplot2}-based plotting functions for use after fitting Bayesian models (typically, though not exclusively, via Markov chain Monte Carlo). The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of packages for Bayesian modeling, particularly (but not necessarily) packages powered by RStan (the \R interface to Stan). Examples of packages that will soon (or already are) using \strong{bayesplot} are \strong{rstan} itself, as well as the \strong{rstan}-dependent \strong{rstanarm} and \strong{brms} packages for applied regression modeling. } \section{Plotting functionality}{ \if{html}{ \figure{bayesplot1.png}{options: width="30\%" alt="mcmc_areas"} \figure{bayesplot2.png}{options: width="30\%" alt="ppc_hist"} \figure{bayesplot3.png}{options: width="30\%" alt="ppc_dens_overlay"} } The plotting functions in \strong{bayesplot} are organized into several modules: \itemize{ \item \link[=MCMC-overview]{MCMC}: Visualizations of Markov chain Monte Carlo (MCMC) simulations generated by \emph{any} MCMC algorithm as well as diagnostics. There are also additional functions specifically for use with models fit using the \link[=NUTS]{No-U-Turn Sampler (NUTS)}. \item \link[=PPC-overview]{PPC}: Graphical (posterior or prior) predictive checks (PPCs). \item \link[=PPD-overview]{PPD}: Plots of (posterior or prior) predictive distributions without comparisons to observed data. } } \section{Resources}{ \itemize{ \item \strong{Online documentation and vignettes}: Visit the \strong{bayesplot} website at \url{https://mc-stan.org/bayesplot/} \item \strong{Bug reports and feature requests}: If you would like to request a new feature or if you have noticed a bug that needs to be fixed please let us know at the \strong{bayesplot} issue tracker at \url{https://github.com/stan-dev/bayesplot/issues/} \item \strong{General questions and help}: To ask a question about \strong{bayesplot} on the Stan Forums forum please visit \url{https://discourse.mc-stan.org}. } } \examples{ # A few quick examples (all of the functions have many examples # on their individual help pages) # MCMC plots x <- example_mcmc_draws(params = 5) mcmc_intervals(x, prob = 0.5) mcmc_intervals(x, regex_pars = "beta") color_scheme_set("purple") mcmc_areas(x, regex_pars = "beta", prob = 0.8) color_scheme_set("mix-blue-red") mcmc_trace(x, pars = c("alpha", "sigma"), facet_args = list(nrow = 2)) color_scheme_set("brightblue") mcmc_scatter(x, pars = c("beta[1]", "sigma"), transformations = list(sigma = "log")) # Graphical PPCs y <- example_y_data() yrep <- example_yrep_draws() ppc_dens_overlay(y, yrep[1:50, ]) \donttest{ color_scheme_set("pink") ppc_stat(y, yrep, stat = "median") + grid_lines() ppc_hist(y, yrep[1:8, ]) # Same plots but without y (using ppd_ instead of ppc_) bayesplot_theme_set(ggplot2::theme_gray()) ypred <- yrep ppd_dens_overlay(ypred[1:50, ]) ppd_stat(ypred, stat = "median") + grid_lines() ppd_hist(ypred[1:8, ]) } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) } \seealso{ \code{\link[=theme_default]{theme_default()}} for the default ggplot theme used by \strong{bayesplot} and \code{\link[=bayesplot_theme_set]{bayesplot_theme_set()}} to change it. \link{bayesplot-colors} to set or view the color scheme used for plotting. \code{\link[ggplot2:ggsave]{ggplot2::ggsave()}} for saving plots. } \author{ \strong{Maintainer}: Jonah Gabry \email{jsg2201@columbia.edu} Authors: \itemize{ \item Tristan Mahr } Other contributors: \itemize{ \item Paul-Christian Bürkner [contributor] \item Martin Modrák [contributor] \item Malcolm Barrett [contributor] \item Frank Weber [contributor] \item Eduardo Coronado Sroka [contributor] \item Teemu Sailynoja [contributor] \item Aki Vehtari [contributor] } } bayesplot/man/MCMC-distributions.Rd0000644000176200001440000002224414556270424016752 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-distributions.R \name{MCMC-distributions} \alias{MCMC-distributions} \alias{mcmc_hist} \alias{mcmc_dens} \alias{mcmc_hist_by_chain} \alias{mcmc_dens_overlay} \alias{mcmc_dens_chains} \alias{mcmc_dens_chains_data} \alias{mcmc_violin} \title{Histograms and kernel density plots of MCMC draws} \usage{ mcmc_hist( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE, alpha = 1 ) mcmc_dens( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), trim = FALSE, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL, alpha = 1 ) mcmc_hist_by_chain( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE, alpha = 1 ) mcmc_dens_overlay( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), color_chains = TRUE, trim = FALSE, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) mcmc_dens_chains( x, pars = character(), regex_pars = character(), transformations = list(), ..., color_chains = TRUE, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) mcmc_dens_chains_data( x, pars = character(), regex_pars = character(), transformations = list(), ..., bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) mcmc_violin( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), probs = c(0.1, 0.5, 0.9) ) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{pars}{An optional character vector of parameter names. If neither \code{pars} nor \code{regex_pars} is specified then the default is to use \emph{all} parameters. As of version \verb{1.7.0}, \strong{bayesplot} also supports 'tidy' parameter selection by specifying \code{pars = vars(...)}, where \code{...} is specified the same way as in \link[dplyr:select]{dplyr::select(...)} and similar functions. Examples of using \code{pars} in this way can be found on the \link[=tidy-params]{Tidy parameter selection} page.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} \item{transformations}{Optionally, transformations to apply to parameters before plotting. If \code{transformations} is a function or a single string naming a function then that function will be used to transform all parameters. To apply transformations to particular parameters, the \code{transformations} argument can be a named list with length equal to the number of parameters to be transformed. Currently only univariate transformations of scalar parameters can be specified (multivariate transformations will be implemented in a future release). If \code{transformations} is a list, the name of each list element should be a parameter name and the content of each list element should be a function (or any item to match as a function via \code{\link[=match.fun]{match.fun()}}, e.g. a string naming a function). If a function is specified by its name as a string (e.g. \code{"log"}), then it can be used to construct a new parameter label for the appropriate parameter (e.g. \code{"log(sigma)"}). If a function itself is specified (e.g. \code{log} or \code{function(x) log(x)}) then \code{"t"} is used in the new parameter label to indicate that the parameter is transformed (e.g. \code{"t(sigma)"}). Note: due to partial argument matching \code{transformations} can be abbreviated for convenience in interactive use (e.g., \code{transform}).} \item{...}{Currently ignored.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{freq}{For histograms, \code{freq=TRUE} (the default) puts count on the y-axis. Setting \code{freq=FALSE} puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the \code{\link[=yaxis_text]{yaxis_text()}} convenience function.)} \item{alpha}{Passed to the geom to control the transparency.} \item{trim}{A logical scalar passed to \code{\link[ggplot2:geom_density]{ggplot2::geom_density()}}.} \item{bw, adjust, kernel, n_dens}{Optional arguments passed to \code{\link[stats:density]{stats::density()}} to override default kernel density estimation parameters. \code{n_dens} defaults to \code{1024}.} \item{color_chains}{Option for whether to separately color chains.} \item{probs}{A numeric vector passed to \code{\link[ggplot2:geom_violin]{ggplot2::geom_violin()}}'s \code{draw_quantiles} argument to specify at which quantiles to draw horizontal lines. Set to \code{NULL} to remove the lines.} } \value{ A ggplot object that can be further customized using the \strong{ggplot2} package. } \description{ Various types of histograms and kernel density plots of MCMC draws. See the \strong{Plot Descriptions} section, below, for details. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_hist()}}{ Histograms of posterior draws with all chains merged. } \item{\code{mcmc_dens()}}{ Kernel density plots of posterior draws with all chains merged. } \item{\code{mcmc_hist_by_chain()}}{ Histograms of posterior draws with chains separated via faceting. } \item{\code{mcmc_dens_overlay()}}{ Kernel density plots of posterior draws with chains separated but overlaid on a single plot. } \item{\code{mcmc_violin()}}{ The density estimate of each chain is plotted as a violin with horizontal lines at notable quantiles. } \item{\code{mcmc_dens_chains()}}{ Ridgeline kernel density plots of posterior draws with chains separated but overlaid on a single plot. In \code{mcmc_dens_overlay()} parameters appear in separate facets; in \code{mcmc_dens_chains()} they appear in the same panel and can overlap vertically. } } } \examples{ set.seed(9262017) # some parameter draws to use for demonstration x <- example_mcmc_draws() dim(x) dimnames(x) ################## ### Histograms ### ################## # histograms of all parameters color_scheme_set("brightblue") mcmc_hist(x) # histograms of some parameters color_scheme_set("pink") mcmc_hist(x, pars = c("alpha", "beta[2]")) \donttest{ mcmc_hist(x, pars = "sigma", regex_pars = "beta") } # example of using 'transformations' argument to plot log(sigma), # and parsing facet labels (e.g. to get greek letters for parameters) mcmc_hist(x, transformations = list(sigma = "log"), facet_args = list(labeller = ggplot2::label_parsed)) + facet_text(size = 15) \donttest{ # instead of list(sigma = "log"), you could specify the transformation as # list(sigma = log) or list(sigma = function(x) log(x)), but then the # label for the transformed sigma is 't(sigma)' instead of 'log(sigma)' mcmc_hist(x, transformations = list(sigma = log)) # separate histograms by chain color_scheme_set("pink") mcmc_hist_by_chain(x, regex_pars = "beta") } ################# ### Densities ### ################# mcmc_dens(x, pars = c("sigma", "beta[2]"), facet_args = list(nrow = 2)) \donttest{ # separate and overlay chains color_scheme_set("mix-teal-pink") mcmc_dens_overlay(x, pars = c("sigma", "beta[2]"), facet_args = list(nrow = 2)) + facet_text(size = 14) x2 <- example_mcmc_draws(params = 6) mcmc_dens_chains(x2, pars = c("beta[1]", "beta[2]", "beta[3]")) } # separate chains as violin plots color_scheme_set("green") mcmc_violin(x) + panel_bg(color = "gray20", size = 2, fill = "gray30") } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/PPC-discrete.Rd0000644000176200001440000001554614335264324015561 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-discrete.R \name{PPC-discrete} \alias{PPC-discrete} \alias{ppc_bars} \alias{ppc_bars_grouped} \alias{ppc_rootogram} \alias{ppc_bars_data} \title{PPCs for discrete outcomes} \usage{ ppc_bars( y, yrep, ..., prob = 0.9, width = 0.9, size = 1, fatten = 2.5, linewidth = 1, freq = TRUE ) ppc_bars_grouped( y, yrep, group, ..., facet_args = list(), prob = 0.9, width = 0.9, size = 1, fatten = 2.5, linewidth = 1, freq = TRUE ) ppc_rootogram( y, yrep, style = c("standing", "hanging", "suspended"), ..., prob = 0.9, size = 1 ) ppc_bars_data(y, yrep, group = NULL, prob = 0.9, freq = TRUE) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{...}{Currently unused.} \item{prob}{A value between \code{0} and \code{1} indicating the desired probability mass to include in the \code{yrep} intervals. Set \code{prob=0} to remove the intervals. (Note: for rootograms these are intervals of the \emph{square roots} of the expected counts.)} \item{width}{For bar plots only, passed to \code{\link[ggplot2:geom_bar]{ggplot2::geom_bar()}} to control the bar width.} \item{size, fatten, linewidth}{For bar plots, \code{size}, \code{fatten}, and \code{linewidth} are passed to \code{\link[ggplot2:geom_linerange]{ggplot2::geom_pointrange()}} to control the appearance of the \code{yrep} points and intervals. For rootograms \code{size} is passed to \code{\link[ggplot2:geom_path]{ggplot2::geom_line()}}.} \item{freq}{For bar plots only, if \code{TRUE} (the default) the y-axis will display counts. Setting \code{freq=FALSE} will put proportions on the y-axis.} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{facet_args}{An optional list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} to control faceting.} \item{style}{For \code{ppc_rootogram}, a string specifying the rootogram style. The options are \code{"standing"}, \code{"hanging"}, and \code{"suspended"}. See the \strong{Plot Descriptions} section, below, for details on the different styles.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Many of the \link[=PPC-overview]{PPC} functions in \strong{bayesplot} can be used with discrete data. The small subset of these functions that can \emph{only} be used if \code{y} and \code{yrep} are discrete are documented on this page. Currently these include rootograms for count outcomes and bar plots for ordinal, categorical, and multinomial outcomes. See the \strong{Plot Descriptions} section below. } \details{ For all of these plots \code{y} and \code{yrep} must be integers, although they need not be integers in the strict sense of \R's \link[base:integer]{integer} type. For rootogram plots \code{y} and \code{yrep} must also be non-negative. } \section{Plot Descriptions}{ \describe{ \item{\code{ppc_bars()}}{ Bar plot of \code{y} with \code{yrep} medians and uncertainty intervals superimposed on the bars. } \item{\code{ppc_bars_grouped()}}{ Same as \code{ppc_bars()} but a separate plot (facet) is generated for each level of a grouping variable. } \item{\code{ppc_rootogram()}}{ Rootograms allow for diagnosing problems in count data models such as overdispersion or excess zeros. They consist of a histogram of \code{y} with the expected counts based on \code{yrep} overlaid as a line along with uncertainty intervals. The y-axis represents the square roots of the counts to approximately adjust for scale differences and thus ease comparison between observed and expected counts. Using the \code{style} argument, the histogram style can be adjusted to focus on different aspects of the data: \itemize{ \item \emph{Standing}: basic histogram of observed counts with curve showing expected counts. \item \emph{Hanging}: observed counts counts hanging from the curve representing expected counts. \item \emph{Suspended}: histogram of the differences between expected and observed counts. } \strong{All of the rootograms are plotted on the square root scale}. See Kleiber and Zeileis (2016) for advice on interpreting rootograms and selecting among the different styles. } } } \examples{ set.seed(9222017) # bar plots f <- function(N) { sample(1:4, size = N, replace = TRUE, prob = c(0.25, 0.4, 0.1, 0.25)) } y <- f(100) yrep <- t(replicate(500, f(100))) dim(yrep) group <- gl(2, 50, length = 100, labels = c("GroupA", "GroupB")) color_scheme_set("mix-pink-blue") ppc_bars(y, yrep) # split by group, change interval width, and display proportion # instead of count on y-axis color_scheme_set("mix-blue-pink") ppc_bars_grouped(y, yrep, group, prob = 0.5, freq = FALSE) \dontrun{ # example for ordinal regression using rstanarm library(rstanarm) fit <- stan_polr( tobgp ~ agegp, data = esoph, method = "probit", prior = R2(0.2, "mean"), init_r = 0.1, seed = 12345, # cores = 4, refresh = 0 ) # coded as character, so convert to integer yrep_char <- posterior_predict(fit) print(yrep_char[1, 1:4]) yrep_int <- sapply(data.frame(yrep_char, stringsAsFactors = TRUE), as.integer) y_int <- as.integer(esoph$tobgp) ppc_bars(y_int, yrep_int) ppc_bars_grouped( y = y_int, yrep = yrep_int, group = esoph$agegp, freq=FALSE, prob = 0.5, fatten = 1, size = 1.5 ) } # rootograms for counts y <- rpois(100, 20) yrep <- matrix(rpois(10000, 20), ncol = 100) color_scheme_set("brightblue") ppc_rootogram(y, yrep) ppc_rootogram(y, yrep, prob = 0) ppc_rootogram(y, yrep, style = "hanging", prob = 0.8) ppc_rootogram(y, yrep, style = "suspended") } \references{ Kleiber, C. and Zeileis, A. (2016). Visualizing count data regressions using rootograms. \emph{The American Statistician}. 70(3): 296--303. \url{https://arxiv.org/abs/1605.01311}. } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/PPC-scatterplots.Rd0000644000176200001440000001124314212422623016464 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-scatterplots.R \name{PPC-scatterplots} \alias{PPC-scatterplots} \alias{ppc_scatter} \alias{ppc_scatter_avg} \alias{ppc_scatter_avg_grouped} \alias{ppc_scatter_data} \alias{ppc_scatter_avg_data} \title{PPC scatterplots} \usage{ ppc_scatter( y, yrep, ..., facet_args = list(), size = 2.5, alpha = 0.8, ref_line = TRUE ) ppc_scatter_avg(y, yrep, ..., size = 2.5, alpha = 0.8, ref_line = TRUE) ppc_scatter_avg_grouped( y, yrep, group, ..., facet_args = list(), size = 2.5, alpha = 0.8, ref_line = TRUE ) ppc_scatter_data(y, yrep) ppc_scatter_avg_data(y, yrep, group = NULL) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{...}{Currently unused.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{size, alpha}{Arguments passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the appearance of the points.} \item{ref_line}{If \code{TRUE} (the default) a dashed line with intercept 0 and slope 1 is drawn behind the scatter plot.} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Scatterplots of the observed data \code{y} vs. simulated/replicated data \code{yrep} from the posterior predictive distribution. See the \strong{Plot Descriptions} and \strong{Details} sections, below. } \details{ For Binomial data, the plots may be more useful if the input contains the "success" \emph{proportions} (not discrete "success" or "failure" counts). } \section{Plot Descriptions}{ \describe{ \item{\code{ppc_scatter()}}{ For each dataset (row) in \code{yrep} a scatterplot is generated showing \code{y} against that row of \code{yrep}. For this plot \code{yrep} should only contain a small number of rows. } \item{\code{ppc_scatter_avg()}}{ A single scatterplot of \code{y} against the average values of \code{yrep}, i.e., the points \verb{(x,y) = (mean(yrep[, n]), y[n])}, where each \code{yrep[, n]} is a vector of length equal to the number of posterior draws. Unlike for \code{ppc_scatter()}, for \code{ppc_scatter_avg()} \code{yrep} should contain many draws (rows). } \item{\code{ppc_scatter_avg_grouped()}}{ The same as \code{ppc_scatter_avg()}, but a separate plot is generated for each level of a grouping variable. } } } \examples{ y <- example_y_data() yrep <- example_yrep_draws() p1 <- ppc_scatter_avg(y, yrep) p1 # don't draw line x=y ppc_scatter_avg(y, yrep, ref_line = FALSE) p2 <- ppc_scatter(y, yrep[20:23, ], alpha = 0.5, size = 1.5) p2 # give x and y axes the same limits lims <- ggplot2::lims(x = c(0, 160), y = c(0, 160)) p1 + lims p2 + lims # for ppc_scatter_avg_grouped the default is to allow the facets # to have different x and y axes group <- example_group_data() ppc_scatter_avg_grouped(y, yrep, group) # let x-axis vary but force y-axis to be the same ppc_scatter_avg_grouped(y, yrep, group, facet_args = list(scales = "free_x")) } \references{ Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/bayesplot-extractors.Rd0000644000176200001440000000710614127610043017516 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot-extractors.R \name{bayesplot-extractors} \alias{bayesplot-extractors} \alias{log_posterior} \alias{nuts_params} \alias{rhat} \alias{neff_ratio} \alias{log_posterior.stanfit} \alias{log_posterior.stanreg} \alias{log_posterior.CmdStanMCMC} \alias{nuts_params.stanfit} \alias{nuts_params.stanreg} \alias{nuts_params.list} \alias{nuts_params.CmdStanMCMC} \alias{rhat.stanfit} \alias{rhat.stanreg} \alias{rhat.CmdStanMCMC} \alias{neff_ratio.stanfit} \alias{neff_ratio.stanreg} \alias{neff_ratio.CmdStanMCMC} \title{Extract quantities needed for plotting from model objects} \usage{ log_posterior(object, ...) nuts_params(object, ...) rhat(object, ...) neff_ratio(object, ...) \method{log_posterior}{stanfit}(object, inc_warmup = FALSE, ...) \method{log_posterior}{stanreg}(object, inc_warmup = FALSE, ...) \method{log_posterior}{CmdStanMCMC}(object, inc_warmup = FALSE, ...) \method{nuts_params}{stanfit}(object, pars = NULL, inc_warmup = FALSE, ...) \method{nuts_params}{stanreg}(object, pars = NULL, inc_warmup = FALSE, ...) \method{nuts_params}{list}(object, pars = NULL, ...) \method{nuts_params}{CmdStanMCMC}(object, pars = NULL, ...) \method{rhat}{stanfit}(object, pars = NULL, ...) \method{rhat}{stanreg}(object, pars = NULL, regex_pars = NULL, ...) \method{rhat}{CmdStanMCMC}(object, pars = NULL, ...) \method{neff_ratio}{stanfit}(object, pars = NULL, ...) \method{neff_ratio}{stanreg}(object, pars = NULL, regex_pars = NULL, ...) \method{neff_ratio}{CmdStanMCMC}(object, pars = NULL, ...) } \arguments{ \item{object}{The object to use.} \item{...}{Arguments passed to individual methods.} \item{inc_warmup}{A logical scalar (defaulting to \code{FALSE}) indicating whether to include warmup draws, if applicable.} \item{pars}{An optional character vector of parameter names. For \code{nuts_params()} these will be NUTS sampler parameter names rather than model parameters. If \code{pars} is omitted all parameters are included.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} } \value{ \describe{ \item{\code{log_posterior()}}{ \code{log_posterior()} methods return a molten data frame (see \code{\link[reshape2:melt]{reshape2::melt()}}). The data frame should have columns \code{"Iteration"} (integer), \code{"Chain"} (integer), and \code{"Value"} (numeric). See \strong{Examples}, below. } \item{\code{nuts_params()}}{ \code{nuts_params()} methods return a molten data frame (see \code{\link[reshape2:melt]{reshape2::melt()}}). The data frame should have columns \code{"Parameter"} (factor), \code{"Iteration"} (integer), \code{"Chain"} (integer), and \code{"Value"} (numeric). See \strong{Examples}, below. } \item{\code{rhat()}, \code{neff_ratio()}}{ Methods return (named) vectors. } } } \description{ Generics and methods for extracting quantities needed for plotting from various types of model objects. Currently methods are provided for stanfit (\strong{rstan}), CmdStanMCMC (\strong{cmdstanr}), and stanreg (\strong{rstanarm}) objects, but adding new methods should be relatively straightforward. } \examples{ \dontrun{ library(rstanarm) fit <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0) np <- nuts_params(fit) head(np) tail(np) lp <- log_posterior(fit) head(lp) tail(lp) } } \seealso{ \link{MCMC-nuts}, \link{MCMC-diagnostics} } bayesplot/man/PPC-loo.Rd0000644000176200001440000002624014556465326014553 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-loo.R \name{PPC-loo} \alias{PPC-loo} \alias{ppc_loo_pit_overlay} \alias{ppc_loo_pit_data} \alias{ppc_loo_pit_qq} \alias{ppc_loo_pit} \alias{ppc_loo_intervals} \alias{ppc_loo_ribbon} \title{LOO predictive checks} \usage{ ppc_loo_pit_overlay( y, yrep, lw = NULL, ..., psis_object = NULL, pit = NULL, samples = 100, size = 0.25, alpha = 0.7, boundary_correction = TRUE, grid_len = 512, bw = "nrd0", trim = FALSE, adjust = 1, kernel = "gaussian", n_dens = 1024 ) ppc_loo_pit_data( y, yrep, lw = NULL, ..., psis_object = NULL, pit = NULL, samples = 100, bw = "nrd0", boundary_correction = TRUE, grid_len = 512 ) ppc_loo_pit_qq( y, yrep, lw = NULL, ..., psis_object = NULL, pit = NULL, compare = c("uniform", "normal"), size = 2, alpha = 1 ) ppc_loo_pit( y, yrep, lw, pit = NULL, compare = c("uniform", "normal"), ..., size = 2, alpha = 1 ) ppc_loo_intervals( y, yrep, psis_object, ..., subset = NULL, intervals = NULL, prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1, order = c("index", "median") ) ppc_loo_ribbon( y, yrep, psis_object, ..., subset = NULL, intervals = NULL, prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25 ) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{lw}{A matrix of (smoothed) log weights with the same dimensions as \code{yrep}. See \code{\link[loo:psis]{loo::psis()}} and the associated \code{weights()} method as well as the \strong{Examples} section, below. If \code{lw} is not specified then \code{psis_object} can be provided and log weights will be extracted.} \item{...}{Currently unused.} \item{psis_object}{If using \strong{loo} version \verb{2.0.0} or greater, an object returned by the \code{psis()} function (or by the \code{loo()} function with argument \code{save_psis} set to \code{TRUE}).} \item{pit}{For \code{ppc_loo_pit_overlay()} and \code{ppc_loo_pit_qq()}, optionally a vector of precomputed PIT values that can be specified instead of \code{y}, \code{yrep}, and \code{lw} (these are all ignored if \code{pit} is specified). If not specified the PIT values are computed internally before plotting.} \item{samples}{For \code{ppc_loo_pit_overlay()}, the number of data sets (each the same size as \code{y}) to simulate from the standard uniform distribution. The default is 100. The density estimate of each dataset is plotted as a thin line in the plot, with the density estimate of the LOO PITs overlaid as a thicker dark line.} \item{alpha, size, fatten, linewidth}{Arguments passed to code geoms to control plot aesthetics. For \code{ppc_loo_pit_qq()} and \code{ppc_loo_pit_overlay()}, \code{size} and \code{alpha} are passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} and \code{\link[ggplot2:geom_density]{ggplot2::geom_density()}}, respectively. For \code{ppc_loo_intervals()}, \code{size} \code{linewidth} and \code{fatten} are passed to \code{\link[ggplot2:geom_linerange]{ggplot2::geom_pointrange()}}. For \code{ppc_loo_ribbon()}, \code{alpha} and \code{size} are passed to \code{\link[ggplot2:geom_ribbon]{ggplot2::geom_ribbon()}}.} \item{boundary_correction}{For \code{ppc_loo_pit_overlay()}, when set to \code{TRUE} (the default) the function will compute boundary corrected density values via convolution and a Gaussian filter, also known as the reflection method (Boneva et al., 1971). As a result, parameters controlling the standard kernel density estimation such as \code{adjust}, \code{kernel} and \code{n_dens} are ignored. NOTE: The current implementation only works well for continuous observations.} \item{grid_len}{For \code{ppc_loo_pit_overlay()}, when \code{boundary_correction} is set to \code{TRUE} this parameter specifies the number of points used to generate the estimations. This is set to 512 by default.} \item{bw, adjust, kernel, n_dens}{Optional arguments passed to \code{\link[stats:density]{stats::density()}} to override default kernel density estimation parameters. \code{n_dens} defaults to \code{1024}.} \item{trim}{Passed to \code{\link[ggplot2:geom_density]{ggplot2::stat_density()}}.} \item{compare}{For \code{ppc_loo_pit_qq()}, a string that can be either \code{"uniform"} or \code{"normal"}. If \code{"uniform"} (the default) the Q-Q plot compares computed PIT values to the standard uniform distribution. If \code{compare="normal"}, the Q-Q plot compares standard normal quantiles calculated from the PIT values to the theoretical standard normal quantiles.} \item{subset}{For \code{ppc_loo_intervals()} and \code{ppc_loo_ribbon()}, an optional integer vector indicating which observations in \code{y} (and \code{yrep}) to include. Dropping observations from \code{y} and \code{yrep} manually before passing them to the plotting function will not work because the dimensions will not match up with the dimensions of \code{psis_object}, but if all of \code{y} and \code{yrep} are passed along with \code{subset} then \strong{bayesplot} can do the subsetting internally for \code{y}, \code{yrep} \emph{and} \code{psis_object}. See the \strong{Examples} section for a demonstration.} \item{intervals}{For \code{ppc_loo_intervals()} and \code{ppc_loo_ribbon()}, optionally a matrix of pre-computed LOO predictive intervals that can be specified instead of \code{yrep} (ignored if \code{intervals} is specified). If not specified the intervals are computed internally before plotting. If specified, \code{intervals} must be a matrix with number of rows equal to the number of data points and five columns in the following order: lower outer interval, lower inner interval, median (50\%), upper inner interval and upper outer interval (column names are ignored).} \item{prob, prob_outer}{Values between \code{0} and \code{1} indicating the desired probability mass to include in the inner and outer intervals. The defaults are \code{prob=0.5} and \code{prob_outer=0.9}.} \item{order}{For \code{ppc_loo_intervals()}, a string indicating how to arrange the plotted intervals. The default (\code{"index"}) is to plot them in the order of the observations. The alternative (\code{"median"}) arranges them by median value from smallest (left) to largest (right).} } \value{ A ggplot object that can be further customized using the \strong{ggplot2} package. } \description{ Leave-One-Out (LOO) predictive checks. See the \strong{Plot Descriptions} section, below, and \href{https://github.com/jgabry/bayes-vis-paper#readme}{Gabry et al. (2019)} for details. } \section{Plot Descriptions}{ \describe{ \item{\code{ppc_loo_pit_overlay()}, \code{ppc_loo_pit_qq()}}{ The calibration of marginal predictions can be assessed using probability integral transformation (PIT) checks. LOO improves the check by avoiding the double use of data. See the section on marginal predictive checks in Gelman et al. (2013, p. 152--153) and section 5 of Gabry et al. (2019) for an example of using \strong{bayesplot} for these checks. The LOO PIT values are asymptotically uniform (for continuous data) if the model is calibrated. The \code{ppc_loo_pit_overlay()} function creates a plot comparing the density of the LOO PITs (thick line) to the density estimates of many simulated data sets from the standard uniform distribution (thin lines). See Gabry et al. (2019) for an example of interpreting the shape of the miscalibration that can be observed in these plots. The \code{ppc_loo_pit_qq()} function provides an alternative visualization of the miscalibration with a quantile-quantile (Q-Q) plot comparing the LOO PITs to the standard uniform distribution. Comparing to the uniform is not good for extreme probabilities close to 0 and 1, so it can sometimes be useful to set the \code{compare} argument to \code{"normal"}, which will produce a Q-Q plot comparing standard normal quantiles calculated from the PIT values to the theoretical standard normal quantiles. This can help see the (mis)calibration better for the extreme values. However, in most cases we have found that the overlaid density plot (\code{ppc_loo_pit_overlay()}) function will provide a clearer picture of calibration problems than the Q-Q plot. } \item{\code{ppc_loo_intervals()}, \code{ppc_loo_ribbon()}}{ Similar to \code{\link[=ppc_intervals]{ppc_intervals()}} and \code{\link[=ppc_ribbon]{ppc_ribbon()}} but the intervals are for the LOO predictive distribution. } } } \examples{ \dontrun{ library(rstanarm) library(loo) head(radon) fit <- stan_lmer( log_radon ~ floor + log_uranium + floor:log_uranium + (1 + floor | county), data = radon, iter = 100, chains = 2, cores = 2 ) y <- radon$log_radon yrep <- posterior_predict(fit) loo1 <- loo(fit, save_psis = TRUE, cores = 4) psis1 <- loo1$psis_object lw <- weights(psis1) # normalized log weights # marginal predictive check using LOO probability integral transform color_scheme_set("orange") ppc_loo_pit_overlay(y, yrep, lw = lw) ppc_loo_pit_qq(y, yrep, lw = lw) ppc_loo_pit_qq(y, yrep, lw = lw, compare = "normal") # can use the psis object instead of lw ppc_loo_pit_qq(y, yrep, psis_object = psis1) # loo predictive intervals vs observations keep_obs <- 1:50 ppc_loo_intervals(y, yrep, psis_object = psis1, subset = keep_obs) color_scheme_set("gray") ppc_loo_intervals(y, yrep, psis_object = psis1, subset = keep_obs, order = "median") } } \references{ Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (p. 152--153) Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. \emph{Statistics and Computing}. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4. arXiv preprint: \url{https://arxiv.org/abs/1507.04544} Boneva, L. I., Kendall, D., & Stefanov, I. (1971). Spline transformations: Three new diagnostic aids for the statistical data-analyst. \emph{J. R. Stat. Soc. B} (Methodological), 33(1), 1-71. https://www.jstor.org/stable/2986005. } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/MCMC-overview.Rd0000644000176200001440000000742314335220171015705 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-overview.R \name{MCMC-overview} \alias{MCMC-overview} \alias{MCMC} \title{Plots for Markov chain Monte Carlo simulations} \description{ The \strong{bayesplot} MCMC module provides various plotting functions for creating graphical displays of Markov chain Monte Carlo (MCMC) simulations. The \strong{MCMC plotting functions} section, below, provides links to the documentation for various categories of MCMC plots. Currently the MCMC plotting functions accept posterior draws provided in one of the following formats: \itemize{ \item \strong{3-D array}: An array with dimensions \verb{Iteration, Chain, Parameter} in that order. \item \strong{list}: A list of matrices, where each matrix corresponds to a Markov chain. All of the matrices should have the same number of iterations (rows) and parameters (columns), and parameters should have the same names and be in the same order. \item \strong{matrix (2-D array)}: A matrix with one column per parameter. If using matrix there should only be a single Markov chain or all chains should already be merged (stacked). \item \strong{data frame}: There are two types of data frames allowed. Either a data frame with one column per parameter (if only a single chain or all chains have already been merged), or a data frame with one column per parameter plus an additional column \code{"Chain"} that contains the chain number (an integer) corresponding to each row in the data frame. \item \strong{draws}: Any of the \code{draws} formats supported by the \pkg{\link{posterior}} package. } \strong{Note}: typically the user should \emph{not} include warmup iterations in the object passed to \strong{bayesplot} plotting functions, although for certain plots (e.g. trace plots) it can occasionally be useful to include the warmup iterations for diagnostic purposes. } \section{MCMC plotting functions}{ \itemize{ \item \link[=MCMC-distributions]{Posterior distributions}: Histograms and kernel density plots of parameter draws, optionally showing each Markov chain separately. \item \link[=MCMC-intervals]{Uncertainty intervals}: Uncertainty intervals computed from parameter draws. \item \link[=MCMC-traces]{Trace plots}: Times series of parameter draws, optionally including HMC/NUTS diagnostic information. \item \link[=MCMC-scatterplots]{Scatterplots}: Scatterplots, heatmaps, and pairs plots of parameter draws, optionally including HMC/NUTS diagnostic information. \item \link[=MCMC-parcoord]{Parallel coordinates plots}: Parallel coordinates plot of MCMC draws (one dimension per parameter), optionally including HMC/NUTS diagnostic information. \item \link[=MCMC-combos]{Combos}: Combination plots (e.g. trace plot + histogram). \item \link[=MCMC-diagnostics]{General MCMC diagnostics}: MCMC diagnostic plots including R-hat, effective sample size, autocorrelation. \link[=MCMC-nuts]{NUTS diagnostics}: Special diagnostic plots for the No-U-Turn Sampler. \item \link[=MCMC-recover]{Comparisons to "true" values}: Plots comparing MCMC estimates to "true" parameter values (e.g., values used to simulate data). } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/PPD-intervals.Rd0000644000176200001440000001223414335264324015756 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppd-intervals.R \name{PPD-intervals} \alias{PPD-intervals} \alias{ppd_intervals} \alias{ppd_intervals_grouped} \alias{ppd_ribbon} \alias{ppd_ribbon_grouped} \alias{ppd_intervals_data} \alias{ppd_ribbon_data} \title{PPD intervals} \usage{ ppd_intervals( ypred, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1 ) ppd_intervals_grouped( ypred, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1 ) ppd_ribbon( ypred, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25 ) ppd_ribbon_grouped( ypred, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25 ) ppd_intervals_data( ypred, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9 ) ppd_ribbon_data( ypred, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9 ) } \arguments{ \item{ypred}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{ypred}. The number of columns, \code{N}, is the number of predicted observations.} \item{x}{A numeric vector to use as the x-axis variable. For example, \code{x} could be a predictor variable from a regression model, a time variable for time-series models, etc. If \code{x} is missing or \code{NULL} then the observation index is used for the x-axis.} \item{...}{Currently unused.} \item{prob, prob_outer}{Values between \code{0} and \code{1} indicating the desired probability mass to include in the inner and outer intervals. The defaults are \code{prob=0.5} and \code{prob_outer=0.9}.} \item{alpha, size, fatten, linewidth}{Arguments passed to geoms. For ribbon plots \code{alpha} is passed to \code{\link[ggplot2:geom_ribbon]{ggplot2::geom_ribbon()}} to control the opacity of the outer ribbon and \code{size} is passed to \code{\link[ggplot2:geom_path]{ggplot2::geom_line()}} to control the size of the line representing the median prediction (\code{size=0} will remove the line). For interval plots \code{alpha}, \code{size}, \code{fatten}, and \code{linewidth} are passed to \code{\link[ggplot2:geom_linerange]{ggplot2::geom_pointrange()}} (\code{fatten=0} will remove the point estimates).} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Medians and central interval estimates of posterior or prior predictive distributions. Each of these functions makes the same plot as the corresponding \code{\link[=PPC-intervals]{ppc_}} function but without plotting any observed data \code{y}. The \strong{Plot Descriptions} section at \link{PPC-intervals} has details on the individual plots. } \examples{ color_scheme_set("brightblue") ypred <- example_yrep_draws() x <- example_x_data() group <- example_group_data() ppd_intervals(ypred[, 1:50]) ppd_intervals(ypred[, 1:50], fatten = 0) ppd_intervals(ypred[, 1:50], fatten = 0, linewidth = 2) ppd_intervals(ypred[, 1:50], prob_outer = 0.75, fatten = 0, linewidth = 2) # put a predictor variable on the x-axis ppd_intervals(ypred[, 1:100], x = x[1:100], fatten = 1) + ggplot2::labs(y = "Prediction", x = "Some variable of interest") # with a grouping variable too ppd_intervals_grouped( ypred = ypred[, 1:100], x = x[1:100], group = group[1:100], size = 2, fatten = 0, facet_args = list(nrow = 2) ) # even reducing size, ppd_intervals is too cluttered when there are many # observations included (ppd_ribbon is better) ppd_intervals(ypred, size = 0.5, fatten = 0.1, linewidth = 0.5) ppd_ribbon(ypred) ppd_ribbon(ypred, size = 0) # remove line showing median prediction } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) } \seealso{ Other PPDs: \code{\link{PPD-distributions}}, \code{\link{PPD-overview}}, \code{\link{PPD-test-statistics}} } \concept{PPDs} bayesplot/man/MCMC-scatterplots.Rd0000644000176200001440000004236714556270424016607 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-scatterplots.R \name{MCMC-scatterplots} \alias{MCMC-scatterplots} \alias{mcmc_scatter} \alias{mcmc_hex} \alias{mcmc_pairs} \alias{scatter_style_np} \alias{pairs_style_np} \alias{pairs_condition} \title{Scatterplots of MCMC draws} \usage{ mcmc_scatter( x, pars = character(), regex_pars = character(), transformations = list(), ..., size = 2.5, alpha = 0.8, np = NULL, np_style = scatter_style_np() ) mcmc_hex( x, pars = character(), regex_pars = character(), transformations = list(), ..., bins = 30, binwidth = NULL ) mcmc_pairs( x, pars = character(), regex_pars = character(), transformations = list(), ..., diag_fun = c("hist", "dens"), off_diag_fun = c("scatter", "hex"), diag_args = list(), off_diag_args = list(), condition = pairs_condition(), lp = NULL, np = NULL, np_style = pairs_style_np(), max_treedepth = NULL, grid_args = list(), save_gg_objects = TRUE ) scatter_style_np( div_color = "red", div_shape = 16, div_size = 2.5, div_alpha = 1 ) pairs_style_np( div_color = "red", div_shape = 4, div_size = 1, div_alpha = 1, td_color = "yellow2", td_shape = 3, td_size = 1, td_alpha = 1 ) pairs_condition(chains = NULL, draws = NULL, nuts = NULL) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{pars}{An optional character vector of parameter names. If neither \code{pars} nor \code{regex_pars} is specified then the default is to use \emph{all} parameters. As of version \verb{1.7.0}, \strong{bayesplot} also supports 'tidy' parameter selection by specifying \code{pars = vars(...)}, where \code{...} is specified the same way as in \link[dplyr:select]{dplyr::select(...)} and similar functions. Examples of using \code{pars} in this way can be found on the \link[=tidy-params]{Tidy parameter selection} page.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} \item{transformations}{Optionally, transformations to apply to parameters before plotting. If \code{transformations} is a function or a single string naming a function then that function will be used to transform all parameters. To apply transformations to particular parameters, the \code{transformations} argument can be a named list with length equal to the number of parameters to be transformed. Currently only univariate transformations of scalar parameters can be specified (multivariate transformations will be implemented in a future release). If \code{transformations} is a list, the name of each list element should be a parameter name and the content of each list element should be a function (or any item to match as a function via \code{\link[=match.fun]{match.fun()}}, e.g. a string naming a function). If a function is specified by its name as a string (e.g. \code{"log"}), then it can be used to construct a new parameter label for the appropriate parameter (e.g. \code{"log(sigma)"}). If a function itself is specified (e.g. \code{log} or \code{function(x) log(x)}) then \code{"t"} is used in the new parameter label to indicate that the parameter is transformed (e.g. \code{"t(sigma)"}). Note: due to partial argument matching \code{transformations} can be abbreviated for convenience in interactive use (e.g., \code{transform}).} \item{...}{Currently ignored.} \item{size, alpha}{For \code{mcmc_scatter()}, passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the appearance of the points.} \item{np}{Optionally, a data frame of NUTS sampler parameters, either created by \code{\link[=nuts_params]{nuts_params()}} or in the same form as the object returned by \code{\link[=nuts_params]{nuts_params()}}. The colors, shapes, and sizes of the superimposed points can be customized using the \code{np_style} argument.} \item{np_style}{If \code{np} is specified, \code{np_style} can be a call to the \code{scatter_style_np()} helper function (for \code{mcmc_scatter()}) or the \code{pairs_style_np()} helper function (for \code{mcmc_pairs()}) to specify arguments controlling the appearance of superimposed points representing NUTS diagnostic information. (Note: for \code{pairs_style_np()} the \code{size} arguments are interpreted as scaling factors).} \item{bins, binwidth}{For \code{mcmc_hex()}, an optional numeric vector of \emph{length two} passed to \code{\link[ggplot2:geom_hex]{ggplot2::geom_hex()}} to override the default binwidth in both the vertical and horizontal directions.} \item{diag_fun, off_diag_fun}{For \code{mcmc_pairs()}, the plotting function to use for the plots along the diagonal and for the off-diagonal plots, respectively. Currently \code{diag_fun} can be \code{"hist"} for histogram or \code{"dens"} for density, and \code{off_diag_fun} can be \code{"scatter"} for scatterplot or \code{"hex"} for a hexagonal heatmap.} \item{diag_args, off_diag_args}{For \code{mcmc_pairs()}, optional named lists of arguments to pass to the functions implied by the \code{diag_fun} and \code{off_diag_fun} arguments, respectively. For example, if \code{off_diag_fun} is \code{"scatter"} then \code{off_diag_args} could include optional arguments to \code{mcmc_scatter()} like \code{size} and \code{alpha}.} \item{condition}{For \code{mcmc_pairs()}, a call to the \code{pairs_condition()} helper function, which is used to specify a criterion for determining which chains (or iterations) are shown in the plots above the diagonal and which are shown in the plots below the diagonal. The histograms (or density plots) along the diagonal are always made using all chains and iterations, but the scatterplots (or hex plots) above and below the diagonal show different combinations of chains/iterations depending on \code{condition}. The default is a call to \code{pairs_condition()} with none of its arguments specified. In this case half of the chains (or roughly half if there are an odd number) will be used in the plots above the diagonal and the rest in the plots below the diagonal. The \code{chains}, \code{draws}, and \code{nuts} arguments to \code{pairs_condition()}, which are documented below, can be used to change this default.} \item{lp}{For \code{mcmc_pairs()}, a molten data frame of draws of the log-posterior or, more commonly, of a quantity equal to the log-posterior up to a constant. \code{lp} should either be created via \code{\link[=log_posterior]{log_posterior()}} or be an object with the same form as the object returned by \code{\link[=log_posterior]{log_posterior()}}.} \item{max_treedepth}{For \code{mcmc_pairs()}, an integer representing the maximum treedepth allowed when fitting the model (if fit using NUTS). This is only needed for detecting which transitions (if any) hit the maximum treedepth.} \item{grid_args, save_gg_objects}{For \code{mcmc_pairs()}, arguments to pass to \code{\link[=bayesplot_grid]{bayesplot_grid()}}. For example, since \code{mcmc_pairs()} returns more than a single ggplot object, using \code{\link[=ggtitle]{ggtitle()}} afterwards will not work. But you you can still add a title to the plot using \code{grid_args = list(top="My title")}.} \item{div_color, div_shape, div_size, div_alpha, td_color, td_shape, td_size, td_alpha}{Optional arguments to the \code{scatter_style_np()} or \code{pairs_style_np()} helper functions that are eventually passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}}.The default values are displayed in the \strong{Usage} section above.} \item{chains, draws, nuts}{Optional arguments to the \code{pairs_condition()} helper function, which is used to specify the \code{condition} argument for \code{mcmc_pairs()}. \itemize{ \item The \code{chains} argument can be used to select some subset of the chains. If \code{chains} is an integer vector then the behavior is the same as the default (half the chains above the diagonal and half below) except using only the specified subset of chains. Alternatively, \code{chains} can be a list of two integer vectors with the first specifying the chains to be shown in the plots above the diagonal and the second for below the diagonal. \item The \code{draws} argument to \code{pairs_condition()} can be used to directly specify which realizations are plotted above and below the diagonal. \code{draws} can be a single proportion, which is interpreted as the proportion of realizations (among all chains) to plot in the lower panel starting with the first realization in each chain, with the complement (from the end of each chain) plotted in the upper panel. Alternatively \code{draws} can be a logical vector with length equal to the product of the number of iterations and the number of chains, in which case realizations corresponding to \code{FALSE} and \code{TRUE} will be plotted in the lower and upper panels, respectively. \item For models fit using NUTS, the \code{nuts} argument to \code{pairs_condition()} can be used. It takes a (possibly abbreviated) string to select among \code{"accept_stat__"}, \code{"stepsize__"}, \code{"treedepth__"}, \code{"n_leapfrog__"}, \code{"divergent__"}, \code{"energy__"}, and \code{"lp__"}. These are the sampler parameters associated with \code{\link[=NUTS]{NUTS()}} (and \code{"lp__"} is the log-posterior up to an additive constant). In this case, plots below the diagonal will contain realizations that are below the median of the indicated variable (or are zero in the case of \code{"divergent__"}), and plots above the diagonal will contain realizations that are greater than or equal to the median of the indicated variable (or are one in the case of \code{"divergent__"}). If \code{"lp__"} is used then the \code{lp} argument to \code{mcmc_pairs} must also be specified. For the other NUTS parameters the \code{np} argument to \code{mcmc_pairs()} must also be specified. }} } \value{ \code{mcmc_scatter()} and \code{mcmc_hex()} return a ggplot object that can be further customized using the \strong{ggplot2} package. \code{mcmc_pairs()} returns many ggplot objects organized into a grid via \code{\link[=bayesplot_grid]{bayesplot_grid()}}. } \description{ Scatterplots, hexagonal heatmaps, and pairs plots from MCMC draws. See the \strong{Plot Descriptions} section, below, for details. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_scatter()}}{ Bivariate scatterplot of posterior draws. If using a very large number of posterior draws then \code{mcmc_hex()} may be preferable to avoid overplotting. For models fit using \link{NUTS} the \code{np}, and \code{np_style} arguments can be used to add additional information in the plot (in this case the approximate location of divergences). For more on why the scatter plot with divergences is a useful diagnostic tool see \href{https://github.com/jgabry/bayes-vis-paper#readme}{Gabry et al. (2019)}. } \item{\code{mcmc_hex()}}{ Hexagonal heatmap of 2-D bin counts. This plot is useful in cases where the posterior sample size is large enough that \code{mcmc_scatter()} suffers from overplotting. } \item{\code{mcmc_pairs()}}{ A square plot matrix with univariate marginal distributions along the diagonal (as histograms or kernel density plots) and bivariate distributions off the diagonal (as scatterplots or hex heatmaps). For the off-diagonal plots, the default is to split the chains so that (roughly) half are displayed above the diagonal and half are below (all chains are always merged together for the plots along the diagonal). Other possibilities are available by setting the \code{condition} argument. Additionally, extra diagnostic information for models fit using \link{NUTS} can be added to the pairs plot using the \code{lp}, \code{np}, and \code{np_style} arguments. If \code{np} is specified (and \code{condition} is \emph{not} \code{"divergent__"}), then points (red, by default) will be superimposed onto the off-diagonal plots indicating which (if any) iterations encountered a divergent transition. Also, if both \code{np} and \code{max_treedepth} are specified then points (yellow, by default) will be superimposed to indicate a transition that hit the maximum treedepth rather than terminated its evolution normally. The \code{np_style} argument can be used with the \code{pairs_style_np()} convenience function to change the appearance of these overlaid points. See the \strong{Examples} section. } } } \examples{ library("ggplot2") # some parameter draws to use for demonstration x <- example_mcmc_draws(params = 6) dimnames(x) # scatterplot of alpha vs log(sigma) color_scheme_set("teal") (p <- mcmc_scatter(x, pars = c("alpha", "sigma"), transform = list(sigma = "log"))) p + labs( title = "Insert your own headline-grabbing title", subtitle = "with a provocative subtitle", caption = "and a controversial caption", x = expression(alpha), y = expression(log(sigma)) ) # add ellipse p + stat_ellipse(level = 0.9, color = "gray20", size = 1) # add contour color_scheme_set("red") p2 <- mcmc_scatter(x, pars = c("alpha", "sigma"), size = 3.5, alpha = 0.25) p2 + stat_density_2d(color = "black", size = .5) # can also add lines/smooths color_scheme_set("pink") (p3 <- mcmc_scatter(x, pars = c("alpha", "beta[3]"), alpha = 0.25, size = 3)) p3 + geom_smooth(method = "lm", se = FALSE, color = "gray20", size = .75, linetype = 2) \donttest{ if (requireNamespace("hexbin", quietly = TRUE)) { # hexagonal heatmap color_scheme_set("brightblue") (p <- mcmc_hex(x, pars = c("sigma", "alpha"), transform = list(sigma = "log"))) p + plot_bg(fill = "gray95") p + plot_bg(fill = "gray95") + panel_bg(fill = "gray70") } } \donttest{ color_scheme_set("purple") # pairs plots # default of condition=NULL implies splitting chains between upper and lower panels mcmc_pairs(x, pars = "alpha", regex_pars = "beta\\\\[[1,4]\\\\]", off_diag_args = list(size = 1, alpha = 0.5)) # change to density plots instead of histograms and hex plots instead of # scatterplots mcmc_pairs(x, pars = "alpha", regex_pars = "beta\\\\[[1,4]\\\\]", diag_fun = "dens", off_diag_fun = "hex") # plot chain 1 above diagonal and chains 2, 3, and 4 below color_scheme_set("brightblue") mcmc_pairs(x, pars = "alpha", regex_pars = "beta\\\\[[1,4]\\\\]", diag_fun = "dens", off_diag_fun = "hex", condition = pairs_condition(chains = list(1, 2:4))) } \dontrun{ ### Adding NUTS diagnostics to scatterplots and pairs plots # examples using rstanarm package library(rstanarm) # for demonstration purposes, intentionally fit a model that # will (almost certainly) have some divergences fit <- stan_glm( mpg ~ ., data = mtcars, iter = 1000, refresh = 0, # this combo of prior and adapt_delta should lead to some divergences prior = hs(), adapt_delta = 0.9 ) posterior <- as.array(fit) np <- nuts_params(fit) # mcmc_scatter with divergences highlighted color_scheme_set("brightblue") mcmc_scatter(posterior, pars = c("wt", "sigma"), np = np) color_scheme_set("darkgray") div_style <- scatter_style_np(div_color = "green", div_shape = 4, div_size = 4) mcmc_scatter(posterior, pars = c("sigma", "(Intercept)"), np = np, np_style = div_style) # split the draws according to above/below median accept_stat__ # and show approximate location of divergences (red points) color_scheme_set("brightblue") mcmc_pairs( posterior, pars = c("wt", "cyl", "sigma"), off_diag_args = list(size = 1, alpha = 1/3), condition = pairs_condition(nuts = "accept_stat__"), np = np ) # more customizations: # - transform sigma to log(sigma) # - median log-posterior as 'condition' # - hex instead of scatter for off-diagonal plots # - show points where max treedepth hit in blue color_scheme_set("darkgray") mcmc_pairs( posterior, pars = c("wt", "cyl", "sigma"), transform = list(sigma = "log"), off_diag_fun = "hex", condition = pairs_condition(nuts = "lp__"), lp = log_posterior(fit), np = np, np_style = pairs_style_np(div_color = "firebrick", td_color = "blue", td_size = 2), # for demonstration purposes, set max_treedepth to a value that will # result in at least a few max treedepth warnings max_treedepth = with(np, -1 + max(Value[Parameter == "treedepth__"])) ) } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/available_ppc.Rd0000644000176200001440000000337114212422623016102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/available-module-functions.R \name{available_ppc} \alias{available_ppc} \alias{available_ppd} \alias{available_mcmc} \title{Get or view the names of available plotting or data functions} \usage{ available_ppc(pattern = NULL, fixed = FALSE, invert = FALSE, plots_only = TRUE) available_ppd(pattern = NULL, fixed = FALSE, invert = FALSE, plots_only = TRUE) available_mcmc( pattern = NULL, fixed = FALSE, invert = FALSE, plots_only = TRUE ) } \arguments{ \item{pattern, fixed, invert}{Passed to \code{\link[base:grep]{base::grep()}}.} \item{plots_only}{If \code{TRUE} (the default) only plotting functions are searched for. If \code{FALSE} then functions that return data for plotting (functions ending in \verb{_data()}) are also included.} } \value{ A possibly empty character vector of function names with several additional attributes (for use by a custom print method). If \code{pattern} is missing then the returned object contains the names of all available plotting functions in the \link{MCMC}, \link{PPC}, or \link{PPD} module, depending on which function is called. 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׆,.4deOy>l><6O܍o= x^W2 )f$00iȟY VZd>̟U%Ȳ>w<؂g9NEg "҉ KMx_ BÌuK1 C-Dl,dpN*ҬN#}U⑏}NxǻĮ $~r=)S #ȈUЏ22=F7G}|lUd]|+Jmç5:{_/ȄnIY`W}.&:@ie n&G?y4~NNcl7.ت80/`H˲yLj8Zސ}X4 ghu3(JXivl'-,3UŲ!wc^3Dϖ 2!9ĘsH:%`x~ : 1xo þix'_kti'zJ'o|ý/v|+t[ 2B !GCx.6c 0UhXbD$1u1X`w+PCLG6C-^h\Ȁ yL6!^CK~H$y wq Ǡ:Iduje9]g=t6qO~Շ5'Qx+gzMLCy~}/`:N~~.3g&'d]37E:8UEM؀ڄ Mf.?A1!Rdvɳ6YZ0SA!M2Q#i"?KZpȷ 1j| V-_ְ0"`JCc`Ik|ӮU 84@8ZOj¶A.BAse]peӟ,pbO4n2pl1iNC!ur :"W7g_7wDOD| "vfj>cdjy{omoZHk3L%Μ,A[ #+%) K8\|AF>0CCFY`$A31=C58*U/fDnI>JW:ЎQȇ|D=EVxznd~ktwL: Ncm-pl(ދa:`H1a`uPp1wY.q8ىϘ՟MNJDljxx}.N O;t}2XgVGYC=]`''tm:±0‘:5nؚE>AZu*NO5Nn&PqL`_~J1l(62&faը.:?'Ҧj$C/'Vޱ p~f\d4^l)o9; ^/r 1Emx̀F~2|EYf{LhS,lDx7 H7S09JQd<]?Pe ?f=Xg>Oqm^"='~_wzՅhz pp?gLq@l[qU&`iE &&i.Pa}/2an3҇%еj<.N}Whl~/ ,ͮE|K+e\;Zúl.-v١²]>ӸOK[!܇p~y.A&l`:e[;LeBcepLW/hY2* _Wvϝ]Ӯ㣊]v8gK֠P8 bEa{-W:4Z䐗AWa4QTEpd˭ LL<|t$M;`_giuoyL[~ _@Z/u8f:Ted|Y0Y3Yry6oӏHyKt7h|V Lb?<*z5RFv+˿◪/]\;:txhf|U1^]CuMǽɅ*JqK_ 6[F D D D D D D D D D D N]݂8#t@@@@@@@@@@@-F D D D D D D D D D PqCǢlyw6J J J J J J J J J J ?%%%%%%%%%%$`˫#QQQQQQQQQQ=O+IENDB`bayesplot/man/MCMC-traces.Rd0000644000176200001440000003300614563172262015326 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-traces.R \name{MCMC-traces} \alias{MCMC-traces} \alias{mcmc_trace} \alias{mcmc_trace_highlight} \alias{trace_style_np} \alias{mcmc_rank_overlay} \alias{mcmc_rank_hist} \alias{mcmc_rank_ecdf} \alias{mcmc_trace_data} \title{Trace and rank plots of MCMC draws} \usage{ mcmc_trace( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), n_warmup = 0, iter1 = 0, window = NULL, size = NULL, np = NULL, np_style = trace_style_np(), divergences = NULL ) mcmc_trace_highlight( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), n_warmup = 0, window = NULL, size = NULL, alpha = 0.2, highlight = 1 ) trace_style_np(div_color = "red", div_size = 0.25, div_alpha = 1) mcmc_rank_overlay( x, pars = character(), regex_pars = character(), transformations = list(), facet_args = list(), ..., n_bins = 20, ref_line = FALSE ) mcmc_rank_hist( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), n_bins = 20, ref_line = FALSE ) mcmc_rank_ecdf( x, pars = character(), regex_pars = character(), transformations = list(), ..., K = NULL, facet_args = list(), prob = 0.99, plot_diff = FALSE, interpolate_adj = NULL ) mcmc_trace_data( x, pars = character(), regex_pars = character(), transformations = list(), ..., highlight = NULL, n_warmup = 0, iter1 = 0 ) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{pars}{An optional character vector of parameter names. If neither \code{pars} nor \code{regex_pars} is specified then the default is to use \emph{all} parameters. As of version \verb{1.7.0}, \strong{bayesplot} also supports 'tidy' parameter selection by specifying \code{pars = vars(...)}, where \code{...} is specified the same way as in \link[dplyr:select]{dplyr::select(...)} and similar functions. Examples of using \code{pars} in this way can be found on the \link[=tidy-params]{Tidy parameter selection} page.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} \item{transformations}{Optionally, transformations to apply to parameters before plotting. If \code{transformations} is a function or a single string naming a function then that function will be used to transform all parameters. To apply transformations to particular parameters, the \code{transformations} argument can be a named list with length equal to the number of parameters to be transformed. Currently only univariate transformations of scalar parameters can be specified (multivariate transformations will be implemented in a future release). If \code{transformations} is a list, the name of each list element should be a parameter name and the content of each list element should be a function (or any item to match as a function via \code{\link[=match.fun]{match.fun()}}, e.g. a string naming a function). If a function is specified by its name as a string (e.g. \code{"log"}), then it can be used to construct a new parameter label for the appropriate parameter (e.g. \code{"log(sigma)"}). If a function itself is specified (e.g. \code{log} or \code{function(x) log(x)}) then \code{"t"} is used in the new parameter label to indicate that the parameter is transformed (e.g. \code{"t(sigma)"}). Note: due to partial argument matching \code{transformations} can be abbreviated for convenience in interactive use (e.g., \code{transform}).} \item{...}{Currently ignored.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{n_warmup}{An integer; the number of warmup iterations included in \code{x}. The default is \code{n_warmup = 0}, i.e. to assume no warmup iterations are included. If \code{n_warmup > 0} then the background for iterations \code{1:n_warmup} is shaded gray.} \item{iter1}{An integer; the iteration number of the first included draw (default is \code{0}). This can be used to make it more obvious that the warmup iterations have been discarded from the traceplot. It cannot be specified if \code{n_warmup} is also set to a positive value.} \item{window}{An integer vector of length two specifying the limits of a range of iterations to display.} \item{size}{An optional value to override the default line size for \code{mcmc_trace()} or the default point size for \code{mcmc_trace_highlight()}.} \item{np}{For models fit using \link{NUTS} (more generally, any \href{https://en.wikipedia.org/wiki/Symplectic_integrator}{symplectic integrator}), an optional data frame providing NUTS diagnostic information. The data frame should be the object returned by \code{\link[=nuts_params]{nuts_params()}} or one with the same structure. If \code{np} is specified then tick marks are added to the bottom of the trace plot indicating within which iterations there was a divergence (if there were any). See the end of the \strong{Examples} section, below.} \item{np_style}{A call to the \code{trace_style_np()} helper function to specify arguments controlling the appearance of tick marks representing divergences (if the \code{np} argument is specified).} \item{divergences}{Deprecated. Use the \code{np} argument instead.} \item{alpha}{For \code{mcmc_trace_highlight()}, passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the transparency of the points for the chains not highlighted.} \item{highlight}{For \code{mcmc_trace_highlight()}, an integer specifying one of the chains that will be more visible than the others in the plot.} \item{div_color, div_size, div_alpha}{Optional arguments to the \code{trace_style_np()} helper function that are eventually passed to \code{\link[ggplot2:geom_rug]{ggplot2::geom_rug()}} if the \code{np} argument is also specified. They control the color, size, and transparency specifications for showing divergences in the plot. The default values are displayed in the \strong{Usage} section above.} \item{n_bins}{For the rank plots, the number of bins to use for the histogram of rank-normalized MCMC samples. Defaults to \code{20}.} \item{ref_line}{For the rank plots, whether to draw a horizontal line at the average number of ranks per bin. Defaults to \code{FALSE}.} \item{K}{An optional integer defining the number of equally spaced evaluation points for the PIT-ECDF. Reducing K when using \code{interpolate_adj = FALSE} makes computing the confidence bands faster. For \code{ppc_pit_ecdf} and \code{ppc_pit_ecdf_grouped}, if PIT values are supplied, defaults to \code{length(pit)}, otherwise yrep determines the maximum accuracy of the estimated PIT values and \code{K} is set to \code{min(nrow(yrep) + 1, 1000)}. For \code{mcmc_rank_ecdf}, defaults to the number of iterations per chain in \code{x}.} \item{prob}{For \code{mcmc_rank_ecdf()}, a value between 0 and 1 specifying the desired simultaneous confidence of the confidence bands to be drawn for the rank ECDF plots.} \item{plot_diff}{For \code{mcmc_rank_ecdf()}, a boolean specifying if the difference between the observed rank ECDFs and the theoretical expectation should be drawn instead of the unmodified rank ECDF plots.} \item{interpolate_adj}{A boolean defining if the simultaneous confidence bands should be interpolated based on precomputed values rather than computed exactly. Computing the bands may be computationally intensive and the approximation gives a fast method for assessing the ECDF trajectory. The default is to use interpolation if \code{K} is greater than 200.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. \code{mcmc_trace_data()} returns the data for the trace \emph{and} rank plots in the same data frame. } \description{ Trace and rank plots of MCMC draws. See the \strong{Plot Descriptions} section, below, for details. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_trace()}}{ Standard trace plots of MCMC draws. For models fit using \link{NUTS}, the \code{np} argument can be used to also show divergences on the trace plot. } \item{\code{mcmc_trace_highlight()}}{ Traces are plotted using points rather than lines and the opacity of all chains but one (specified by the \code{highlight} argument) is reduced. } \item{\code{mcmc_rank_hist()}}{ Whereas traditional trace plots visualize how the chains mix over the course of sampling, rank histograms visualize how the values from the chains mix together in terms of ranking. An ideal plot would show the rankings mixing or overlapping in a uniform distribution. See Vehtari et al. (2019) for details. } \item{\code{mcmc_rank_overlay()}}{ Ranks from \code{mcmc_rank_hist()} are plotted using overlaid lines in a single panel. } \item{\code{mcmc_rank_ecdf()}}{ The ECDFs of the ranks from \code{mcmc_rank_hist()} are plotted with the simultaneous confidence bands with a coverage determined by \code{prob}, that is, bands that completely cover all of the rank ECDFs with the probability \code{prob}. If \code{plot_diff = TRUE}, the difference between the observed rank ECDFs and the theoretical expectation for samples originating from the same distribution is drawn. See Säilynoja et al. (2021) for details. } } } \examples{ # some parameter draws to use for demonstration x <- example_mcmc_draws(chains = 4, params = 6) dim(x) dimnames(x) # trace plots of the betas color_scheme_set("viridis") mcmc_trace(x, regex_pars = "beta") \donttest{ color_scheme_set("viridisA") mcmc_trace(x, regex_pars = "beta") color_scheme_set("viridisC") mcmc_trace(x, regex_pars = "beta") } # mix color schemes color_scheme_set("mix-blue-red") mcmc_trace(x, regex_pars = "beta") # use traditional ggplot discrete color scale mcmc_trace(x, pars = c("alpha", "sigma")) + ggplot2::scale_color_discrete() # zoom in on a window of iterations, increase line size, # add tick marks, move legend to the top, add gray background color_scheme_set("viridisA") mcmc_trace(x[,, 1:4], window = c(100, 130), size = 1) + panel_bg(fill = "gray90", color = NA) + legend_move("top") # Rank-normalized histogram plots. Instead of showing how chains mix over # time, look at how the ranking of MCMC samples mixed between chains. color_scheme_set("viridisE") mcmc_rank_hist(x, "alpha") mcmc_rank_hist(x, pars = c("alpha", "sigma"), ref_line = TRUE) mcmc_rank_overlay(x, "alpha") # ECDF and ECDF difference plots of the ranking of MCMC samples between chains. # Provide 99\% simultaneous confidence intervals for the chains sampling from # the same distribution. mcmc_rank_ecdf(x, prob = 0.99) mcmc_rank_ecdf(x, prob = 0.99, plot_diff = TRUE) \dontrun{ # parse facet label text color_scheme_set("purple") p <- mcmc_trace( x, regex_pars = "beta\\\\[[1,3]\\\\]", facet_args = list(labeller = ggplot2::label_parsed) ) p + facet_text(size = 15) # mark first 100 draws as warmup mcmc_trace(x, n_warmup = 100) # plot as points, highlighting chain 2 color_scheme_set("brightblue") mcmc_trace_highlight(x, pars = "sigma", highlight = 2, size = 2) # for models fit using HMC/NUTS divergences can be displayed in the trace plot library("rstanarm") fit <- stan_glm(mpg ~ ., data = mtcars, refresh = 0, # next line to keep example fast and also ensure we get some divergences prior = hs(), iter = 400, adapt_delta = 0.8) # extract draws using as.array (instead of as.matrix) to keep # chains separate for trace plot posterior <- as.array(fit) # for stanfit and stanreg objects use nuts_params() to get the divergences mcmc_trace(posterior, pars = "sigma", np = nuts_params(fit)) color_scheme_set("viridis") mcmc_trace( posterior, pars = c("wt", "sigma"), size = 0.5, facet_args = list(nrow = 2), np = nuts_params(fit), np_style = trace_style_np(div_color = "black", div_size = 0.5) ) } } \references{ Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., Bürkner, P. (2019). Rank-normalization, folding, and localization: An improved \emph{R}-hat for assessing convergence of MCMC. \href{https://arxiv.org/abs/1903.08008}{arXiv preprint}. Säilynoja, T., Bürkner, P., Vehtari, A. (2021). Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison \href{https://arxiv.org/abs/2103.10522}{arXiv preprint}. } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}} } \concept{MCMC} bayesplot/man/PPD-distributions.Rd0000644000176200001440000001052414556270424016654 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppd-distributions.R \name{PPD-distributions} \alias{PPD-distributions} \alias{ppd_data} \alias{ppd_dens_overlay} \alias{ppd_ecdf_overlay} \alias{ppd_dens} \alias{ppd_hist} \alias{ppd_freqpoly} \alias{ppd_freqpoly_grouped} \alias{ppd_boxplot} \title{PPD distributions} \usage{ ppd_data(ypred, group = NULL) ppd_dens_overlay( ypred, ..., size = 0.25, alpha = 0.7, trim = FALSE, bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024 ) ppd_ecdf_overlay( ypred, ..., discrete = FALSE, pad = TRUE, size = 0.25, alpha = 0.7 ) ppd_dens(ypred, ..., trim = FALSE, size = 0.5, alpha = 1) ppd_hist(ypred, ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) ppd_freqpoly( ypred, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1 ) ppd_freqpoly_grouped( ypred, group, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1 ) ppd_boxplot(ypred, ..., notch = TRUE, size = 0.5, alpha = 1) } \arguments{ \item{ypred}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{ypred}. The number of columns, \code{N}, is the number of predicted observations.} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{...}{Currently unused.} \item{size, alpha}{Passed to the appropriate geom to control the appearance of the predictive distributions.} \item{trim}{A logical scalar passed to \code{\link[ggplot2:geom_density]{ggplot2::geom_density()}}.} \item{bw, adjust, kernel, n_dens}{Optional arguments passed to \code{\link[stats:density]{stats::density()}} to override default kernel density estimation parameters. \code{n_dens} defaults to \code{1024}.} \item{discrete}{For \code{ppc_ecdf_overlay()}, should the data be treated as discrete? The default is \code{FALSE}, in which case \code{geom="line"} is passed to \code{\link[ggplot2:stat_ecdf]{ggplot2::stat_ecdf()}}. If \code{discrete} is set to \code{TRUE} then \code{geom="step"} is used.} \item{pad}{A logical scalar passed to \code{\link[ggplot2:stat_ecdf]{ggplot2::stat_ecdf()}}.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{freq}{For histograms, \code{freq=TRUE} (the default) puts count on the y-axis. Setting \code{freq=FALSE} puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the \code{\link[=yaxis_text]{yaxis_text()}} convenience function.)} \item{notch}{For the box plot, a logical scalar passed to \code{\link[ggplot2:geom_boxplot]{ggplot2::geom_boxplot()}}. Note: unlike \code{geom_boxplot()}, the default is \code{notch=TRUE}.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Plot posterior or prior predictive distributions. Each of these functions makes the same plot as the corresponding \code{\link[=PPC-distributions]{ppc_}} function but without plotting any observed data \code{y}. The \strong{Plot Descriptions} section at \link{PPC-distributions} has details on the individual plots. } \details{ For Binomial data, the plots may be more useful if the input contains the "success" \emph{proportions} (not discrete "success" or "failure" counts). } \examples{ # difference between ppd_dens_overlay() and ppc_dens_overlay() color_scheme_set("brightblue") preds <- example_yrep_draws() ppd_dens_overlay(ypred = preds[1:50, ]) ppc_dens_overlay(y = example_y_data(), yrep = preds[1:50, ]) } \seealso{ Other PPDs: \code{\link{PPD-intervals}}, \code{\link{PPD-overview}}, \code{\link{PPD-test-statistics}} } \concept{PPDs} bayesplot/man/reexports.Rd0000644000176200001440000000061014127610044015345 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tidy-params.R \docType{import} \name{reexports} \alias{reexports} \alias{vars} \title{Objects exported from other packages} \keyword{internal} \description{ These objects are imported from other packages. Follow the links below to see their documentation. \describe{ \item{dplyr}{\code{\link[dplyr]{vars}}} }} bayesplot/man/PPC-overview.Rd0000644000176200001440000001540214335220171015604 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-overview.R \name{PPC-overview} \alias{PPC-overview} \alias{PPC} \title{Graphical posterior predictive checking} \description{ The \strong{bayesplot} PPC module provides various plotting functions for creating graphical displays comparing observed data to simulated data from the posterior (or prior) predictive distribution. See the sections below for a brief discussion of the ideas behind posterior predictive checking, an overview of the available PPC plots, and tips on providing an interface to \strong{bayesplot} from another package. For plots of posterior (or prior) predictive distributions that do \emph{not} include observed data see \link{PPD-overview} instead. } \details{ The idea behind posterior predictive checking is simple: if a model is a good fit then we should be able to use it to generate data that looks a lot like the data we observed. \subsection{Posterior predictive distribution}{ To generate the data used for posterior predictive checks we simulate from the \emph{posterior predictive distribution}. The posterior predictive distribution is the distribution of the outcome variable implied by a model after using the observed data \eqn{y} (a vector of outcome values), and typically predictors \eqn{X}, to update our beliefs about the unknown parameters \eqn{\theta} in the model. For each draw of the parameters \eqn{\theta} from the posterior distribution \eqn{p(\theta \,|\, y, X)}{p(\theta | y, X)} we generate an entire vector of outcomes. The result is an \eqn{S \times N}{S x N} matrix of simulations, where \eqn{S} is the the size of the posterior sample (number of draws from the posterior distribution) and \eqn{N} is the number of data points in \eqn{y}. That is, each row of the matrix is an individual "replicated" dataset of \eqn{N} observations. } \subsection{Notation}{ When simulating from the posterior predictive distribution we can use either the same values of the predictors \eqn{X} that we used when fitting the model or new observations of those predictors. When we use the same values of \eqn{X} we denote the resulting simulations by \eqn{y^{rep}}{yrep} as they can be thought of as \emph{replications} of the outcome \eqn{y} rather than predictions for future observations. This corresponds to the notation from Gelman et. al. (2013) and is the notation used throughout the documentation for this package. } \subsection{Graphical posterior predictive checking}{ Using the datasets \eqn{y^{rep}}{yrep} drawn from the posterior predictive distribution, the functions in the \strong{bayesplot} package produce various graphical displays comparing the observed data \eqn{y} to the replications. For a more thorough discussion of posterior predictive checking see Chapter 6 of Gelman et. al. (2013). } \subsection{Prior predictive checking}{ To use \strong{bayesplot} for \emph{prior} predictive checks you can simply use draws from the prior predictive distribution instead of the posterior predictive distribution. See Gabry et al. (2019) for more on prior predictive checking and when it is reasonable to compare the prior predictive distribution to the observed data. If you want to avoid using the observed data for prior predictive checks then you can use the \strong{bayesplot} \link{PPD} plots instead, which do not take a \code{y} argument, or you can use the PPC plots but provide plausible or implausible \code{y} values that you want to compare to the prior predictive realizations. } } \section{PPC plotting functions}{ The plotting functions for prior and posterior predictive checking all have the prefix \code{ppc_} and all require the arguments \code{y}, a vector of observations, and \code{yrep}, a matrix of replications (in-sample predictions). The plots are organized into several categories, each with its own documentation: \itemize{ \item \link{PPC-distributions}: Histograms, kernel density estimates, boxplots, and other plots comparing the empirical distribution of data \code{y} to the distributions of individual simulated datasets (rows) in \code{yrep}. \item \link{PPC-test-statistics}: The distribution of a statistic, or a pair of statistics, over the simulated datasets (rows) in \code{yrep} compared to value of the statistic(s) computed from \code{y}. \item \link{PPC-intervals}: Interval estimates of \code{yrep} with \code{y} overlaid. The x-axis variable can be optionally specified by the user (e.g. to plot against a predictor variable or over time). \item \link{PPC-errors}: Plots of predictive errors (\code{y - yrep}) computed from \code{y} and each of the simulated datasets (rows) in \code{yrep}. For binomial models binned error plots are also available. \item \link{PPC-scatterplots}: Scatterplots (and similar visualizations) of the data \code{y} vs. individual simulated datasets (rows) in \code{yrep}, or vs. the average value of the distributions of each data point (columns) in \code{yrep}. \item \link{PPC-discrete}: PPC functions that can only be used if \code{y} and \code{yrep} are discrete. For example, rootograms for count outcomes and bar plots for ordinal, categorical, and multinomial outcomes. \item \link{PPC-loo}: PPC functions for predictive checks based on (approximate) leave-one-out (LOO) cross-validation. ' \item \link{PPC-censoring}: PPC functions comparing the empirical distribution of censored data \code{y} to the distributions of individual simulated datasets (rows) in \code{yrep}. } } \section{Providing an interface for predictive checking from another package}{ In addition to the various plotting functions, the \strong{bayesplot} package provides the S3 generic \code{\link[=pp_check]{pp_check()}}. Authors of \R packages for Bayesian inference are encouraged to define \code{pp_check()} methods for the fitted model objects created by their packages. See the package vignettes for more details and a simple example, and see the \strong{rstanarm} and \strong{brms} packages for full examples of \code{pp_check()} methods. } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/theme_default.Rd0000644000176200001440000000331114127610044016121 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot-ggplot-themes.R \name{theme_default} \alias{theme_default} \title{Default \strong{bayesplot} plotting theme} \usage{ theme_default( base_size = getOption("bayesplot.base_size", 12), base_family = getOption("bayesplot.base_family", "serif") ) } \arguments{ \item{base_size, base_family}{Base font size and family (passed to \code{\link[ggplot2:ggtheme]{ggplot2::theme_bw()}}). It is possible to set \code{"bayesplot.base_size"} and \code{"bayesplot.base_family"} via \code{\link[=options]{options()}} to change the defaults, which are \code{12} and \code{"serif"}, respectively.} } \value{ A ggplot \link[ggplot2:theme]{theme} object. } \description{ The \code{\link[=theme_default]{theme_default()}} function returns the default ggplot \link[ggplot2:theme]{theme} used by the \strong{bayesplot} plotting functions. See \code{\link[=bayesplot_theme_set]{bayesplot_theme_set()}} for details on setting and updating the plotting theme. } \examples{ class(theme_default()) bayesplot_theme_set() # defaults to setting theme_default() x <- example_mcmc_draws() mcmc_hist(x) # change the default font size and family for bayesplots bayesplot_theme_set(theme_default(base_size = 8, base_family = "sans")) mcmc_hist(x) mcmc_areas(x, regex_pars = "beta") # change back bayesplot_theme_set() mcmc_areas(x, regex_pars = "beta") } \seealso{ \code{\link[=bayesplot_theme_set]{bayesplot_theme_set()}} to change the ggplot theme. \link{bayesplot-colors} to set or view the color scheme used for plotting. \link{bayesplot-helpers} for a variety of convenience functions, many of which provide shortcuts for tweaking theme elements after creating a plot. } bayesplot/man/PPD-test-statistics.Rd0000644000176200001440000001152314556270424017121 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppd-test-statistics.R \name{PPD-test-statistics} \alias{PPD-test-statistics} \alias{PPD-statistics} \alias{ppd_stat} \alias{ppd_stat_grouped} \alias{ppd_stat_freqpoly} \alias{ppd_stat_freqpoly_grouped} \alias{ppd_stat_2d} \alias{ppd_stat_data} \title{PPD test statistics} \usage{ ppd_stat( ypred, stat = "mean", ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppd_stat_grouped( ypred, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppd_stat_freqpoly( ypred, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE ) ppd_stat_freqpoly_grouped( ypred, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE ) ppd_stat_2d(ypred, stat = c("mean", "sd"), ..., size = 2.5, alpha = 0.7) ppd_stat_data(ypred, group = NULL, stat) } \arguments{ \item{ypred}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{ypred}. The number of columns, \code{N}, is the number of predicted observations.} \item{stat}{A single function or a string naming a function, except for the 2D plot which requires a vector of exactly two names or functions. In all cases the function(s) should take a vector input and return a scalar statistic. If specified as a string (or strings) then the legend will display the function name(s). If specified as a function (or functions) then generic naming is used in the legend.} \item{...}{Currently unused.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{freq}{For histograms, \code{freq=TRUE} (the default) puts count on the y-axis. Setting \code{freq=FALSE} puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the \code{\link[=yaxis_text]{yaxis_text()}} convenience function.)} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{size, alpha}{For the 2D plot only, arguments passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the appearance of scatterplot points.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ The distribution of a (test) statistic \code{T(ypred)}, or a pair of (test) statistics, over the simulations from the posterior or prior predictive distribution. Each of these functions makes the same plot as the corresponding \code{\link[=PPC-test-statistics]{ppc_}} function but without comparing to any observed data \code{y}. The \strong{Plot Descriptions} section at \link{PPC-test-statistics} has details on the individual plots. } \details{ For Binomial data, the plots may be more useful if the input contains the "success" \emph{proportions} (not discrete "success" or "failure" counts). } \examples{ yrep <- example_yrep_draws() ppd_stat(yrep) ppd_stat(yrep, stat = "sd") + legend_none() # use your own function for the 'stat' argument color_scheme_set("brightblue") q25 <- function(y) quantile(y, 0.25) ppd_stat(yrep, stat = "q25") # legend includes function name } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) } \seealso{ Other PPDs: \code{\link{PPD-distributions}}, \code{\link{PPD-intervals}}, \code{\link{PPD-overview}} } \concept{PPDs} bayesplot/man/PPC-errors.Rd0000644000176200001440000001654614556465326015306 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-errors.R \name{PPC-errors} \alias{PPC-errors} \alias{ppc_error_hist} \alias{ppc_error_hist_grouped} \alias{ppc_error_scatter} \alias{ppc_error_scatter_avg} \alias{ppc_error_scatter_avg_grouped} \alias{ppc_error_scatter_avg_vs_x} \alias{ppc_error_binned} \alias{ppc_error_data} \title{PPC errors} \usage{ ppc_error_hist( y, yrep, ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppc_error_hist_grouped( y, yrep, group, ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppc_error_scatter(y, yrep, ..., facet_args = list(), size = 2.5, alpha = 0.8) ppc_error_scatter_avg(y, yrep, ..., size = 2.5, alpha = 0.8) ppc_error_scatter_avg_grouped( y, yrep, group, ..., facet_args = list(), size = 2.5, alpha = 0.8 ) ppc_error_scatter_avg_vs_x(y, yrep, x, ..., size = 2.5, alpha = 0.8) ppc_error_binned( y, yrep, ..., facet_args = list(), bins = NULL, size = 1, alpha = 0.25 ) ppc_error_data(y, yrep, group = NULL) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{...}{Currently unused.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{For \code{ppc_error_binned()}, the number of bins to use (approximately).} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{freq}{For histograms, \code{freq=TRUE} (the default) puts count on the y-axis. Setting \code{freq=FALSE} puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the \code{\link[=yaxis_text]{yaxis_text()}} convenience function.)} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{size, alpha}{For scatterplots, arguments passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the appearance of the points. For the binned error plot, arguments controlling the size of the outline and opacity of the shaded region indicating the 2-SE bounds.} \item{x}{A numeric vector the same length as \code{y} to use as the x-axis variable.} } \value{ A ggplot object that can be further customized using the \strong{ggplot2} package. } \description{ Various plots of predictive errors \code{y - yrep}. See the \strong{Details} and \strong{Plot Descriptions} sections, below. } \details{ All of these functions (aside from the \verb{*_scatter_avg} functions) compute and plot predictive errors for each row of the matrix \code{yrep}, so it is usually a good idea for \code{yrep} to contain only a small number of draws (rows). See \strong{Examples}, below. For binomial and Bernoulli data the \code{ppc_error_binned()} function can be used to generate binned error plots. Bernoulli data can be input as a vector of 0s and 1s, whereas for binomial data \code{y} and \code{yrep} should contain "success" proportions (not counts). See the \strong{Examples} section, below. } \section{Plot descriptions}{ \describe{ \item{\code{ppc_error_hist()}}{ A separate histogram is plotted for the predictive errors computed from \code{y} and each dataset (row) in \code{yrep}. For this plot \code{yrep} should have only a small number of rows. } \item{\code{ppc_error_hist_grouped()}}{ Like \code{ppc_error_hist()}, except errors are computed within levels of a grouping variable. The number of histograms is therefore equal to the product of the number of rows in \code{yrep} and the number of groups (unique values of \code{group}). } \item{\code{ppc_error_scatter()}}{ A separate scatterplot is displayed for \code{y} vs. the predictive errors computed from \code{y} and each dataset (row) in \code{yrep}. For this plot \code{yrep} should have only a small number of rows. } \item{\code{ppc_error_scatter_avg()}}{ A single scatterplot of \code{y} vs. the average of the errors computed from \code{y} and each dataset (row) in \code{yrep}. For each individual data point \code{y[n]} the average error is the average of the errors for \code{y[n]} computed over the the draws from the posterior predictive distribution. } \item{\code{ppc_error_scatter_avg_vs_x()}}{ Same as \code{ppc_error_scatter_avg()}, except the average is plotted on the y-axis and a predictor variable \code{x} is plotted on the x-axis. } \item{\code{ppc_error_binned()}}{ Intended for use with binomial data. A separate binned error plot (similar to \code{arm::binnedplot()}) is generated for each dataset (row) in \code{yrep}. For this plot \code{y} and \code{yrep} should contain proportions rather than counts, and \code{yrep} should have only a small number of rows. } } } \examples{ y <- example_y_data() yrep <- example_yrep_draws() ppc_error_hist(y, yrep[1:3, ]) # errors within groups group <- example_group_data() (p1 <- ppc_error_hist_grouped(y, yrep[1:3, ], group)) p1 + yaxis_text() # defaults to showing counts on y-axis \donttest{ table(group) # more obs in GroupB, can set freq=FALSE to show density on y-axis (p2 <- ppc_error_hist_grouped(y, yrep[1:3, ], group, freq = FALSE)) p2 + yaxis_text() } # scatterplots ppc_error_scatter(y, yrep[10:14, ]) ppc_error_scatter_avg(y, yrep) x <- example_x_data() ppc_error_scatter_avg_vs_x(y, yrep, x) \dontrun{ # binned error plot with binomial model from rstanarm library(rstanarm) example("example_model", package = "rstanarm") formula(example_model) # get observed proportion of "successes" y <- example_model$y # matrix of "success" and "failure" counts trials <- rowSums(y) y_prop <- y[, 1] / trials # proportions # get predicted success proportions yrep <- posterior_predict(example_model) yrep_prop <- sweep(yrep, 2, trials, "/") ppc_error_binned(y_prop, yrep_prop[1:6, ]) } } \references{ Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/MCMC-intervals.Rd0000644000176200001440000002535514404646343016064 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-intervals.R \name{MCMC-intervals} \alias{MCMC-intervals} \alias{mcmc_intervals} \alias{mcmc_areas} \alias{mcmc_areas_ridges} \alias{mcmc_intervals_data} \alias{mcmc_areas_data} \alias{mcmc_areas_ridges_data} \title{Plot interval estimates from MCMC draws} \usage{ mcmc_intervals( x, pars = character(), regex_pars = character(), transformations = list(), ..., prob = 0.5, prob_outer = 0.9, point_est = c("median", "mean", "none"), outer_size = 0.5, inner_size = 2, point_size = 4, rhat = numeric() ) mcmc_areas( x, pars = character(), regex_pars = character(), transformations = list(), ..., area_method = c("equal area", "equal height", "scaled height"), prob = 0.5, prob_outer = 1, point_est = c("median", "mean", "none"), rhat = numeric(), border_size = NULL, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) mcmc_areas_ridges( x, pars = character(), regex_pars = character(), transformations = list(), ..., prob_outer = 1, prob = 1, border_size = NULL, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) mcmc_intervals_data( x, pars = character(), regex_pars = character(), transformations = list(), ..., prob = 0.5, prob_outer = 0.9, point_est = c("median", "mean", "none"), rhat = numeric() ) mcmc_areas_data( x, pars = character(), regex_pars = character(), transformations = list(), ..., prob = 0.5, prob_outer = 1, point_est = c("median", "mean", "none"), rhat = numeric(), bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) mcmc_areas_ridges_data( x, pars = character(), regex_pars = character(), transformations = list(), ..., prob_outer = 1, prob = 1, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{pars}{An optional character vector of parameter names. If neither \code{pars} nor \code{regex_pars} is specified then the default is to use \emph{all} parameters. As of version \verb{1.7.0}, \strong{bayesplot} also supports 'tidy' parameter selection by specifying \code{pars = vars(...)}, where \code{...} is specified the same way as in \link[dplyr:select]{dplyr::select(...)} and similar functions. Examples of using \code{pars} in this way can be found on the \link[=tidy-params]{Tidy parameter selection} page.} \item{regex_pars}{An optional \link[base:grep]{regular expression} to use for parameter selection. Can be specified instead of \code{pars} or in addition to \code{pars}. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored since \link[tidyselect:language]{select helpers} perform a similar function.} \item{transformations}{Optionally, transformations to apply to parameters before plotting. If \code{transformations} is a function or a single string naming a function then that function will be used to transform all parameters. To apply transformations to particular parameters, the \code{transformations} argument can be a named list with length equal to the number of parameters to be transformed. Currently only univariate transformations of scalar parameters can be specified (multivariate transformations will be implemented in a future release). If \code{transformations} is a list, the name of each list element should be a parameter name and the content of each list element should be a function (or any item to match as a function via \code{\link[=match.fun]{match.fun()}}, e.g. a string naming a function). If a function is specified by its name as a string (e.g. \code{"log"}), then it can be used to construct a new parameter label for the appropriate parameter (e.g. \code{"log(sigma)"}). If a function itself is specified (e.g. \code{log} or \code{function(x) log(x)}) then \code{"t"} is used in the new parameter label to indicate that the parameter is transformed (e.g. \code{"t(sigma)"}). Note: due to partial argument matching \code{transformations} can be abbreviated for convenience in interactive use (e.g., \code{transform}).} \item{...}{Currently unused.} \item{prob}{The probability mass to include in the inner interval (for \code{mcmc_intervals()}) or in the shaded region (for \code{mcmc_areas()}). The default is \code{0.5} (50\% interval) and \code{1} for \code{mcmc_areas_ridges()}.} \item{prob_outer}{The probability mass to include in the outer interval. The default is \code{0.9} for \code{mcmc_intervals()} (90\% interval) and \code{1} for \code{mcmc_areas()} and for \code{mcmc_areas_ridges()}.} \item{point_est}{The point estimate to show. Either \code{"median"} (the default), \code{"mean"}, or \code{"none"}.} \item{inner_size, outer_size}{For \code{mcmc_intervals()}, the size of the inner and interval segments, respectively.} \item{point_size}{For \code{mcmc_intervals()}, the size of point estimate.} \item{rhat}{An optional numeric vector of R-hat estimates, with one element per parameter included in \code{x}. If \code{rhat} is provided, the intervals/areas and point estimates in the resulting plot are colored based on R-hat value. See \code{\link[=rhat]{rhat()}} for methods for extracting R-hat estimates.} \item{area_method}{How to constrain the areas in \code{mcmc_areas()}. The default is \code{"equal area"}, setting the density curves to have the same area. With \code{"equal height"}, the curves are scaled so that the highest points across the curves are the same height. The method \code{"scaled height"} tries a compromise between to the two: the heights from \code{"equal height"} are scaled using \code{height*sqrt(height)}} \item{border_size}{For \code{mcmc_areas()} and \code{mcmc_areas_ridges()}, the size of the ridgelines.} \item{bw, adjust, kernel, n_dens}{Optional arguments passed to \code{\link[stats:density]{stats::density()}} to override default kernel density estimation parameters. \code{n_dens} defaults to \code{1024}.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Plot central (quantile-based) posterior interval estimates from MCMC draws. See the \strong{Plot Descriptions} section, below, for details. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_intervals()}}{ Plots of uncertainty intervals computed from posterior draws with all chains merged. } \item{\code{mcmc_areas()}}{ Density plots computed from posterior draws with all chains merged, with uncertainty intervals shown as shaded areas under the curves. } \item{\code{mcmc_areas_ridges()}}{ Density plot, as in \code{mcmc_areas()}, but drawn with overlapping ridgelines. This plot provides a compact display of (hierarchically) related distributions. } } } \examples{ set.seed(9262017) # load ggplot2 to use its functions to modify our plots library(ggplot2) # some parameter draws to use for demonstration x <- example_mcmc_draws(params = 6) dim(x) dimnames(x) color_scheme_set("brightblue") mcmc_intervals(x) mcmc_intervals(x, pars = c("beta[1]", "beta[2]")) mcmc_areas(x, regex_pars = "beta\\\\[[1-3]\\\\]", prob = 0.8) + labs( title = "Posterior distributions", subtitle = "with medians and 80\% intervals" ) color_scheme_set("red") p <- mcmc_areas( x, pars = c("alpha", "beta[4]"), prob = 2/3, prob_outer = 0.9, point_est = "mean", border_size = 1.5 # make the ridgelines fatter ) plot(p) \donttest{ # control spacing at top and bottom of plot # see ?ggplot2::expansion p + scale_y_discrete( limits = c("beta[4]", "alpha"), expand = expansion(add = c(1, 2)) ) p + scale_y_discrete( limits = c("beta[4]", "alpha"), expand = expansion(add = c(.1, .3)) ) # relabel parameters p + scale_y_discrete( labels = c("alpha" = "param label 1", "beta[4]" = "param label 2") ) # relabel parameters and define the order p + scale_y_discrete( labels = c("alpha" = "param label 1", "beta[4]" = "param label 2"), limits = c("beta[4]", "alpha") ) # color by rhat value color_scheme_set("blue") fake_rhat_values <- c(1, 1.07, 1.3, 1.01, 1.15, 1.005) mcmc_intervals(x, rhat = fake_rhat_values) # get the dataframe that is used in the plotting functions mcmc_intervals_data(x) mcmc_intervals_data(x, rhat = fake_rhat_values) mcmc_areas_data(x, pars = "alpha") color_scheme_set("gray") p <- mcmc_areas(x, pars = c("alpha", "beta[4]"), rhat = c(1, 1.1)) p + legend_move("bottom") p + legend_move("none") # or p + legend_none() } # Different area calculations b3 <- c("beta[1]", "beta[2]", "beta[3]") mcmc_areas(x, pars = b3, area_method = "equal area") + labs( title = "Curves have same area", subtitle = "A wide, uncertain interval is spread thin when areas are equal" ) mcmc_areas(x, pars = b3, area_method = "equal height") + labs( title = "Curves have same maximum height", subtitle = "Local curvature is clearer but more uncertain curves use more area" ) mcmc_areas(x, pars = b3, area_method = "scaled height") + labs( title = "Same maximum heights but heights scaled by square-root", subtitle = "Compromise: Local curvature is accentuated and less area is used" ) \donttest{ # apply transformations mcmc_intervals( x, pars = c("beta[2]", "sigma"), transformations = list("sigma" = "log", "beta[2]" = function(x) x + 3) ) # apply same transformation to all selected parameters mcmc_intervals(x, regex_pars = "beta", transformations = "exp") } \dontrun{ # example using fitted model from rstanarm package library(rstanarm) fit <- stan_glm( mpg ~ 0 + wt + factor(cyl), data = mtcars, iter = 500, refresh = 0 ) x <- as.matrix(fit) color_scheme_set("teal") mcmc_intervals(x, point_est = "mean", prob = 0.8, prob_outer = 0.95) mcmc_areas(x, regex_pars = "cyl", bw = "SJ", rhat = rhat(fit, regex_pars = "cyl")) } \dontrun{ # Example of hierarchically related parameters # plotted with ridgelines m <- shinystan::eight_schools@posterior_sample mcmc_areas_ridges(m, pars = "mu", regex_pars = "theta", border_size = 0.75) + ggtitle("Treatment effect on eight schools (Rubin, 1981)") } } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/PPC-censoring.Rd0000644000176200001440000000774314212422623015736 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-censoring.R \name{PPC-censoring} \alias{PPC-censoring} \alias{ppc_km_overlay} \alias{ppc_km_overlay_grouped} \title{PPC censoring} \usage{ ppc_km_overlay(y, yrep, ..., status_y, size = 0.25, alpha = 0.7) ppc_km_overlay_grouped(y, yrep, group, ..., status_y, size = 0.25, alpha = 0.7) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{...}{Currently only used internally.} \item{status_y}{The status indicator for the observations from \code{y}. This must be a numeric vector of the same length as \code{y} with values in \{0, 1\} (0 = right censored, 1 = event).} \item{size, alpha}{Passed to the appropriate geom to control the appearance of the \code{yrep} distributions.} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} } \value{ A ggplot object that can be further customized using the \strong{ggplot2} package. } \description{ Compare the empirical distribution of censored data \code{y} to the distributions of simulated/replicated data \code{yrep} from the posterior predictive distribution. See the \strong{Plot Descriptions} section, below, for details. Although some of the other \pkg{bayesplot} plots can be used with censored data, \code{ppc_km_overlay()} is currently the only plotting function designed \emph{specifically} for censored data. We encourage you to suggest or contribute additional plots at \href{https://github.com/stan-dev/bayesplot}{github.com/stan-dev/bayesplot}. } \section{Plot Descriptions}{ \describe{ \item{\code{ppc_km_overlay()}}{ Empirical CCDF estimates of each dataset (row) in \code{yrep} are overlaid, with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for \code{y} itself on top (and in a darker shade). This is a PPC suitable for right-censored \code{y}. Note that the replicated data from \code{yrep} is assumed to be uncensored. } \item{\code{ppc_km_overlay_grouped()}}{ The same as \code{ppc_km_overlay()}, but with separate facets by \code{group}. } } } \examples{ color_scheme_set("brightblue") y <- example_y_data() # For illustrative purposes, (right-)censor values y > 110: status_y <- as.numeric(y <= 110) y <- pmin(y, 110) # In reality, the replicated data (yrep) would be obtained from a # model which takes the censoring of y properly into account. Here, # for illustrative purposes, we simply use example_yrep_draws(): yrep <- example_yrep_draws() dim(yrep) \donttest{ ppc_km_overlay(y, yrep[1:25, ], status_y = status_y) } # With separate facets by group: group <- example_group_data() \donttest{ ppc_km_overlay_grouped(y, yrep[1:25, ], group = group, status_y = status_y) } } \references{ Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) Kaplan, E. L. and Meier, P. (1958). Nonparametric estimation from incomplete observations. \emph{Journal of the American Statistical Association}. 53(282), 457--481. doi:10.1080/01621459.1958.10501452. } \seealso{ Other PPCs: \code{\link{PPC-discrete}}, \code{\link{PPC-distributions}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/bayesplot-colors.Rd0000644000176200001440000001265214127610043016623 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot-colors.R \name{bayesplot-colors} \alias{bayesplot-colors} \alias{color_scheme_set} \alias{color_scheme_get} \alias{color_scheme_view} \title{Set, get, or view \strong{bayesplot} color schemes} \usage{ color_scheme_set(scheme = "blue") color_scheme_get(scheme = NULL, i = NULL) color_scheme_view(scheme = NULL) } \arguments{ \item{scheme}{For \code{color_scheme_set()}, either a string naming one of the available color schemes or a character vector of \emph{exactly six} colors specifying a custom scheme. For \code{color_scheme_get()}, \code{scheme} can be missing (to get the current color scheme) or a string naming one of the preset schemes. For \code{color_scheme_view()}, \code{scheme} can be missing (to use the current color scheme) or a character vector containing a subset of the available scheme names. See the \strong{Available color schemes} section below for a list of available scheme names. The \strong{Custom color schemes} section describes how to specify a custom scheme.} \item{i}{For \code{color_scheme_get()}, an optional subset of the integers from \code{1} (lightest) to \code{6} (darkest) indicating which of the colors in the scheme to return. If \code{i} is not specified then all six colors in the scheme are included.} } \value{ \code{color_scheme_set()} has the side effect of setting the color scheme used for plotting. It also returns (\link[base:invisible]{invisibly}) a list of the hexadecimal color values used in \code{scheme}. \code{color_scheme_get()} returns a list of the hexadecimal color values (without changing the current scheme). If the \code{scheme} argument is not specified the returned values correspond to the current color scheme. If the optional argument \code{i} is specified then the returned list only contains \code{length(i)} elements. \code{color_scheme_view()} returns a ggplot object if only a single scheme is specified and a gtable object if multiple schemes names are specified. } \description{ Set, get, or view color schemes. Choose from a preset scheme or create a custom scheme. See the \strong{Available color schemes} section below for a list of available scheme names. The \strong{Custom color schemes} section describes how to specify a custom scheme. } \section{Available color schemes}{ Currently, the available preset color schemes are: \itemize{ \item \code{"blue"}, \code{"brightblue"} \item \code{"gray"}, \code{"darkgray"} \item \code{"green"} \item \code{"pink"} \item \code{"purple"} \item \code{"red"} \item \code{"teal"} \item \code{"yellow"} \item \href{https://CRAN.R-project.org/package=viridis}{\code{"viridis"}}, \code{"viridisA"}, \code{"viridisB"}, \code{"viridisC"}, \code{"viridisD"}, \code{"viridisE"} \item \code{"mix-x-y"}, replacing \code{x} and \code{y} with any two of the scheme names listed above (e.g. "mix-teal-pink", "mix-blue-red", etc.). The order of \code{x} and \code{y} matters, i.e., the color schemes \code{"mix-blue-red"} and \code{"mix-red-blue"} are not identical. There is no guarantee that every possible mixed scheme will look good with every possible plot. \item \code{"brewer-x"}, replacing \code{x} with the name of a palette available from \code{\link[RColorBrewer:ColorBrewer]{RColorBrewer::brewer.pal()}} (e.g., \code{brewer-PuBuGn}). } If you have a suggestion for a new color scheme please let us know via the \strong{bayesplot} \href{https://github.com/stan-dev/bayesplot/issues}{issue tracker}. } \section{Custom color schemes}{ A \strong{bayesplot} color scheme consists of six colors. To specify a custom color scheme simply pass a character vector containing either the names of six \link[grDevices:colors]{colors} or six hexadecimal color values (or a mix of names and hex values). The colors should be in order from lightest to darkest. See the end of the \strong{Examples} section for a demonstration. } \examples{ color_scheme_set("blue") color_scheme_view() color_scheme_get() color_scheme_get(i = c(3, 5)) # 3rd and 5th colors only color_scheme_get("brightblue") color_scheme_view("brightblue") # compare multiple schemes color_scheme_view(c("pink", "gray", "teal")) color_scheme_view(c("viridis", "viridisA", "viridisB", "viridisC")) color_scheme_set("pink") x <- example_mcmc_draws() mcmc_intervals(x) color_scheme_set("teal") color_scheme_view() mcmc_intervals(x) color_scheme_set("red") mcmc_areas(x, regex_pars = "beta") color_scheme_set("purple") color_scheme_view() y <- example_y_data() yrep <- example_yrep_draws() ppc_stat(y, yrep, stat = "mean") + legend_none() ############################ ### Mixing color schemes ### ############################ color_scheme_set("mix-teal-pink") ppc_stat(y, yrep, stat = "sd") + legend_none() mcmc_areas(x, regex_pars = "beta") ########################## ### ColorBrewer scheme ### ########################## color_scheme_set("brewer-Spectral") color_scheme_view() mcmc_trace(x, pars = "sigma") ########################### ### Custom color scheme ### ########################### orange_scheme <- c("#ffebcc", "#ffcc80", "#ffad33", "#e68a00", "#995c00", "#663d00") color_scheme_set(orange_scheme) color_scheme_view() mcmc_areas(x, regex_pars = "alpha") mcmc_dens_overlay(x) ppc_stat(y, yrep, stat = "var") + legend_none() } \seealso{ \code{\link[=theme_default]{theme_default()}} for the default ggplot theme used by \strong{bayesplot} and \code{\link[=bayesplot_theme_set]{bayesplot_theme_set()}} to change it. } bayesplot/man/PPC-distributions.Rd0000644000176200001440000003037714563172262016662 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ppc-distributions.R \name{PPC-distributions} \alias{PPC-distributions} \alias{ppc_data} \alias{ppc_dens_overlay} \alias{ppc_dens_overlay_grouped} \alias{ppc_ecdf_overlay} \alias{ppc_ecdf_overlay_grouped} \alias{ppc_dens} \alias{ppc_hist} \alias{ppc_freqpoly} \alias{ppc_freqpoly_grouped} \alias{ppc_boxplot} \alias{ppc_violin_grouped} \alias{ppc_pit_ecdf} \alias{ppc_pit_ecdf_grouped} \title{PPC distributions} \usage{ ppc_data(y, yrep, group = NULL) ppc_dens_overlay( y, yrep, ..., size = 0.25, alpha = 0.7, trim = FALSE, bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024 ) ppc_dens_overlay_grouped( y, yrep, group, ..., size = 0.25, alpha = 0.7, trim = FALSE, bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024 ) ppc_ecdf_overlay( y, yrep, ..., discrete = FALSE, pad = TRUE, size = 0.25, alpha = 0.7 ) ppc_ecdf_overlay_grouped( y, yrep, group, ..., discrete = FALSE, pad = TRUE, size = 0.25, alpha = 0.7 ) ppc_dens(y, yrep, ..., trim = FALSE, size = 0.5, alpha = 1) ppc_hist( y, yrep, ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE ) ppc_freqpoly( y, yrep, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1 ) ppc_freqpoly_grouped( y, yrep, group, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1 ) ppc_boxplot(y, yrep, ..., notch = TRUE, size = 0.5, alpha = 1) ppc_violin_grouped( y, yrep, group, ..., probs = c(0.1, 0.5, 0.9), size = 1, alpha = 1, y_draw = c("violin", "points", "both"), y_size = 1, y_alpha = 1, y_jitter = 0.1 ) ppc_pit_ecdf( y, yrep, ..., pit = NULL, K = NULL, prob = 0.99, plot_diff = FALSE, interpolate_adj = NULL ) ppc_pit_ecdf_grouped( y, yrep, group, ..., K = NULL, pit = NULL, prob = 0.99, plot_diff = FALSE, interpolate_adj = NULL ) } \arguments{ \item{y}{A vector of observations. See \strong{Details}.} \item{yrep}{An \code{S} by \code{N} matrix of draws from the posterior (or prior) predictive distribution. The number of rows, \code{S}, is the size of the posterior (or prior) sample used to generate \code{yrep}. The number of columns, \code{N} is the number of predicted observations (\code{length(y)}). The columns of \code{yrep} should be in the same order as the data points in \code{y} for the plots to make sense. See the \strong{Details} and \strong{Plot Descriptions} sections for additional advice specific to particular plots.} \item{group}{A grouping variable of the same length as \code{y}. Will be coerced to \link[base:factor]{factor} if not already a factor. Each value in \code{group} is interpreted as the group level pertaining to the corresponding observation.} \item{...}{Currently unused.} \item{size, alpha}{Passed to the appropriate geom to control the appearance of the predictive distributions.} \item{trim}{A logical scalar passed to \code{\link[ggplot2:geom_density]{ggplot2::geom_density()}}.} \item{bw, adjust, kernel, n_dens}{Optional arguments passed to \code{\link[stats:density]{stats::density()}} to override default kernel density estimation parameters. \code{n_dens} defaults to \code{1024}.} \item{discrete}{For \code{ppc_ecdf_overlay()}, should the data be treated as discrete? The default is \code{FALSE}, in which case \code{geom="line"} is passed to \code{\link[ggplot2:stat_ecdf]{ggplot2::stat_ecdf()}}. If \code{discrete} is set to \code{TRUE} then \code{geom="step"} is used.} \item{pad}{A logical scalar passed to \code{\link[ggplot2:stat_ecdf]{ggplot2::stat_ecdf()}}.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} \item{freq}{For histograms, \code{freq=TRUE} (the default) puts count on the y-axis. Setting \code{freq=FALSE} puts density on the y-axis. (For many plots the y-axis text is off by default. To view the count or density labels on the y-axis see the \code{\link[=yaxis_text]{yaxis_text()}} convenience function.)} \item{notch}{For the box plot, a logical scalar passed to \code{\link[ggplot2:geom_boxplot]{ggplot2::geom_boxplot()}}. Note: unlike \code{geom_boxplot()}, the default is \code{notch=TRUE}.} \item{probs}{A numeric vector passed to \code{\link[ggplot2:geom_violin]{ggplot2::geom_violin()}}'s \code{draw_quantiles} argument to specify at which quantiles to draw horizontal lines. Set to \code{NULL} to remove the lines.} \item{y_draw}{For \code{ppc_violin_grouped()}, a string specifying how to draw \code{y}: \code{"violin"} (default), \code{"points"} (jittered points), or \code{"both"}.} \item{y_jitter, y_size, y_alpha}{For \code{ppc_violin_grouped()}, if \code{y_draw} is \code{"points"} or \code{"both"} then \code{y_size}, \code{y_alpha}, and \code{y_jitter} are passed to to the \code{size}, \code{alpha}, and \code{width} arguments of \code{\link[ggplot2:geom_jitter]{ggplot2::geom_jitter()}} to control the appearance of \code{y} points. The default of \code{y_jitter=NULL} will let \strong{ggplot2} determine the amount of jitter.} \item{pit}{An optional vector of probability integral transformed values for which the ECDF is to be drawn. If NULL, PIT values are computed to \code{y} with respect to the corresponding values in \code{yrep}.} \item{K}{An optional integer defining the number of equally spaced evaluation points for the PIT-ECDF. Reducing K when using \code{interpolate_adj = FALSE} makes computing the confidence bands faster. For \code{ppc_pit_ecdf} and \code{ppc_pit_ecdf_grouped}, if PIT values are supplied, defaults to \code{length(pit)}, otherwise yrep determines the maximum accuracy of the estimated PIT values and \code{K} is set to \code{min(nrow(yrep) + 1, 1000)}. For \code{mcmc_rank_ecdf}, defaults to the number of iterations per chain in \code{x}.} \item{prob}{The desired simultaneous coverage level of the bands around the ECDF. A value in (0,1).} \item{plot_diff}{A boolean defining whether to plot the difference between the observed PIT- ECDF and the theoretical expectation for uniform PIT values rather than plotting the regular ECDF. The default is \code{FALSE}, but for large samples we recommend setting \code{plot_diff=TRUE} as the difference plot will visually show a more dynamic range.} \item{interpolate_adj}{A boolean defining if the simultaneous confidence bands should be interpolated based on precomputed values rather than computed exactly. Computing the bands may be computationally intensive and the approximation gives a fast method for assessing the ECDF trajectory. The default is to use interpolation if \code{K} is greater than 200.} } \value{ The plotting functions return a ggplot object that can be further customized using the \strong{ggplot2} package. The functions with suffix \verb{_data()} return the data that would have been drawn by the plotting function. } \description{ Compare the empirical distribution of the data \code{y} to the distributions of simulated/replicated data \code{yrep} from the posterior predictive distribution. See the \strong{Plot Descriptions} section, below, for details. } \details{ For Binomial data, the plots may be more useful if the input contains the "success" \emph{proportions} (not discrete "success" or "failure" counts). } \section{Plot Descriptions}{ \describe{ \item{\verb{ppc_hist(), ppc_freqpoly(), ppc_dens(), ppc_boxplot()}}{ A separate histogram, shaded frequency polygon, smoothed kernel density estimate, or box and whiskers plot is displayed for \code{y} and each dataset (row) in \code{yrep}. For these plots \code{yrep} should therefore contain only a small number of rows. See the \strong{Examples} section. } \item{\code{ppc_freqpoly_grouped()}}{ A separate frequency polygon is plotted for each level of a grouping variable for \code{y} and each dataset (row) in \code{yrep}. For this plot \code{yrep} should therefore contain only a small number of rows. See the \strong{Examples} section. } \item{\verb{ppc_ecdf_overlay(), ppc_dens_overlay(), ppc_ecdf_overlay_grouped(), ppc_dens_overlay_grouped()}}{ Kernel density or empirical CDF estimates of each dataset (row) in \code{yrep} are overlaid, with the distribution of \code{y} itself on top (and in a darker shade). When using \code{ppc_ecdf_overlay()} with discrete data, set the \code{discrete} argument to \code{TRUE} for better results. For an example of \code{ppc_dens_overlay()} also see Gabry et al. (2019). } \item{\code{ppc_violin_grouped()}}{ The density estimate of \code{yrep} within each level of a grouping variable is plotted as a violin with horizontal lines at notable quantiles. \code{y} is overlaid on the plot either as a violin, points, or both, depending on the \code{y_draw} argument. } \item{\code{ppc_pit_ecdf()}, \code{ppc_pit_ecdf_grouped()}}{ The PIT-ECDF of the empirical PIT values of \code{y} computed with respect to the corresponding \code{yrep} values. \code{100 * prob}\% central simultaneous confidence intervals are provided to asses if \code{y} and \code{yrep} originate from the same distribution. The PIT values can also be provided directly as \code{pit}. See Säilynoja et al. (2021) for more details.} } } \examples{ color_scheme_set("brightblue") y <- example_y_data() yrep <- example_yrep_draws() group <- example_group_data() dim(yrep) ppc_dens_overlay(y, yrep[1:25, ]) \donttest{ # ppc_ecdf_overlay with continuous data (set discrete=TRUE if discrete data) ppc_ecdf_overlay(y, yrep[sample(nrow(yrep), 25), ]) # PIT-ECDF and PIT-ECDF difference plot of the PIT values of y compared to # yrep with 99\% simultaneous confidence bands. ppc_pit_ecdf(y, yrep, prob = 0.99, plot_diff = FALSE) ppc_pit_ecdf(y, yrep, prob = 0.99, plot_diff = TRUE) } # for ppc_hist,dens,freqpoly,boxplot definitely use a subset yrep rows so # only a few (instead of nrow(yrep)) histograms are plotted ppc_hist(y, yrep[1:8, ]) \donttest{ color_scheme_set("red") ppc_boxplot(y, yrep[1:8, ]) # wizard hat plot color_scheme_set("blue") ppc_dens(y, yrep[200:202, ]) } \donttest{ # frequency polygons ppc_freqpoly(y, yrep[1:3, ], alpha = 0.1, size = 1, binwidth = 5) ppc_freqpoly_grouped(y, yrep[1:3, ], group) + yaxis_text() # if groups are different sizes then the 'freq' argument can be useful ppc_freqpoly_grouped(y, yrep[1:3, ], group, freq = FALSE) + yaxis_text() } # density and distribution overlays by group ppc_dens_overlay_grouped(y, yrep[1:25, ], group = group) ppc_ecdf_overlay_grouped(y, yrep[1:25, ], group = group) \donttest{ # PIT-ECDF plots of the PIT values by group # with 99\% simultaneous confidence bands. ppc_pit_ecdf_grouped(y, yrep, group=group, prob=0.99) } \donttest{ # don't need to only use small number of rows for ppc_violin_grouped # (as it pools yrep draws within groups) color_scheme_set("gray") ppc_violin_grouped(y, yrep, group, size = 1.5) ppc_violin_grouped(y, yrep, group, alpha = 0) # change how y is drawn ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "points", y_size = 1.5) ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "both", y_size = 1.5, y_alpha = 0.5, y_jitter = 0.33 ) } } \references{ Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. \emph{J. R. Stat. Soc. A}, 182: 389-402. doi:10.1111/rssa.12378. (\href{https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378}{journal version}, \href{https://arxiv.org/abs/1709.01449}{arXiv preprint}, \href{https://github.com/jgabry/bayes-vis-paper}{code on GitHub}) Säilynoja, T., Bürkner, P., Vehtari, A. (2021). Graphical Test for Discrete Uniformity and its Applications in Goodness of Fit Evaluation and Multiple Sample Comparison \href{https://arxiv.org/abs/2103.10522}{arXiv preprint}. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). \emph{Bayesian Data Analysis.} Chapman & Hall/CRC Press, London, third edition. (Ch. 6) } \seealso{ Other PPCs: \code{\link{PPC-censoring}}, \code{\link{PPC-discrete}}, \code{\link{PPC-errors}}, \code{\link{PPC-intervals}}, \code{\link{PPC-loo}}, \code{\link{PPC-overview}}, \code{\link{PPC-scatterplots}}, \code{\link{PPC-test-statistics}} } \concept{PPCs} bayesplot/man/MCMC-combos.Rd0000644000176200001440000000643314212422623015321 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-combo.R \name{MCMC-combos} \alias{MCMC-combos} \alias{mcmc_combo} \title{Combination plots} \usage{ mcmc_combo(x, combo = c("dens", "trace"), ..., widths = NULL, gg_theme = NULL) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{combo}{A character vector with at least two elements. Each element of \code{combo} corresponds to a column in the resulting graphic and should be the name of one of the available \link[=MCMC-overview]{MCMC} functions (omitting the \code{mcmc_} prefix).} \item{...}{Arguments passed to the plotting functions named in \code{combo}.} \item{widths}{A numeric vector the same length as \code{combo} specifying relative column widths. For example, if the plot has two columns, then \code{widths = c(2, 1)} will allocate more space for the first column by a factor of 2 (as would \code{widths = c(.3, .15)}, etc.). The default, \code{NULL}, allocates the same horizontal space for each column.} \item{gg_theme}{Unlike most of the other \strong{bayesplot} functions, \code{mcmc_combo} returns a gtable object rather than a ggplot object, and so theme objects can't be added directly to the returned plot object. The \code{gg_theme} argument helps get around this problem by accepting a \strong{ggplot2} \link[ggplot2:theme]{theme} object that is added to each of the plots \emph{before} combining them into the gtable object that is returned. This can be a theme object created by a call to \code{\link[ggplot2:theme]{ggplot2::theme()}} or one of the \strong{bayesplot} convenience functions, e.g. \code{\link[=legend_none]{legend_none()}} (see the \strong{Examples} section, below).} } \value{ A gtable object (the result of calling \code{\link[gridExtra:arrangeGrob]{gridExtra::arrangeGrob()}}) with \code{length(combo)} columns and a row for each parameter. } \description{ Combination plots } \examples{ # some parameter draws to use for demonstration x <- example_mcmc_draws() dim(x) dimnames(x) mcmc_combo(x, pars = c("alpha", "sigma")) mcmc_combo(x, pars = c("alpha", "sigma"), widths = c(1, 2)) \donttest{ # change second plot, show log(sigma) instead of sigma, # and remove the legends color_scheme_set("mix-blue-red") mcmc_combo( x, combo = c("dens_overlay", "trace"), pars = c("alpha", "sigma"), transformations = list(sigma = "log"), gg_theme = legend_none() ) # same thing but this time also change the entire ggplot theme mcmc_combo( x, combo = c("dens_overlay", "trace"), pars = c("alpha", "sigma"), transformations = list(sigma = "log"), gg_theme = ggplot2::theme_gray() + legend_none() ) } } \seealso{ Other MCMC: \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-recover}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/bayesplot-helpers.Rd0000644000176200001440000002612414335264324016773 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot-helpers.R \name{bayesplot-helpers} \alias{bayesplot-helpers} \alias{vline_at} \alias{hline_at} \alias{vline_0} \alias{hline_0} \alias{abline_01} \alias{lbub} \alias{legend_move} \alias{legend_none} \alias{legend_text} \alias{xaxis_title} \alias{xaxis_text} \alias{xaxis_ticks} \alias{yaxis_title} \alias{yaxis_text} \alias{yaxis_ticks} \alias{facet_text} \alias{facet_bg} \alias{panel_bg} \alias{plot_bg} \alias{grid_lines} \alias{overlay_function} \title{Convenience functions for adding or changing plot details} \usage{ vline_at(v, fun, ..., na.rm = TRUE) hline_at(v, fun, ..., na.rm = TRUE) vline_0(..., na.rm = TRUE) hline_0(..., na.rm = TRUE) abline_01(..., na.rm = TRUE) lbub(p, med = TRUE) legend_move(position = "right") legend_none() legend_text(...) xaxis_title(on = TRUE, ...) xaxis_text(on = TRUE, ...) xaxis_ticks(on = TRUE, ...) yaxis_title(on = TRUE, ...) yaxis_text(on = TRUE, ...) yaxis_ticks(on = TRUE, ...) facet_text(on = TRUE, ...) facet_bg(on = TRUE, ...) panel_bg(on = TRUE, ...) plot_bg(on = TRUE, ...) grid_lines(color = "gray50", size = 0.2) overlay_function(...) } \arguments{ \item{v}{Either a numeric vector specifying the value(s) at which to draw the vertical or horizontal line(s), or an object of any type to use as the first argument to \code{fun}.} \item{fun}{A function, or the name of a function, that returns a numeric vector.} \item{...}{For the various \code{vline_}, \code{hline_}, and \code{abline_} functions, \code{...} is passed to \code{\link[ggplot2:geom_abline]{ggplot2::geom_vline()}}, \code{\link[ggplot2:geom_abline]{ggplot2::geom_hline()}}, and \code{\link[ggplot2:geom_abline]{ggplot2::geom_abline()}}, respectively, to control the appearance of the line(s). For functions ending in \verb{_bg}, \code{...} is passed to \code{\link[ggplot2:element]{ggplot2::element_rect()}}. For functions ending in \verb{_text} or \verb{_title}, \code{...} is passed to \code{\link[ggplot2:element]{ggplot2::element_text()}}. For \code{xaxis_ticks} and \code{yaxis_ticks}, \code{...} is passed to \code{\link[ggplot2:element]{ggplot2::element_line()}}. For \code{overlay_function}, \code{...} is passed to \code{\link[ggplot2:geom_function]{ggplot2::stat_function()}}.} \item{na.rm}{A logical scalar passed to the appropriate geom (e.g. \code{\link[ggplot2:geom_abline]{ggplot2::geom_vline()}}). The default is \code{TRUE}.} \item{p}{The probability mass (in \verb{[0,1]}) to include in the interval.} \item{med}{Should the median also be included in addition to the lower and upper bounds of the interval?} \item{position}{The position of the legend. Either a numeric vector (of length 2) giving the relative coordinates (between 0 and 1) for the legend, or a string among \code{"right"}, \code{"left"}, \code{"top"}, \code{"bottom"}. Using \code{position = "none"} is also allowed and is equivalent to using \code{legend_none()}.} \item{on}{For functions modifying ggplot \link[ggplot2:theme]{theme} elements, set \code{on=FALSE} to set the element to \code{\link[ggplot2:element]{ggplot2::element_blank()}}. For example, facet text can be removed by adding \code{facet_text(on=FALSE)}, or simply \code{facet_text(FALSE)} to a ggplot object. If \code{on=TRUE} (the default), then \code{...} can be used to customize the appearance of the theme element.} \item{color, size}{Passed to \code{\link[ggplot2:element]{ggplot2::element_line()}}.} } \value{ A \strong{ggplot2} layer or \code{\link[ggplot2:theme]{ggplot2::theme()}} object that can be added to existing ggplot objects, like those created by many of the \strong{bayesplot} plotting functions. See the \strong{Details} section. } \description{ Convenience functions for adding to (and changing details of) ggplot objects (many of the objects returned by \strong{bayesplot} functions). See the \strong{Examples} section, below. } \details{ \subsection{Add vertical, horizontal, and diagonal lines to plots}{ \itemize{ \item \code{vline_at()} and \code{hline_at()} return an object created by either \code{\link[ggplot2:geom_abline]{ggplot2::geom_vline()}} or \code{\link[ggplot2:geom_abline]{ggplot2::geom_hline()}} that can be added to a ggplot object to draw a vertical or horizontal line (at one or several values). If \code{fun} is missing then the lines are drawn at the values in \code{v}. If \code{fun} is specified then the lines are drawn at the values returned by \code{fun(v)}. \item \code{vline_0()} and \code{hline_0()} are wrappers for \code{vline_at()} and \code{hline_at()} with \code{v = 0} and \code{fun} missing. \item \code{abline_01()} is a wrapper for \code{\link[ggplot2:geom_abline]{ggplot2::geom_abline()}} with the intercept set to \code{0} and the slope set to \code{1}. \item \code{lbub()} returns a \emph{function} that takes a single argument \code{x} and returns the lower and upper bounds (\code{lb}, \code{ub}) of the \code{100*p}\\% central interval of \code{x}, as well as the median (if \code{med=TRUE}). } } \subsection{Control appearance of facet strips}{ \itemize{ \item \code{facet_text()} returns ggplot2 theme objects that can be added to an existing plot (ggplot object) to format the text in facet strips. \item \code{facet_bg()} can be added to a plot to change the background of the facet strips. } } \subsection{Move legend, remove legend, or style the legend text}{ \itemize{ \item \code{legend_move()} and \code{legend_none()} return a ggplot2 theme object that can be added to an existing plot (ggplot object) in order to change the position of the legend or remove it. \item \code{legend_text()} works much like \code{facet_text()} but for the legend. } } \subsection{Control appearance of \eqn{x}-axis and \eqn{y}-axis features}{ \itemize{ \item \code{xaxis_title()} and \code{yaxis_title()} return a ggplot2 theme object that can be added to an existing plot (ggplot object) in order to toggle or format the titles displayed on the \code{x} or \code{y} axis. (To change the titles themselves use \code{\link[ggplot2:labs]{ggplot2::labs()}}.) \item \code{xaxis_text()} and \code{yaxis_text()} return a ggplot2 theme object that can be added to an existing plot (ggplot object) in order to toggle or format the text displayed on the \code{x} or \code{y} axis (e.g. tick labels). \item \code{xaxis_ticks()} and \code{yaxis_ticks()} return a ggplot2 theme object that can be added to an existing plot (ggplot object) to change the appearance of the axis tick marks. } } \subsection{Customize plot background}{ \itemize{ \item \code{plot_bg()} returns a ggplot2 theme object that can be added to an existing plot (ggplot object) to format the background of the \emph{entire} plot. \item \code{panel_bg()} returns a ggplot2 theme object that can be added to an existing plot (ggplot object) to format the background of the just the plotting area. \item \code{grid_lines()} returns a ggplot2 theme object that can be added to an existing plot (ggplot object) to add grid lines to the plot background. } } \subsection{Superimpose a function on an existing plot}{ \itemize{ \item \code{overlay_function()} is a simple wrapper for \code{\link[ggplot2:geom_function]{ggplot2::stat_function()}} but with the \code{inherit.aes} argument fixed to \code{FALSE}. Fixing \code{inherit.aes=FALSE} will avoid potential errors due to the \code{\link[ggplot2:aes]{ggplot2::aes()}}thetic mapping used by certain \strong{bayesplot} plotting functions. } } } \examples{ color_scheme_set("gray") x <- example_mcmc_draws(chains = 1) dim(x) colnames(x) ################################### ### vertical & horizontal lines ### ################################### (p <- mcmc_intervals(x, regex_pars = "beta")) # vertical line at zero (with some optional styling) p + vline_0() p + vline_0(linewidth = 0.25, color = "darkgray", linetype = 2) # vertical line(s) at specified values v <- c(-0.5, 0, 0.5) p + vline_at(v, linetype = 3, linewidth = 0.25) my_lines <- vline_at(v, alpha = 0.25, linewidth = 0.75 * c(1, 2, 1), color = c("maroon", "skyblue", "violet")) p + my_lines \donttest{ # add vertical line(s) at computed values # (three ways of getting lines at column means) color_scheme_set("brightblue") p <- mcmc_intervals(x, regex_pars = "beta") p + vline_at(x[, 3:4], colMeans) p + vline_at(x[, 3:4], "colMeans", color = "darkgray", lty = 2, linewidth = 0.25) p + vline_at(x[, 3:4], function(a) apply(a, 2, mean), color = "orange", linewidth = 2, alpha = 0.1) } # using the lbub function to get interval lower and upper bounds (lb, ub) color_scheme_set("pink") parsed <- ggplot2::label_parsed p2 <- mcmc_hist(x, pars = "beta[1]", binwidth = 1/20, facet_args = list(labeller = parsed)) (p2 <- p2 + facet_text(size = 16)) b1 <- x[, "beta[1]"] p2 + vline_at(b1, fun = lbub(0.8), color = "gray20", linewidth = 2 * c(1,.5,1), alpha = 0.75) p2 + vline_at(b1, lbub(0.8, med = FALSE), color = "gray20", linewidth = 2, alpha = 0.75) ########################## ### format axis titles ### ########################## color_scheme_set("green") y <- example_y_data() yrep <- example_yrep_draws() (p3 <- ppc_stat(y, yrep, stat = "median", binwidth = 1/4)) # turn off the legend, turn on x-axis title p3 + legend_none() + xaxis_title(size = 13, family = "sans") + ggplot2::xlab(expression(italic(T(y)) == median(italic(y)))) ################################ ### format axis & facet text ### ################################ color_scheme_set("gray") p4 <- mcmc_trace(example_mcmc_draws(), pars = c("alpha", "sigma")) myfacets <- facet_bg(fill = "gray30", color = NA) + facet_text(face = "bold", color = "skyblue", size = 14) p4 + myfacets \donttest{ ########################## ### control tick marks ### ########################## p4 + myfacets + yaxis_text(FALSE) + yaxis_ticks(FALSE) + xaxis_ticks(linewidth = 1, color = "skyblue") } ############################## ### change plot background ### ############################## color_scheme_set("blue") # add grid lines ppc_stat(y, yrep) + grid_lines() # panel_bg vs plot_bg ppc_scatter_avg(y, yrep) + panel_bg(fill = "gray90") ppc_scatter_avg(y, yrep) + plot_bg(fill = "gray90") color_scheme_set("yellow") p5 <- ppc_scatter_avg(y, yrep, alpha = 1) p5 + panel_bg(fill = "gray20") + grid_lines(color = "white") \donttest{ color_scheme_set("purple") ppc_dens_overlay(y, yrep[1:30, ]) + legend_text(size = 14) + legend_move(c(0.75, 0.5)) + plot_bg(fill = "gray90") + panel_bg(color = "black", fill = "gray99", linewidth = 3) } ############################################### ### superimpose a function on existing plot ### ############################################### # compare posterior of beta[1] to Gaussian with same posterior mean # and sd as beta[1] x <- example_mcmc_draws(chains = 4) dim(x) purple_gaussian <- overlay_function( fun = dnorm, args = list(mean(x[,, "beta[1]"]), sd(x[,, "beta[1]"])), color = "purple", linewidth = 2 ) color_scheme_set("gray") mcmc_hist(x, pars = "beta[1]", freq = FALSE) + purple_gaussian \donttest{ mcmc_dens(x, pars = "beta[1]") + purple_gaussian } } \seealso{ \code{\link[=theme_default]{theme_default()}} for the default ggplot theme used by \strong{bayesplot}. } bayesplot/man/bayesplot_grid.Rd0000644000176200001440000000543214127610044016330 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/bayesplot_grid.R \name{bayesplot_grid} \alias{bayesplot_grid} \title{Arrange plots in a grid} \usage{ bayesplot_grid( ..., plots = list(), xlim = NULL, ylim = NULL, grid_args = list(), titles = character(), subtitles = character(), legends = TRUE, save_gg_objects = TRUE ) } \arguments{ \item{...}{One or more ggplot objects.} \item{plots}{A list of ggplot objects. Can be used as an alternative to specifying plot objects via \code{...}.} \item{xlim, ylim}{Optionally, numeric vectors of length 2 specifying lower and upper limits for the axes that will be shared across all plots.} \item{grid_args}{An optional named list of arguments to pass to \code{\link[gridExtra:arrangeGrob]{gridExtra::arrangeGrob()}} (\code{nrow}, \code{ncol}, \code{widths}, etc.).} \item{titles, subtitles}{Optional character vectors of plot titles and subtitles. If specified, \code{titles} and \code{subtitles} must must have length equal to the number of plots specified.} \item{legends}{If any of the plots have legends should they be displayed? Defaults to \code{TRUE}.} \item{save_gg_objects}{If \code{TRUE}, the default, then the ggplot objects specified in \code{...} or via the \code{plots} argument are saved in a list in the \code{"bayesplots"} component of the returned object. Setting this to \code{FALSE} will make the returned object smaller but these individual plot objects will not be available.} } \value{ An object of class \code{"bayesplot_grid"} (essentially a gtable object from \code{\link[gridExtra:arrangeGrob]{gridExtra::arrangeGrob()}}), which has a \code{plot} method. } \description{ The \code{bayesplot_grid} function makes it simple to juxtapose plots using common \eqn{x} and/or \eqn{y} axes. } \examples{ y <- example_y_data() yrep <- example_yrep_draws() stats <- c("sd", "median", "max", "min") color_scheme_set("pink") bayesplot_grid( plots = lapply(stats, function(s) ppc_stat(y, yrep, stat = s)), titles = stats, legends = FALSE, grid_args = list(ncol = 1) ) \dontrun{ library(rstanarm) mtcars$log_mpg <- log(mtcars$mpg) fit1 <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0) fit2 <- stan_glm(log_mpg ~ wt, data = mtcars, refresh = 0) y <- mtcars$mpg yrep1 <- posterior_predict(fit1, draws = 50) yrep2 <- posterior_predict(fit2, fun = exp, draws = 50) color_scheme_set("blue") ppc1 <- ppc_dens_overlay(y, yrep1) ppc1 ppc1 + yaxis_text() color_scheme_set("red") ppc2 <- ppc_dens_overlay(y, yrep2) bayesplot_grid(ppc1, ppc2) # make sure the plots use the same limits for the axes bayesplot_grid(ppc1, ppc2, xlim = c(-5, 60), ylim = c(0, 0.2)) # remove the legends and add text bayesplot_grid(ppc1, ppc2, xlim = c(-5, 60), ylim = c(0, 0.2), legends = FALSE, subtitles = rep("Predicted MPG", 2)) } } bayesplot/man/MCMC-recover.Rd0000644000176200001440000001365214556270424015520 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mcmc-recover.R \name{MCMC-recover} \alias{MCMC-recover} \alias{mcmc_recover_intervals} \alias{mcmc_recover_scatter} \alias{mcmc_recover_hist} \title{Compare MCMC estimates to "true" parameter values} \usage{ mcmc_recover_intervals( x, true, batch = rep(1, length(true)), ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, point_est = c("median", "mean", "none"), size = 4, alpha = 1 ) mcmc_recover_scatter( x, true, batch = rep(1, length(true)), ..., facet_args = list(), point_est = c("median", "mean"), size = 3, alpha = 1 ) mcmc_recover_hist( x, true, ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL ) } \arguments{ \item{x}{An object containing MCMC draws: \itemize{ \item A 3-D array, matrix, list of matrices, or data frame. The \link{MCMC-overview} page provides details on how to specify each these. \item A \code{draws} object from the \pkg{\link{posterior}} package (e.g., \code{draws_array}, \code{draws_rvars}, etc.). \item An object with an \code{as.array()} method that returns the same kind of 3-D array described on the \link{MCMC-overview} page. }} \item{true}{A numeric vector of "true" values of the parameters in \code{x}. There should be one value in \code{true} for each parameter included in \code{x} and the order of the parameters in \code{true} should be the same as the order of the parameters in \code{x}.} \item{batch}{Optionally, a vector-like object (numeric, character, integer, factor) used to split the parameters into batches. If \code{batch} is specified, it must have the same length as \code{true} and be in the same order as \code{true}. Parameters in the same batch will be grouped together in the same facet in the plot (see the \strong{Examples} section, below). The default is to group all parameters together into a single batch. Changing the default is most useful when parameters are on very different scales, in which case \code{batch} can be used to group them into batches within which it makes sense to use the same y-axis.} \item{...}{Currently unused.} \item{facet_args}{A named list of arguments (other than \code{facets}) passed to \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}} or \code{\link[ggplot2:facet_grid]{ggplot2::facet_grid()}} to control faceting. Note: if \code{scales} is not included in \code{facet_args} then \strong{bayesplot} may use \code{scales="free"} as the default (depending on the plot) instead of the \strong{ggplot2} default of \code{scales="fixed"}.} \item{prob}{The probability mass to include in the inner interval. The default is \code{0.5} (50\% interval).} \item{prob_outer}{The probability mass to include in the outer interval. The default is \code{0.9} (90\% interval).} \item{point_est}{The point estimate to show. Either \code{"median"} (the default), \code{"mean"}, or \code{"none"}.} \item{size, alpha}{Passed to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}} to control the appearance of plotted points.} \item{binwidth}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{bins}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} to override the default binwidth.} \item{breaks}{Passed to \code{\link[ggplot2:geom_histogram]{ggplot2::geom_histogram()}} as an alternative to \code{binwidth}.} } \value{ A ggplot object that can be further customized using the \strong{ggplot2} package. } \description{ Plots comparing MCMC estimates to "true" parameter values. Before fitting a model to real data it is useful to simulate data according to the model using known (fixed) parameter values and to check that these "true" parameter values are (approximately) recovered by fitting the model to the simulated data. See the \strong{Plot Descriptions} section, below, for details on the available plots. } \section{Plot Descriptions}{ \describe{ \item{\code{mcmc_recover_intervals()}}{ Central intervals and point estimates computed from MCMC draws, with "true" values plotted using a different shape. } \item{\code{mcmc_recover_scatter()}}{ Scatterplot of posterior means (or medians) against "true" values. } \item{\code{mcmc_recover_hist()}}{ Histograms of the draws for each parameter with the "true" value overlaid as a vertical line. } } } \examples{ \dontrun{ library(rstanarm) alpha <- 1; beta <- rnorm(10, 0, 3); sigma <- 2 X <- matrix(rnorm(1000), 100, 10) y <- rnorm(100, mean = c(alpha + X \%*\% beta), sd = sigma) fit <- stan_glm(y ~ ., data = data.frame(y, X), refresh = 0) draws <- as.matrix(fit) print(colnames(draws)) true <- c(alpha, beta, sigma) mcmc_recover_intervals(draws, true) # put the coefficients on X into the same batch mcmc_recover_intervals(draws, true, batch = c(1, rep(2, 10), 1)) # equivalent mcmc_recover_intervals(draws, true, batch = grepl("X", colnames(draws))) # same but facets stacked vertically mcmc_recover_intervals(draws, true, batch = grepl("X", colnames(draws)), facet_args = list(ncol = 1), size = 3) # each parameter in its own facet mcmc_recover_intervals(draws, true, batch = 1:ncol(draws)) # same but in a different order mcmc_recover_intervals(draws, true, batch = c(1, 3, 4, 2, 5:12)) # present as bias by centering with true values mcmc_recover_intervals(sweep(draws, 2, true), rep(0, ncol(draws))) + hline_0() # scatterplot of posterior means vs true values mcmc_recover_scatter(draws, true, point_est = "mean") # histograms of parameter draws with true value added as vertical line color_scheme_set("brightblue") mcmc_recover_hist(draws[, 1:4], true[1:4]) } } \seealso{ Other MCMC: \code{\link{MCMC-combos}}, \code{\link{MCMC-diagnostics}}, \code{\link{MCMC-distributions}}, \code{\link{MCMC-intervals}}, \code{\link{MCMC-nuts}}, \code{\link{MCMC-overview}}, \code{\link{MCMC-parcoord}}, \code{\link{MCMC-scatterplots}}, \code{\link{MCMC-traces}} } \concept{MCMC} bayesplot/man/tidy-params.Rd0000644000176200001440000001610614335264324015562 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tidy-params.R \name{tidy-params} \alias{tidy-params} \alias{param_range} \alias{param_glue} \title{Tidy parameter selection} \usage{ param_range(prefix, range, vars = NULL) param_glue(pattern, ..., vars = NULL) } \arguments{ \item{prefix, range}{For \code{param_range()} only, \code{prefix} is a string naming a parameter and \code{range} is an integer vector providing the indices of a subset of elements to select. For example, using \if{html}{\out{

}}\preformatted{ param_range("beta", c(1,2,8)) }\if{html}{\out{
}} would select parameters named \code{beta[1]}, \code{beta[2]}, and \code{beta[8]}. \code{param_range()} is only designed for the case that the indices are integers surrounded by brackets. If there are no brackets use \link[tidyselect:language]{num_range()}.} \item{vars}{\code{NULL} or a character vector of parameter names to choose from. This is only needed for the atypical use case of calling the function as a standalone function outside of \code{vars()}, \code{select()}, etc. Typically this is left as \code{NULL} and will be set automatically for the user.} \item{pattern, ...}{For \code{param_glue()} only, \code{pattern} is a string containing expressions enclosed in braces and \code{...} should be named arguments providing one character vector per expression in braces in \code{pattern}. It is easiest to describe how to use these arguments with an example: \if{html}{\out{
}}\preformatted{param_glue("beta_\{var\}[\{level\}]", var = c("age", "income"), level = c(3,8)) }\if{html}{\out{
}} would select parameters with names \code{"beta_age[3]"}, \code{"beta_income[3]"}, \code{"beta_age[8]"}, \code{"beta_income[8]"}.} } \description{ Parameter selection in the style of \strong{dplyr} and other tidyverse packages. } \details{ As of version \verb{1.7.0}, \strong{bayesplot} allows the \code{pars} argument for \link[=MCMC-overview]{MCMC plots} to use "tidy" variable selection (in the style of the \strong{dplyr} package). The \code{\link[dplyr:vars]{vars()}} function is re-exported from \strong{dplyr} for this purpose. Features of tidy selection includes direct selection (\code{vars(alpha, sigma)}), everything-but selection (\code{vars(-alpha)}), ranged selection (\code{vars(`beta[1]`:`beta[3]`)}), support for selection functions (\code{vars(starts_with("beta"))}), and combinations of these features. See the \strong{Examples} section, below. When using \code{pars} for tidy parameter selection, the \code{regex_pars} argument is ignored because \strong{bayesplot} supports using \link[tidyselect:language]{tidyselect helper functions} (\code{starts_with()}, \code{contains()}, \code{num_range()}, etc.) for the same purpose. \strong{bayesplot} also exports some additional helper functions to help with parameter selection: \itemize{ \item \code{param_range()}: like \code{\link[tidyselect:starts_with]{num_range()}} but used when parameter indexes are in brackets (e.g. \code{beta[2]}). \item \code{param_glue()}: for more complicated parameter names with multiple indexes (including variable names) inside the brackets (e.g., \verb{beta[(Intercept) age_group:3]}). } These functions can be used inside of \code{vars()}, \code{dplyr::select()}, and similar functions, just like the \link[tidyselect:language]{tidyselect helper functions}. } \section{Extra Advice}{ Parameter names in \code{vars()} are not quoted. When the names contain special characters like brackets, they should be wrapped in backticks, as in \code{vars(`beta[1]`)}. To exclude a range of variables, wrap the sequence in parentheses and then negate it. For example, (\code{vars(-(`beta[1]`:`beta[3]`))}) would exclude \code{beta[1]}, \code{beta[2]}, and \code{beta[3]}. \code{vars()} is a helper function. It holds onto the names and expressions used to select columns. When selecting variables inside a \strong{bayesplot} function, use \code{vars(...)}: \code{mcmc_hist(data, pars = vars(alpha))}. When using \code{select()} to prepare a dataframe for a \strong{bayesplot} function, do not use \code{vars()}: \code{data \%>\% select(alpha) \%>\% mcmc_hist()}. Internally, tidy selection works by converting names and expressions into position numbers. As a result, integers will select parameters; \code{vars(1, 3)} selects the first and third ones. We do not endorse this approach because positions might change as variables are added and removed from models. To select a parameter that happens to be called \code{1}, use backticks to escape it \code{vars(`1`)}. } \examples{ x <- example_mcmc_draws(params = 6) dimnames(x) mcmc_hex(x, pars = vars(alpha, `beta[2]`)) mcmc_dens(x, pars = vars(sigma, contains("beta"))) mcmc_hist(x, pars = vars(-contains("beta"))) # using the param_range() helper mcmc_hist(x, pars = vars(param_range("beta", c(1, 3, 4)))) \donttest{ ############################# ## Examples using rstanarm ## ############################# if (requireNamespace("rstanarm", quietly = TRUE)) { # see ?rstanarm::example_model fit <- example("example_model", package = "rstanarm", local=TRUE)$value print(fit) posterior <- as.data.frame(fit) str(posterior) color_scheme_set("brightblue") mcmc_hist(posterior, pars = vars(size, contains("period"))) # same as previous but using dplyr::select() and piping library("dplyr") posterior \%>\% select(size, contains("period")) \%>\% mcmc_hist() mcmc_intervals(posterior, pars = vars(contains("herd"))) mcmc_intervals(posterior, pars = vars(contains("herd"), -contains("Sigma"))) bayesplot_theme_set(ggplot2::theme_dark()) color_scheme_set("viridisC") mcmc_areas_ridges(posterior, pars = vars(starts_with("b["))) bayesplot_theme_set() color_scheme_set("purple") not_789 <- vars(starts_with("b["), -matches("[7-9]")) mcmc_intervals(posterior, pars = not_789) # using the param_glue() helper just_149 <- vars(param_glue("b[(Intercept) herd:{level}]", level = c(1,4,9))) mcmc_intervals(posterior, pars = just_149) # same but using param_glue() with dplyr::select() # before passing to bayesplot posterior \%>\% select(param_glue("b[(Intercept) herd:{level}]", level = c(1, 4, 9))) \%>\% mcmc_intervals() } } \dontrun{ ################################### ## More examples of param_glue() ## ################################### library(dplyr) posterior <- tibble( b_Intercept = rnorm(1000), sd_condition__Intercept = rexp(1000), sigma = rexp(1000), `r_condition[A,Intercept]` = rnorm(1000), `r_condition[B,Intercept]` = rnorm(1000), `r_condition[C,Intercept]` = rnorm(1000), `r_condition[A,Slope]` = rnorm(1000), `r_condition[B,Slope]` = rnorm(1000) ) posterior # using one expression in braces posterior \%>\% select( param_glue("r_condition[{level},Intercept]", level = c("A", "B")) ) \%>\% mcmc_hist() # using multiple expressions in braces posterior \%>\% select( param_glue( "r_condition[{level},{type}]", level = c("A", "B"), type = c("Intercept", "Slope")) ) \%>\% mcmc_hist() } } \seealso{ \code{\link[glue:glue]{glue::glue()}} } bayesplot/man/example-data.Rd0000644000176200001440000000562714127610044015671 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/example-data.R \name{example-data} \alias{example-data} \alias{example_mcmc_draws} \alias{example_yrep_draws} \alias{example_y_data} \alias{example_x_data} \alias{example_group_data} \title{Example draws to use in demonstrations and tests} \usage{ example_mcmc_draws(chains = 4, params = 4) example_yrep_draws() example_y_data() example_x_data() example_group_data() } \arguments{ \item{chains}{An integer between 1 and 4 indicating the desired number of chains.} \item{params}{An integer between 1 and 6 indicating the desired number of parameters.} } \value{ See \strong{Details}. } \description{ These functions return various objects containing data used in the examples throughout the \strong{bayesplot} package documentation. } \details{ Each of these functions returns an object containing data, parameter draws, or predictions corresponding to a basic linear regression model with data \code{y} (outcome vector) and \code{X} (predictor matrix), and parameters \code{alpha} (intercept), \code{beta} (coefficient vector), and \code{sigma} (error sd). \describe{ \item{\code{example_mcmc_draws()}}{ If \code{chains > 1}, a \code{250} (iterations) by \code{chains} by \code{params} array or, if \code{chains = 1}, a \code{250} by \code{params} matrix of MCMC draws from the posterior distribution of the parameters in the linear regression model described above. If \code{params = 1} then only the draws for \code{alpha} are included in the returned object. If \code{params >= 2} then draws for \code{sigma} are also included. And if \code{params} is between \code{3} and the maximum of \code{6} then draws for regression coefficients \code{beta[k]} (\code{k} in \code{1:(params-2)}) are also included. } \item{\code{example_y_data()}}{ A numeric vector with \code{434} observations of the outcome variable in the linear regression model. } \item{\code{example_x_data()}}{ A numeric vector with \code{434} observations of one of the predictor variables in the linear regression model. } \item{\code{example_group_data()}}{ A factor variable with \code{434} observations of a grouping variable with two levels. } \item{\code{example_yrep_draws()}}{ A \code{500} (draws) by \code{434} (data points) matrix of draws from the posterior predictive distribution. Each row represents a full dataset drawn from the posterior predictive distribution of the outcome \code{y} after fitting the linear regression model mentioned above. } } } \examples{ draws <- example_mcmc_draws() dim(draws) dimnames(draws) draws <- example_mcmc_draws(1, 2) dim(draws) colnames(draws) draws <- example_mcmc_draws(params = 6) dimnames(draws)[[3]] y <- example_y_data() x <- example_x_data() group <- example_group_data() length(y) length(x) length(group) tail(data.frame(y, x, group), 5) yrep <- example_yrep_draws() dim(yrep) # ncol(yrep) = length(y) = length(x) = length(group) } \keyword{internal} bayesplot/DESCRIPTION0000644000176200001440000000446614563320543014001 0ustar liggesusersPackage: bayesplot Type: Package Title: Plotting for Bayesian Models Version: 1.11.1 Date: 2024-02-14 Authors@R: c(person("Jonah", "Gabry", role = c("aut", "cre"), email = "jsg2201@columbia.edu"), person("Tristan", "Mahr", role = "aut"), person("Paul-Christian", "Bürkner", role = "ctb"), person("Martin", "Modrák", role = "ctb"), person("Malcolm", "Barrett", role = "ctb"), person("Frank", "Weber", role = "ctb"), person("Eduardo", "Coronado Sroka", role = "ctb"), person("Teemu", "Sailynoja", role = "ctb"), person("Aki", "Vehtari", role = "ctb")) Maintainer: Jonah Gabry Description: Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) . The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. License: GPL (>= 3) URL: https://mc-stan.org/bayesplot/ BugReports: https://github.com/stan-dev/bayesplot/issues/ SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc Depends: R (>= 3.1.0) Imports: dplyr (>= 0.8.0), ggplot2 (>= 3.4.0), ggridges (>= 0.5.5), glue, posterior, reshape2, rlang (>= 0.3.0), stats, tibble (>= 2.0.0), tidyselect, utils Suggests: ggfortify, gridExtra (>= 2.2.1), hexbin, knitr (>= 1.16), loo (>= 2.0.0), RColorBrewer, rmarkdown (>= 1.0.0), rstan (>= 2.17.1), rstanarm (>= 2.17.4), rstantools (>= 1.5.0), scales, shinystan (>= 2.3.0), survival, testthat (>= 2.0.0), vdiffr (>= 1.0.2) RoxygenNote: 7.3.0 VignetteBuilder: knitr Encoding: UTF-8 NeedsCompilation: no Packaged: 2024-02-14 20:46:03 UTC; jgabry Author: Jonah Gabry [aut, cre], Tristan Mahr [aut], Paul-Christian Bürkner [ctb], Martin Modrák [ctb], Malcolm Barrett [ctb], Frank Weber [ctb], Eduardo Coronado Sroka [ctb], Teemu Sailynoja [ctb], Aki Vehtari [ctb] Repository: CRAN Date/Publication: 2024-02-15 05:30:11 UTC bayesplot/build/0000755000176200001440000000000014563223213013354 5ustar liggesusersbayesplot/build/vignette.rds0000644000176200001440000000046214563223213015715 0ustar liggesusersK0N7ET/>|"$i\\뵻Z:|%wK>wG^fC\^fk:'S/MU*"XosʕJcdD9Y5۝^4)H|ϸPAвԡ҂KfN,`pX/k+z(!`Rj 뱞cMu9}|89_ śSk ^S`4v2 + $ͯm+<:ӥPvg.5}Tula4װ"Vd3N _53hbayesplot/tests/0000755000176200001440000000000014563223213013417 5ustar liggesusersbayesplot/tests/testthat/0000755000176200001440000000000014563320543015263 5ustar liggesusersbayesplot/tests/testthat/test-mcmc-recover.R0000644000176200001440000000726014335264325020754 0ustar liggesuserslibrary(bayesplot) context("MCMC: recover") set.seed(123) draws <- matrix(rnorm(4 * 1000), nrow = 1000) colnames(draws) <- c("alpha", "beta[1]", "beta[2]", "sigma") true <- c(-1, 0, 0.5, 1) test_that("mcmc_recover_intervals throws correct errors", { expect_error( mcmc_recover_intervals(draws, letters[1:ncol(draws)]), "is.numeric(true) is not TRUE", fixed = TRUE ) expect_error( mcmc_recover_intervals(draws, true[-1]), "ncol(x) == length(true) is not TRUE", fixed = TRUE ) expect_error( mcmc_recover_intervals(draws, true, batch = 1:3), "length(batch) == length(true) is not TRUE", fixed = TRUE ) expect_error( mcmc_recover_intervals(draws, true, prob = 0.8, prob_outer = 0.5), "prob_outer >= prob is not TRUE", fixed = TRUE ) expect_error( mcmc_recover_intervals(draws, true, prob = 0, prob_outer = 0.5), "prob > 0 is not TRUE", fixed = TRUE ) expect_error( mcmc_recover_intervals(draws, true, prob = .5, prob_outer = 1.1), "prob_outer <= 1 is not TRUE", fixed = TRUE ) }) test_that("mcmc_recover_intervals returns a ggplot object", { expect_gg(mcmc_recover_intervals(draws, true)) expect_gg(mcmc_recover_intervals(draws, true, batch = c(1, 2, 2, 1), point_est = "mean")) expect_gg(mcmc_recover_intervals(draws, true, batch = grepl("beta", colnames(draws)))) expect_gg(mcmc_recover_intervals(draws, true, batch = grepl("beta", colnames(draws)), facet_args = list(ncol = 1))) }) test_that("mcmc_recover_intervals works when point_est = 'none'", { a <- mcmc_recover_intervals(draws, true, batch = 1:4, point_est = "none") expect_gg(a) expect_equal(a$data$Point, rep(NA, ncol(draws))) }) test_that("mcmc_recover_scatter returns a ggplot object", { expect_gg( mcmc_recover_scatter(draws, true) ) expect_gg( mcmc_recover_scatter( draws, true, batch = 1:4, point_est = "mean", facet_args = list(scales = "fixed") ) ) expect_gg( mcmc_recover_scatter( draws, true, batch = c(1, 2, 2, 1), point_est = "mean" ) ) expect_gg( mcmc_recover_scatter( draws, true, batch = grepl("beta", colnames(draws)) ) ) expect_gg( mcmc_recover_scatter( draws, true, batch = grepl("beta", colnames(draws)), facet_args = list(ncol = 1) ) ) }) test_that("mcmc_recover_hist returns a ggplot object", { expect_gg(mcmc_recover_hist(draws, true, binwidth = 0.1)) expect_gg(mcmc_recover_hist(draws, true, binwidth = 0.1, facet_args = list(nrow = 1))) }) # Visual tests ----------------------------------------------------------------- test_that("mcmc_recover_hist renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_recover_hist(draws, true, binwidth = 0.01) vdiffr::expect_doppelganger("mcmc_recover_hist (default)", p_base) }) test_that("mcmc_recover_intervals renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_recover_intervals(draws, true) vdiffr::expect_doppelganger("mcmc_recover_intervals (default)", p_base) p_custom <- mcmc_recover_intervals(draws, true, prob = 0.6, prob_outer = 0.8) vdiffr::expect_doppelganger("mcmc_recover_intervals (prob)", p_custom) }) test_that("mcmc_recover_scatter renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_recover_scatter(draws, true) vdiffr::expect_doppelganger("mcmc_recover_scatter (default)", p_base) p_custom <- mcmc_recover_scatter(draws, true, size = 6) vdiffr::expect_doppelganger("mcmc_recover_scatter (size)", p_custom) }) bayesplot/tests/testthat/test-ppc-loo.R0000644000176200001440000001722014556270424017742 0ustar liggesuserslibrary(bayesplot) context("PPC: loo") options(useFancyQuotes = FALSE) if (requireNamespace("rstanarm", quietly = TRUE) && requireNamespace("loo", quietly = TRUE)) { suppressPackageStartupMessages(library(rstanarm)) suppressPackageStartupMessages(library(loo)) ITER <- 1000 CHAINS <- 3 fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = ITER, chains = CHAINS, refresh = 0) y <- fit$y yrep <- posterior_predict(fit) suppressWarnings( psis1 <- psis(-log_lik(fit), cores = 2) ) lw <- weights(psis1) suppressWarnings( pits <- rstantools::loo_pit(yrep, y, lw) ) } test_that("ppc_loo_pit gives deprecation warning but still works", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_warning(p1 <- ppc_loo_pit(y, yrep, lw), "deprecated") expect_gg(p1) }) test_that("ppc_loo_pit_overlay returns ggplot object", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_gg(ppc_loo_pit_overlay(y, yrep, lw, samples = 25)) expect_gg(ppc_loo_pit_overlay(y, yrep, psis_object = psis1, samples = 25)) }) test_that("ppc_loo_pit_overlay warns about binary data", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_warning( ppc_loo_pit_overlay(rep(1, length(y)), yrep, lw), "not recommended for binary data" ) }) test_that("ppc_loo_pit_overlay works with boundary_correction=TRUE", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_message(p1 <- ppc_loo_pit_overlay(y, yrep, lw, boundary_correction = TRUE), "continuous observations") expect_gg(p1) }) test_that("ppc_loo_pit_overlay works with boundary_correction=FALSE", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") p1 <- ppc_loo_pit_overlay(y, yrep, lw, boundary_correction = FALSE) expect_gg(p1) }) test_that("ppc_loo_pit_qq returns ggplot object", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_gg(p1 <- ppc_loo_pit_qq(y, yrep, lw)) expect_gg(p2 <- ppc_loo_pit_qq(y, yrep, psis_object = psis1)) expect_equal(p1$labels$x, "Uniform") expect_equal(p1$data, p2$data) expect_gg(p3 <- ppc_loo_pit_qq(y, yrep, lw, compare = "normal")) expect_equal(p3$labels$x, "Normal") }) test_that("ppc_loo_pit functions work when pit specified instead of y,yrep,lw", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_gg(ppc_loo_pit_qq(pit = pits)) expect_message( p1 <- ppc_loo_pit_qq(y = y, yrep = yrep, lw = lw, pit = pits), "'pit' specified so ignoring 'y','yrep','lw' if specified" ) expect_message( p2 <- ppc_loo_pit_qq(pit = pits) ) expect_equal(p1$data, p2$data) expect_gg(p1 <- ppc_loo_pit_overlay(pit = pits)) expect_message( ppc_loo_pit_overlay(y = y, yrep = yrep, lw = lw, pit = pits), "'pit' specified so ignoring 'y','yrep','lw' if specified" ) }) test_that("ppc_loo_intervals returns ggplot object", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_gg(ppc_loo_intervals(y, yrep, psis_object = psis1)) expect_gg(g <- ppc_loo_intervals(y, yrep, psis_object = psis1, order = "median")) expect_s3_class(g$data$x, "factor") expect_equal(nlevels(g$data$x), length(g$data$x)) # subset argument expect_gg(g <- ppc_loo_intervals(y, yrep, psis_object = psis1, subset = 1:25)) expect_equal(nrow(g$data), 25) }) test_that("ppc_loo_ribbon returns ggplot object", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_gg(ppc_loo_ribbon(y, yrep, psis_object = psis1, prob = 0.7, alpha = 0.1)) expect_gg(g <- ppc_loo_ribbon(y, yrep, psis_object = psis1, subset = 1:25)) expect_equal(nrow(g$data), 25) }) test_that("ppc_loo_intervals/ribbon work when 'intervals' specified", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") intervals <- t(apply(yrep, 2, quantile, probs = c(0.1, 0.25, 0.5, 0.75, 0.9))) expect_gg(ppc_loo_intervals(y, intervals = intervals)) expect_gg(ppc_loo_ribbon(y, intervals = intervals)) expect_message(ppc_loo_ribbon(y, intervals = intervals), "'intervals' specified so ignoring 'yrep', 'psis_object', 'subset', if specified") expect_message(ppc_loo_intervals(y, yrep, psis_object = psis1, intervals = intervals), "'intervals' specified so ignoring 'yrep', 'psis_object', 'subset', if specified") }) test_that("ppc_loo_intervals/ribbon work when 'intervals' has 3 columns", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") intervals <- t(apply(yrep, 2, quantile, probs = c(0.1, 0.5, 0.9))) expect_gg(ppc_loo_intervals(y, intervals = intervals)) expect_gg(ppc_loo_ribbon(y, intervals = intervals)) }) test_that("errors if dimensions of yrep and lw don't match", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_error( ppc_loo_pit_overlay(y, yrep, lw[, 1:5]), "identical(dim(yrep), dim(lw)) is not TRUE", fixed = TRUE ) }) test_that("error if subset is bigger than num obs", { skip_if_not_installed("rstanarm") skip_if_not_installed("loo") expect_error(.psis_subset(psis1, 1:1000), "too many elements") expect_error( ppc_loo_intervals(y, yrep, psis_object = psis1, subset = 1:1000), "length(y) >= length(subset) is not TRUE", fixed = TRUE ) }) # Visual tests ------------------------------------------------------------ source(test_path("data-for-ppc-tests.R")) set.seed(123) test_that("ppc_loo_pit_overlay renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") skip_if_not_installed("loo") p_base <- suppressMessages(ppc_loo_pit_overlay(vdiff_loo_y, vdiff_loo_yrep, vdiff_loo_lw)) vdiffr::expect_doppelganger("ppc_loo_pit_overlay (default)", p_base) p_custom <- suppressMessages(ppc_loo_pit_overlay( vdiff_loo_y, vdiff_loo_yrep, vdiff_loo_lw, boundary_correction = FALSE )) vdiffr::expect_doppelganger("ppc_loo_pit_overlay (boundary)", p_custom) }) test_that("ppc_loo_pit_qq renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") skip_if_not_installed("loo") p_base <- ppc_loo_pit_qq(vdiff_loo_y, vdiff_loo_yrep, vdiff_loo_lw) vdiffr::expect_doppelganger("ppc_loo_pit_qq (default)", p_base) }) test_that("ppc_loo_intervals renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") skip_if_not_installed("loo") psis_object <- suppressWarnings(loo::psis(-vdiff_loo_lw)) p_base <- ppc_loo_intervals( vdiff_loo_y, vdiff_loo_yrep, psis_object = psis_object ) vdiffr::expect_doppelganger("ppc_loo_intervals (default)", p_base) p_custom <- ppc_loo_intervals( vdiff_loo_y, vdiff_loo_yrep, psis_object = psis_object, prob = 0.6, prob_outer = 0.7 ) vdiffr::expect_doppelganger("ppc_loo_intervals (prob)", p_custom) p_custom <- ppc_loo_intervals( vdiff_loo_y, vdiff_loo_yrep, psis_object = psis_object, order = "median" ) vdiffr::expect_doppelganger("ppc_loo_intervals (order)", p_custom) }) test_that("ppc_loo_ribbon renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") skip_if_not_installed("loo") psis_object <- suppressWarnings(loo::psis(-vdiff_loo_lw)) p_base <- ppc_loo_ribbon( vdiff_loo_y, vdiff_loo_yrep, psis_object = psis_object ) vdiffr::expect_doppelganger("ppc_loo_ribbon (default)", p_base) p_custom <- ppc_loo_ribbon( vdiff_loo_y, vdiff_loo_yrep, psis_object = psis_object, prob = 0.6, prob_outer = 0.7 ) vdiffr::expect_doppelganger("ppc_loo_ribbon (prob)", p_custom) p_custom <- ppc_loo_ribbon( vdiff_loo_y, vdiff_loo_yrep, psis_object = psis_object, subset = 1:10 ) vdiffr::expect_doppelganger("ppc_loo_ribbon (subset)", p_custom) }) bayesplot/tests/testthat/test-mcmc-diagnostics.R0000644000176200001440000001270114556465326021623 0ustar liggesuserslibrary(bayesplot) context("MCMC: diagnostics") source(test_path("data-for-mcmc-tests.R")) test_that("rhat and neff plots return a ggplot object", { rhat <- runif(100, 1, 1.5) expect_gg(mcmc_rhat(rhat)) expect_gg(mcmc_rhat_hist(rhat, binwidth = .01)) ratio <- runif(100, 0, 1) expect_gg(mcmc_neff(ratio)) expect_gg(mcmc_neff_hist(ratio, binwidth = .01)) # 1-D array ok expect_gg(mcmc_rhat(array(rhat))) expect_gg(mcmc_rhat_hist(array(rhat), binwidth = .01)) expect_gg(mcmc_neff(array(ratio))) expect_gg(mcmc_neff_hist(array(ratio), binwidth = .01)) # named ok rhat <- setNames(runif(5, 1, 1.5), paste0("alpha[", 1:5, "]")) expect_gg(mcmc_rhat(rhat)) # doesn't error with ratios > 1 (not common but can happen) expect_gg(mcmc_neff(ratio = c(0.5, 1, 1.25))) expect_gg(mcmc_neff(ratio = c(0.5, 1, 2))) }) test_that("rhat and neff plot functions throw correct errors & warnings", { # need vector or 1D array expect_error(mcmc_rhat_hist(cbind(1:2)), "is.array") expect_error(mcmc_neff_hist(list(1,2)), "is.numeric") # need positive rhat values expect_error(mcmc_rhat(c(-1, 1, 1)), "must be positive") # need ratios between 0 and 1 expect_error(mcmc_neff(c(-1, 0.5, 0.7)), "must be positive") # drop NAs and warn expect_warning(mcmc_rhat(c(1, 1, NA)), "Dropped 1 NAs") expect_warning(mcmc_neff(c(0.2, NA, 1, NA)), "Dropped 2 NAs") }) test_that("duplicated rhats and neffs are kept (#105)", { # https://github.com/stan-dev/bayesplot/issues/105 rhats <- runif(3, 1, 1.2) rhats <- c(rhats, rhats, rhats) df <- mcmc_rhat_data(rhats) expect_equal(nrow(df), length(rhats)) ratios <- runif(3, 0, 1) ratios <- c(ratios, ratios, ratios) df <- mcmc_neff_data(ratios) expect_equal(nrow(df), length(ratios)) }) test_that("'description' & 'rating' columns are correct (#176)", { # https://github.com/stan-dev/bayesplot/issues/176 rhats <- c(1, 1.07, 1.19, 1.07, 1.3, 1) expected_rhats <- sort(rhats) expected_ratings <- rep(c("low", "ok", "high"), each = 2) expected_descriptions <- rep(c("hat(R) <= 1.05", "hat(R) <= 1.1", "hat(R) > 1.1"), each = 2) df <- mcmc_rhat_data(rhats) expect_equal(df$value, expected_rhats) expect_equal(as.character(df$rating), expected_ratings) expect_equal(df$description, expected_descriptions) ratios <- c(0.4, 0.05, 0.6) expected_ratios <- sort(ratios) expected_ratings <- c("low", "ok", "high") expected_descriptions <- c("N[eff]/N <= 0.1", "N[eff]/N <= 0.5", "N[eff]/N > 0.5") df <- mcmc_neff_data(ratios) expect_equal(df$value, expected_ratios) expect_equal(as.character(df$rating), expected_ratings) expect_equal(df$description, expected_descriptions) }) test_that("mcmc_acf & mcmc_acf_bar return a ggplot object", { expect_gg(mcmc_acf(arr, pars = "beta[1]", regex_pars = "x\\:[2,5]")) expect_gg(mcmc_acf_bar(arr, pars = "beta[1]", regex_pars = "x\\:[2,5]")) expect_gg(mcmc_acf(arr1chain, regex_pars = "beta")) expect_gg(mcmc_acf_bar(arr1chain, regex_pars = "beta")) for (x in c("arr", "mat", "dframe", "dframe_multiple_chains", "arr1", "mat1", "dframe1")) { xx <- get(x) expect_gg(mcmc_acf(xx)) expect_gg(mcmc_acf_bar(xx)) } }) test_that("mcmc_acf & mcmc_acf_bar throw correct errors", { expect_error(mcmc_acf(arr, regex_pars = "beta", lags = 200), regexp = "Too few iterations for lags=200") }) # Visual tests ----------------------------------------------------------------- test_that("mcmc_rhat renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") rhats <- seq(from = 1, to = 1.20, length.out = 10) p_base <- mcmc_rhat(rhats) vdiffr::expect_doppelganger("mcmc_rhat (default)", p_base) p_size <- mcmc_rhat(rhats, size = 3) vdiffr::expect_doppelganger("mcmc_rhat (sized)", p_size) }) test_that("mcmc_rhat_hist renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") rhats <- seq(from = 1, to = 1.20, length.out = 10) p_base <- mcmc_rhat_hist(rhats) vdiffr::expect_doppelganger("mcmc_rhat_hist (default)", p_base) p_binwidth <- mcmc_rhat_hist(rhats, binwidth = .02) vdiffr::expect_doppelganger("mcmc_rhat_hist (binwidth)", p_binwidth) }) test_that("mcmc_neff renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") neffs <- seq(from = 0, to = 1, length.out = 20) p_base <- mcmc_neff(neffs) vdiffr::expect_doppelganger("mcmc_neff (default)", p_base) }) test_that("mcmc_neff_hist renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") neffs <- seq(from = 0, to = 1, length.out = 20) p_base <- mcmc_neff_hist(neffs) vdiffr::expect_doppelganger("mcmc_neff_hist (default)", p_base) p_binwidth <- mcmc_neff_hist(neffs, binwidth = .05) vdiffr::expect_doppelganger("mcmc_neff_hist (binwidth)", p_binwidth) }) test_that("mcmc_acf renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_acf(vdiff_dframe) vdiffr::expect_doppelganger("mcmc_acf (default)", p_base) p_lags <- mcmc_acf(vdiff_dframe, lags = 5) vdiffr::expect_doppelganger("mcmc_acf (lags)", p_lags) }) test_that("mcmc_acf_bar renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_acf_bar(vdiff_dframe) vdiffr::expect_doppelganger("mcmc_acf_bar (default)", p_base) p_lags <- mcmc_acf_bar(vdiff_dframe, lags = 5) vdiffr::expect_doppelganger("mcmc_acf_bar (lags)", p_lags) }) bayesplot/tests/testthat/test-helpers-ppc.R0000644000176200001440000000767414556465326020636 0ustar liggesuserslibrary(bayesplot) context("PPC: misc. functions") source(test_path("data-for-ppc-tests.R")) source(test_path("data-for-mcmc-tests.R")) # melt_predictions --------------------------------------------------------------- expect_molten_yrep <- function(yrep) { y <- rnorm(ncol(yrep)) yrep <- validate_predictions(yrep, length(y)) x <- melt_predictions(yrep) expect_equal(ncol(x), 5) expect_equal(nrow(x), prod(dim(yrep))) rep_nums <- rep(seq_len(nrow(yrep)), length(y)) obs_nums <- sort(rep(seq_len(length(y)), nrow(yrep))) expect_identical(colnames(x), c("y_id", "y_name", "rep_id", "rep_label", "value")) expect_equal(x$y_id, obs_nums) expect_equal(x$rep_id, rep_nums) expect_s3_class(x, "data.frame") expect_s3_class(x$rep_label, "factor") expect_type(x$rep_id, "integer") expect_type(x$y_id, "integer") expect_type(x$value, "double") } test_that("melt_predictions returns correct structure", { expect_molten_yrep(yrep) expect_molten_yrep(yrep2) load(test_path("data-for-binomial.rda")) expect_molten_yrep(Ey) expect_molten_yrep(validate_predictions(yrep, length(y))) }) # melt_and_stack ---------------------------------------------------------- test_that("melt_and_stack returns correct structure", { molten_yrep <- melt_predictions(yrep) d <- melt_and_stack(y, yrep) expect_s3_class(d, "data.frame") expect_equal(nrow(d), nrow(molten_yrep) + length(y)) sorted_names <- sort(c(colnames(molten_yrep), c("is_y", "is_y_label"))) expect_equal(sort(colnames(d)), sorted_names) }) # is_whole_number, all_counts -------------------------------------------- test_that("is_whole_number works correctly", { expect_equal(is_whole_number(c(1L, 2, 3/3, 4/5)), c(rep(TRUE, 3), FALSE)) expect_true(!is_whole_number("1")) }) test_that("all_counts works correctly", { expect_true(all_counts(1)) expect_true(all_counts(0:5)) expect_true(all_counts(matrix(rpois(10, 1), 2, 5))) expect_false(all_counts(rnorm(5))) expect_false(all_counts(c("1", "2"))) expect_false(all_counts(c(1, 1.5))) expect_false(all_counts(c(-1, 2))) }) # adjust_gamma test_that("adjust_gamma works with different adjustment methods", { set.seed(8420) expect_equal( adjust_gamma(N = 100, K = 100, L = 1, prob = .99), adjust_gamma(N = 100, K = 100, L = 1, prob = .99, interpolate_adj = TRUE), tolerance = 1e-3 ) expect_equal( adjust_gamma(N = 100, K = 100, L = 4, prob = .99, M = 1000), adjust_gamma(N = 100, K = 100, L = 4, prob = .99, interpolate_adj = TRUE), tolerance = 1e-3 ) set.seed(NULL) }) # get_interpolation_values ------------------------------------------------ test_that("get_interpolation_values catches impossible values", { expect_error( get_interpolation_values(1000, 1000, 0, .5), "No precomputed values to interpolate from for 'L' = 0." ) expect_error( get_interpolation_values(1000, 1000, 4, 0), "No precomputed values to interpolate from for 'prob' = 0." ) expect_error( get_interpolation_values(1000, 0, 4, .95), "No precomputed values available for interpolation for 'K' = 0." ) expect_error( get_interpolation_values(0, 1000, 4, .95), "No precomputed values to interpolate from for sample length of 0." ) expect_error( get_interpolation_values(1e5, 10, 4, .95), "No precomputed values to interpolate from for sample length of 1e+05", fixed = TRUE ) expect_error( get_interpolation_values(100, 300, 4, .95), "No precomputed values available for interpolation for 'K' = 300" ) }) # ecdf_intervals --------------------------------------------------------- test_that("ecdf_intervals returns right dimensions and values", { lims <- ecdf_intervals(.0001, N = 100, K = 100, L = 1) expect_named(lims, c("lower", "upper")) expect_length(lims$upper, 101) expect_length(lims$lower, 101) expect_equal(min(lims$upper), 0) expect_equal(max(lims$upper), 100) expect_equal(min(lims$lower), 0) expect_equal(max(lims$lower), 100) }) bayesplot/tests/testthat/test-pp_check.R0000644000176200001440000000201714127610044020131 0ustar liggesuserslibrary(bayesplot) context("PPC: pp_check generic and default method") test_that("default pp_check method works", { y <- example_y_data() yrep <- example_yrep_draws() g <- example_group_data() expect_equal( pp_check(y, yrep[1:50, ], ppc_dens_overlay), ppc_dens_overlay(y, yrep[1:50, ]), check.environment = FALSE ) expect_equal( pp_check(y, yrep, fun = "stat_grouped", group = g, stat = "median"), ppc_stat_grouped(y, yrep, group = g, stat = "median"), check.environment = FALSE ) }) test_that("pp_check method can be defined", { pp_check.foo <- function(object, ..., type = c("multiple", "overlaid")) { y <- object[["y"]] yrep <- object[["yrep"]] switch(match.arg(type), multiple = ppc_hist(y, yrep[1:min(8, nrow(yrep)),, drop = FALSE]), overlaid = ppc_dens_overlay(y, yrep) ) } x <- structure( list(y = rnorm(50), yrep = matrix(rnorm(500), ncol = 50)), class = "foo" ) expect_gg(pp_check(x)) expect_gg(pp_check(x, type = "overlaid")) }) bayesplot/tests/testthat/test-mcmc-scatter-and-parcoord.R0000644000176200001440000003560714404651143023324 0ustar liggesuserslibrary(bayesplot) context("MCMC: scatter, hex, and parallel coordinates plots") source(test_path("data-for-mcmc-tests.R")) if (requireNamespace("rstanarm", quietly = TRUE)) { suppressPackageStartupMessages(library(rstanarm)) # also fit an rstanarm model to use with mcmc_pairs fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000, chains = 2, refresh = 0) post <- as.array(fit) lp <- log_posterior(fit) np <- ensure_divergences(nuts_params(fit)) } # mcmc_scatter/hex -------------------------------------------------------- test_that("mcmc_scatter returns a ggplot object", { expect_gg(mcmc_scatter(arr, pars = c("beta[1]", "beta[2]"))) expect_gg(mcmc_scatter(arr1chain, regex_pars = "beta", size = 3, alpha = 0.5)) expect_gg(mcmc_scatter(drawsarr, pars = c("theta[1]", "theta[2]"))) expect_gg(mcmc_scatter(mat, pars = c("sigma", "(Intercept)"))) expect_gg(mcmc_scatter(dframe, regex_pars = "x:[2,4]")) expect_gg(mcmc_scatter(dframe_multiple_chains, pars = c("sigma", "(Intercept)"))) }) test_that("mcmc_scatter throws error if number of parameters is not 2", { expect_error(mcmc_scatter(arr, pars = c("sigma", "beta[1]", "beta[2]")), "exactly 2 parameters") expect_error(mcmc_scatter(arr, pars = "sigma"), "exactly 2 parameters") expect_error(mcmc_scatter(drawsarr, pars = "mu"), "exactly 2 parameters") expect_error(mcmc_scatter(arr1), "exactly 2 parameters") expect_error(mcmc_scatter(drawsarr1), "exactly 2 parameters") expect_error(mcmc_scatter(mat1), "exactly 2 parameters") }) test_that("mcmc_scatter accepts NUTS info", { skip_if_not_installed("rstanarm") expect_gg(mcmc_scatter(post, pars = c("wt", "sigma"), np = np)) div_style <- scatter_style_np(div_color = "orange", div_size = 2, div_shape = 3, div_alpha = 0.5) g <- mcmc_scatter(post, pars = c("wt", "sigma"), np = np, np_style = div_style) expect_gg(g) expect_named(g$data, c("x", "y", "Divergent")) }) test_that("mcmc_hex returns a ggplot object", { skip_if_not_installed("hexbin") expect_gg(mcmc_hex(arr, pars = c("beta[1]", "beta[2]"))) expect_gg(mcmc_hex(arr1chain, regex_pars = "beta", binwidth = c(.5,.5))) expect_gg(mcmc_hex(drawsarr, pars = c("theta[1]", "theta[2]"))) }) test_that("mcmc_hex throws error if number of parameters is not 2", { skip_if_not_installed("hexbin") expect_error(mcmc_hex(arr, pars = c("sigma", "beta[1]", "beta[2]")), "exactly 2 parameters") expect_error(mcmc_hex(arr, pars = "sigma"), "exactly 2 parameters") expect_error(mcmc_hex(drawsarr, pars = "mu"), "exactly 2 parameters") expect_error(mcmc_hex(arr1), "exactly 2 parameters") expect_error(mcmc_hex(mat1), "exactly 2 parameters") }) # mcmc_pairs ------------------------------------------------------------- test_that("mcmc_pairs returns a bayesplot_grid object", { g <- mcmc_pairs(arr, pars = c("(Intercept)", "sigma")) expect_bayesplot_grid(g) expect_equal(print(g), plot(g)) expect_bayesplot_grid(mcmc_pairs(arr, pars = "sigma", regex_pars = "beta")) expect_bayesplot_grid(mcmc_pairs(drawsarr, pars = "mu", regex_pars = "theta")) expect_bayesplot_grid(suppressWarnings(mcmc_pairs(arr1chain, regex_pars = "beta"))) expect_bayesplot_grid(suppressWarnings(mcmc_pairs(drawsarr1chain, regex_pars = "theta"))) expect_bayesplot_grid(suppressWarnings(mcmc_pairs(mat, pars = c("(Intercept)", "sigma")))) expect_bayesplot_grid(suppressWarnings(mcmc_pairs(dframe, pars = c("(Intercept)", "sigma")))) expect_bayesplot_grid(mcmc_pairs(dframe_multiple_chains, regex_pars = "beta")) }) test_that("mcmc_pairs using hexbin works", { skip_if_not_installed("hexbin") expect_bayesplot_grid(mcmc_pairs(arr, regex_pars = "x:[1-3]", transformations = "exp", diag_fun = "dens", off_diag_fun = "hex", diag_args = list(trim = FALSE), off_diag_args = list(binwidth = c(0.5, 0.5)))) }) test_that("no mcmc_pairs non-NUTS 'condition's fail", { expect_bayesplot_grid( mcmc_pairs(arr, pars = "sigma", regex_pars = "beta", condition = pairs_condition(chains = list(1, 2:4))) ) expect_bayesplot_grid( mcmc_pairs(arr, pars = "sigma", regex_pars = "beta", condition = pairs_condition(draws = rep(c(T,F), length.out = prod(dim(arr)[1:2])))) ) expect_bayesplot_grid( mcmc_pairs(arr, pars = "sigma", regex_pars = "beta", condition = pairs_condition(draws = 1/3)) ) expect_bayesplot_grid( mcmc_pairs(arr, pars = "sigma", regex_pars = "beta", condition = pairs_condition(chains = c(1,3))) ) }) test_that("mcmc_pairs works with NUTS info", { skip_if_not_installed("rstanarm") expect_bayesplot_grid(mcmc_pairs(post, pars = c("wt", "am", "sigma"), np = np)) expect_bayesplot_grid(mcmc_pairs(post, pars = c("wt", "am"), condition = pairs_condition(nuts="energy__"), np = np)) expect_bayesplot_grid(mcmc_pairs(post, pars = c("wt", "am"), condition = pairs_condition(nuts="divergent__"), np = np)) expect_bayesplot_grid(mcmc_pairs(post, pars = c("wt", "am"), condition = pairs_condition(nuts = "lp__"), lp=lp, np = np, max_treedepth = 2)) p <- mcmc_pairs( post, pars = c("wt", "am"), off_diag_fun = "scatter", condition = pairs_condition(nuts = "lp__"), lp = lp, np = np, np_style = pairs_style_np(div_color = "firebrick", td_color = "dodgerblue", div_size = 2, td_size = 2), max_treedepth = with(np, max(Value[Parameter == "treedepth__"]) - 1) ) expect_bayesplot_grid(p) }) test_that("mcmc_pairs throws correct warnings and errors", { skip_if_not_installed("rstanarm") expect_warning(mcmc_pairs(arr1chain, regex_pars = "beta"), "This plot is more useful with multiple chains") expect_error(mcmc_pairs(arr, pars = "sigma"), "requires at least two parameters") expect_error( mcmc_pairs(arr, condition = pairs_condition(draws = c(T, F))), "length(condition) == (n_iter * n_chain) is not TRUE", fixed = TRUE ) expect_error( mcmc_pairs(arr, condition = pairs_condition(nuts = "accept_stat__")), "the 'np' argument to 'mcmc_pairs' must also be specified" ) expect_error( mcmc_pairs(arr, condition = pairs_condition(nuts = "lp__")), "the 'lp' argument to 'mcmc_pairs' must also be specified" ) expect_error( mcmc_pairs(arr, condition = "lp__"), 'inherits(condition, "pairs_condition") is not TRUE', fixed = TRUE ) expect_error( mcmc_pairs(post, pars = c("wt", "am"), max_treedepth = 2, np = np, np_style = list(color = "green")), 'inherits(np_style, "nuts_style") is not TRUE', fixed = TRUE ) post2 <- post post2[,1:2,"wt"] <- 0 expect_warning( mcmc_pairs(post2, pars = c("wt", "am", "sigma")), "parameters were dropped because they are constant: wt" ) post[,, "sigma"] <- post[,, "am"] expect_warning( mcmc_pairs(post, pars = c("wt", "sigma", "am")), "parameters were dropped because they are duplicative: am" ) }) # pairs_style_np ------------------------------------------------------- test_that("pairs_style_np returns correct structure", { style <- pairs_style_np(div_size = 3, td_color = "gray", td_shape = 1) expect_s3_class(style, "nuts_style") expect_named(style, c("color", "shape", "size", "alpha"), ignore.order = TRUE) expect_named(style$color, c("div", "td")) expect_named(style$size, c("div", "td")) expect_named(style$shape, c("div", "td")) expect_named(style$alpha, c("div", "td")) }) test_that("pairs_style_np throws correct errors", { expect_error( pairs_style_np(div_size = "3"), "is.numeric(div_size) is not TRUE", fixed = TRUE ) expect_error( pairs_style_np(td_color = 1), "is.character(td_color) is not TRUE", fixed = TRUE ) }) # pairs_condition --------------------------------------------------------- test_that("pairs_condition returns correct structure", { # default cond0 <- pairs_condition() expect_s3_class(cond0, "pairs_condition") expect_equivalent(unclass(cond0), list()) expect_equal(attr(cond0, "type"), "default") # chains cond1 <- pairs_condition(chains = 1:4) expect_s3_class(cond1, "integer") expect_s3_class(cond1, "pairs_condition") expect_equivalent(unclass(cond1), 1:4) expect_equal(attr(cond1, "type"), "chain_vector") cond2 <- pairs_condition(chains = list(1:4, 5:6)) expect_s3_class(cond2, "list") expect_s3_class(cond2, "pairs_condition") expect_equivalent(unclass(cond2), list(upper=1:4, lower=5:6)) expect_equal(attr(cond2, "type"), "chain_list") # draws cond3 <- pairs_condition(draws = 0.7) expect_s3_class(cond3, "numeric") expect_s3_class(cond3, "pairs_condition") expect_equivalent(unclass(cond3), 0.7) expect_equal(attr(cond3, "type"), "draws_proportion") cond4 <- pairs_condition(draws = c(T, F, T)) expect_s3_class(cond4, "logical") expect_s3_class(cond4, "pairs_condition") expect_equivalent(unclass(cond4), c(T, F, T)) expect_equal(attr(cond4, "type"), "draws_selection") # nuts cond5 <- pairs_condition(nuts = "lp__") expect_s3_class(cond5, "character") expect_s3_class(cond5, "pairs_condition") expect_equivalent(unclass(cond5), "lp__") expect_equal(attr(cond5, "type"), "nuts") }) test_that("pairs_condition throws correct errors", { # chain expect_error( pairs_condition(chains = "abc"), "must be an integer vector or a list of two integer vectors" ) expect_error( pairs_condition(chains = list(1:2, 3:4, 5:6)), "length(chains) == 2 is not TRUE", fixed = TRUE ) expect_error( pairs_condition(chains = list(1:2, 2:3)), "Each chain can only be specified once" ) expect_error( pairs_condition(chains = c(1:3, 2)), "Each chain can only be specified once" ) # draws expect_error( pairs_condition(draws = "abc"), "must be a single proportion or a logical vector" ) expect_error( pairs_condition(draws = 2), "draws > 0 && draws < 1 is not TRUE", fixed = TRUE ) # nuts expect_error( pairs_condition(nuts = 2), "must be a single string" ) expect_error( pairs_condition(nuts = c("lp__", "energy__")), "must be a single string" ) expect_error( pairs_condition(nuts = "step_size__"), "stepsize__" ) }) test_that("pairs_condition message if multiple args specified", { options(useFancyQuotes = FALSE) expect_message( pairs_condition(chains = 2, draws = 0.5, nuts = "lp__"), "because they are superseded by 'chains': 'draws', 'nuts'", fixed = TRUE ) expect_message( pairs_condition(chains = 2, nuts = "lp__"), "because they are superseded by 'chains': 'nuts'", fixed = TRUE ) expect_message( pairs_condition(draws = 0.5, nuts = "lp__"), "because they are superseded by 'draws': 'nuts'", fixed = TRUE ) }) # mcmc_parcoord ----------------------------------------------------------- test_that("mcmc_parcoord returns a ggplot object", { expect_gg(mcmc_parcoord(arr, pars = c("(Intercept)", "sigma"))) expect_gg(mcmc_parcoord(arr, pars = "sigma", regex_pars = "beta")) }) test_that("mcmc_parcoord with nuts info returns a ggplot object", { skip_if_not_installed("rstanarm") expect_gg(mcmc_parcoord(post, pars = c("wt", "am", "sigma"), np = np)) }) test_that("mcmc_parcoord throws correct warnings and errors", { skip_if_not_installed("rstanarm") expect_error(mcmc_parcoord(arr, pars = "sigma"), "requires at least two parameters") expect_error( mcmc_parcoord(post, np = np[, -1]), "NUTS parameter data frame must have columns: Chain, Iteration, Parameter, Value", fixed = TRUE ) expect_error( mcmc_parcoord(post, np = np, np_style = list(div_color = "green")), 'inherits(np_style, "nuts_style") is not TRUE', fixed = TRUE ) }) # parcoord_style_np ------------------------------------------------------- test_that("parcoord_style_np returns correct structure", { style <- parcoord_style_np() expect_s3_class(style, "nuts_style") expect_named(style, c("color", "alpha", "size"), ignore.order = TRUE) expect_named(style$color, c("div")) expect_named(style$size, c("div")) expect_named(style$alpha, c("div")) }) test_that("parcoord_style_np throws correct errors", { expect_error( parcoord_style_np(div_size = "3"), "is.numeric(div_size) is not TRUE", fixed = TRUE ) expect_error( parcoord_style_np(td_color = 1), "unused argument (td_color = 1)", fixed = TRUE ) }) # Visual tests ----------------------------------------------------------------- test_that("mcmc_scatter renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_scatter(vdiff_dframe_chains) vdiffr::expect_doppelganger("mcmc_scatter (default)", p_base) p_custom <- mcmc_scatter( vdiff_dframe_chains, size = 2, alpha = 0.2 ) vdiffr::expect_doppelganger("mcmc_scatter (size, alpha)", p_custom) p_divergences <- mcmc_scatter( vdiff_dframe_chains, np = vdiff_dframe_chains_divergences ) vdiffr::expect_doppelganger("mcmc_scatter (np)", p_divergences) }) test_that("mcmc_hex renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") skip_if_not_installed("hexbin") p_base <- mcmc_hex(vdiff_dframe, pars = c("V1", "V2")) vdiffr::expect_doppelganger("mcmc_hex (default)", p_base) p_custom <- mcmc_hex( vdiff_dframe, pars = c("V1", "V2"), binwidth = 0.1 ) vdiffr::expect_doppelganger("mcmc_hex (bw)", p_custom) }) test_that("mcmc_parcoord renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_parcoord(vdiff_dframe_chains) vdiffr::expect_doppelganger("mcmc_parcoord (default)", p_base) p_divergences <- mcmc_parcoord( vdiff_dframe_chains, np = vdiff_dframe_chains_divergences, np_style = parcoord_style_np(div_size = 2) ) vdiffr::expect_doppelganger("mcmc_parcoord (np)", p_divergences) }) test_that("mcmc_pairs renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") skip_if_not_installed("hexbin") p_base <- mcmc_pairs(vdiff_dframe_chains) vdiffr::expect_doppelganger("mcmc_pairs (default)", p_base) p_divergences <- mcmc_pairs( vdiff_dframe_chains, np = vdiff_dframe_chains_divergences, np_style = pairs_style_np(div_size = 2), off_diag_fun = "hex" ) vdiffr::expect_doppelganger("mcmc_pairs (divs, hex)", p_divergences) p_treedepth <- mcmc_pairs( vdiff_dframe_chains, np = vdiff_dframe_chains_treedepth, np_style = pairs_style_np(td_color = "green"), max_treedepth = 9 ) vdiffr::expect_doppelganger("mcmc_pairs (td)", p_treedepth) p_divs_treedepth_divergences <- mcmc_pairs( vdiff_dframe_chains, np = vdiff_dframe_chains_np, np_style = pairs_style_np(div_size = 3, td_color = "green"), max_treedepth = 9 ) vdiffr::expect_doppelganger("mcmc_pairs (divs, td)", p_divs_treedepth_divergences) }) bayesplot/tests/testthat/test-ppc-input-validation.R0000644000176200001440000000531014556465326022444 0ustar liggesuserslibrary(bayesplot) context("PPC: input validation") source(test_path("data-for-ppc-tests.R")) test_that("validate_y works", { expect_identical(validate_y(y), y) expect_identical(validate_y(as.array(y)), y) expect_identical(validate_y(y2), y2) names(y) <- paste0("y", 1:length(y)) expect_identical(validate_y(y), unname(y)) }) test_that("validate_y throws errors", { expect_error(validate_y(yrep), "vector") expect_error(validate_y(as.array(yrep)), "vector or 1D array") expect_error(validate_y(LETTERS), "numeric") expect_error(validate_y(c(y, NA)), "NAs not allowed") }) test_that("validate_predictions works", { expect_identical(validate_predictions(yrep, length(y)), yrep) expect_equal(validate_predictions(yrep2, length(y2)), yrep2) colnames(yrep) <- paste0("yrep", 1:ncol(yrep)) expect_equivalent(validate_predictions(yrep, length(y)), unname(yrep)) }) test_that("validate_predictions throws errors", { expect_error(validate_predictions(as.matrix(LETTERS), length(y)), "numeric") expect_error(validate_predictions(rbind(yrep, NA), length(y)), "NAs not allowed") expect_error(validate_predictions(y, length(y)), "matrix") expect_error(validate_predictions(yrep2, length(y)), "must be equal to") expect_error(validate_predictions(yrep, length(y2)), "must be equal to ") }) test_that("validate_group works", { expect_identical(validate_group(1:3, n_obs = 3), as.factor(1:3)) expect_identical(validate_group(as.numeric(1:3), n_obs = 3), as.factor(1:3)) expect_identical(validate_group(group, n_obs = length(y)), group) expect_identical(validate_group(letters[1:3], n_obs = 3), factor(letters[1:3])) }) test_that("validate_group throws errors", { expect_error(validate_group(array(1:3), n_obs = 3), "vector") expect_error(validate_group(c(1,2,NA), n_obs = 3), "NAs not allowed") expect_error(validate_group(1:4, n_obs = 3), "must be equal to the number of observations") }) test_that("validate_x works", { x <- rnorm(3) expect_identical(validate_x(x, y = 1:3), x) expect_identical(validate_x(array(x), y = rnorm(3)), x) expect_identical(validate_x(y = rnorm(3)), 1:3) names(x) <- letters[1:3] expect_identical(validate_x(x, y = 1:3), unname(x)) }) test_that("validate_x throws errors", { expect_error(validate_x(factor(1:3), y = 1:3), "numeric") expect_error(validate_x(c(1,2,NA), y = 1:3), "NAs not allowed") expect_error(validate_x(1:4, y = 1:3), "must be equal to") }) test_that("validate_pit works", { expect_error(validate_pit("pit"), "is.numeric") expect_error(validate_pit(cbind(1, 2)), "vector") expect_error(validate_pit(-1), "between 0 and 1") expect_error(validate_pit(NA), "NAs not allowed") expect_identical(validate_pit(c(name = 0.5)), 0.5) }) bayesplot/tests/testthat/test-ppc-censoring.R0000644000176200001440000000445214334017766021145 0ustar liggesuserslibrary(bayesplot) context("PPC: censoring") source(test_path("data-for-ppc-tests.R")) test_that("ppc_km_overlay returns a ggplot object", { skip_if_not_installed("ggfortify") expect_gg(ppc_km_overlay(y, yrep, status_y = status_y, size = 0.5, alpha = 0.2)) expect_gg(ppc_km_overlay(y2, yrep2, status_y = status_y2)) }) test_that("ppc_km_overlay_grouped returns a ggplot object", { skip_if_not_installed("ggfortify") expect_gg(ppc_km_overlay_grouped(y, yrep, group, status_y = status_y)) expect_gg(ppc_km_overlay_grouped(y, yrep, as.numeric(group), status_y = status_y)) expect_gg(ppc_km_overlay_grouped(y, yrep, as.integer(group), status_y = status_y)) expect_gg(ppc_km_overlay_grouped(y2, yrep2, group2, status_y = status_y2)) expect_gg(ppc_km_overlay_grouped(y2, yrep2, as.numeric(group2), status_y = status_y2)) expect_gg(ppc_km_overlay_grouped(y2, yrep2, as.integer(group2), status_y = status_y2)) }) # Visual tests ----------------------------------------------------------------- test_that("ppc_km_overlay renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") skip_if_not_installed("ggfortify") p_base <- ppc_km_overlay(vdiff_y2, vdiff_yrep2, status_y = vdiff_status_y2) vdiffr::expect_doppelganger("ppc_km_overlay (default)", p_base) p_custom <- ppc_km_overlay( vdiff_y2, vdiff_yrep2, status_y = vdiff_status_y2, size = 2, alpha = .2) vdiffr::expect_doppelganger("ppc_km_overlay (size, alpha)", p_custom) }) test_that("ppc_km_overlay_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") skip_if_not_installed("ggfortify") p_base <- ppc_km_overlay_grouped(vdiff_y2, vdiff_yrep2, vdiff_group2, status_y = vdiff_status_y2) vdiffr::expect_doppelganger("ppc_km_overlay_grouped (default)", p_base) p_custom <- ppc_km_overlay_grouped( vdiff_y2, vdiff_yrep2, vdiff_group2, status_y = vdiff_status_y2, size = 2, alpha = .2 ) vdiffr::expect_doppelganger( "ppc_km_overlay_grouped (size, alpha)", p_custom ) }) bayesplot/tests/testthat/test-helpers-mcmc.R0000644000176200001440000002703414556465326020763 0ustar liggesuserslibrary(bayesplot) context("MCMC: misc. functions") source(test_path("data-for-mcmc-tests.R")) # melt_mcmc ---------------------------------------------------------------- test_that("melt_mcmc does not convert integer parameter names to integers #162", { mat2 <- mat[, 1:2] colnames(mat2) <- c("1", "2") long_mat <- melt_mcmc(mat2) expect_s3_class(long_mat$Parameter, "factor") arr2 <- arr[, , 1:2] dimnames(arr2)[[3]] <- c("1", "2") long_arr <- melt_mcmc(prepare_mcmc_array(arr2)) expect_s3_class(long_arr$Parameter, "factor") dframe2 <- dframe[, 1:2] colnames(dframe2) <- c("1", "2") long_df <- melt_mcmc(as.matrix(dframe2)) expect_s3_class(long_df$Parameter, "factor") }) # 3-D array helpers -------------------------------------------------------- test_that("is_mcmc_array works", { expect_false(is_mcmc_array(mat)) expect_false(is_mcmc_array(dframe)) expect_false(is_mcmc_array(dframe_multiple_chains)) expect_false(is_mcmc_array(arr)) arr2 <- set_mcmc_dimnames(arr, parnames = dimnames(arr)[[3]]) expect_mcmc_array(arr2) }) test_that("parameter_names works", { x <- example_mcmc_draws() expect_identical(parameter_names(x), dimnames(x)[[3]]) dimnames(x) <- list(a = NULL, b = NULL, c = letters[1:dim(x)[3]]) expect_identical(parameter_names(x), dimnames(x)[[3]]) dimnames(x) <- NULL expect_error(parameter_names(x), "No parameter names found") expect_error(parameter_names(x[, 1, ]), "No parameter names found") }) test_that("has_multiple_chains works", { expect_error(has_multiple_chains(mat), "is_3d_array") expect_error(has_multiple_chains(dframe_multiple_chains), "is_3d_array") expect_error(has_multiple_chains(chainlist), "is_3d_array") expect_true(has_multiple_chains(arr)) arr2 <- set_mcmc_dimnames(arr, parnames = dimnames(arr)[[3]]) expect_true(has_multiple_chains(arr2)) arr1chain2 <- set_mcmc_dimnames(arr1chain, parnames = dimnames(arr1chain)[[3]]) expect_false(has_multiple_chains(arr1chain2)) }) test_that("has_multiple_params works", { expect_error(has_multiple_params(mat), "is_3d_array") expect_error(has_multiple_params(dframe_multiple_chains), "is_3d_array") expect_true(has_multiple_params(arr), "is_3d_array") arr2 <- set_mcmc_dimnames(arr, parnames = dimnames(arr)[[3]]) expect_true(has_multiple_params(arr2)) arr2 <- arr2[, , 3, drop = FALSE] expect_false(has_multiple_params(arr2)) }) # data frame with ‘chain’ variable ---------------------------------------- test_that("is_df_with_chain works", { expect_false(is_df_with_chain(arr)) expect_false(is_df_with_chain(mat)) expect_false(is_df_with_chain(chainlist)) expect_false(is_df_with_chain(dframe)) expect_true(is_df_with_chain(dframe_multiple_chains)) mat2 <- cbind(mat, chain = dframe_multiple_chains$chain) expect_false(is_df_with_chain(mat2)) dframe_multiple_chains2 <- cbind(dframe_multiple_chains, Chain = dframe_multiple_chains$chain) dframe_multiple_chains2$chain <- NULL expect_true(is_df_with_chain(dframe_multiple_chains2)) }) test_that("validate_df_with_chain works", { expect_error(validate_df_with_chain(mat), "is_df_with_chain") dframe_multiple_chains2 <- cbind(dframe_multiple_chains, Chain = dframe_multiple_chains$chain) dframe_multiple_chains2$chain <- NULL expect_identical(validate_df_with_chain(dframe_multiple_chains), dframe_multiple_chains2) dframe_multiple_chains2$Chain <- factor(dframe_multiple_chains2$Chain, labels = letters[1:4]) a <- validate_df_with_chain(dframe_multiple_chains2) expect_type(a$Chain, "integer") # no warning raised when using tibbles (#160) tbl <- tibble::tibble(parameter=rnorm(n=40), Chain=rep(1:4, each=10)) a <- validate_df_with_chain(tbl) expect_type(a$Chain, "integer") }) test_that("df_with_chain2array works", { a <- df_with_chain2array(dframe_multiple_chains) expect_mcmc_array(a) expect_error(df_with_chain2array(dframe), "is_df_with_chain") }) # list of chains ---------------------------------------------------------- test_that("is_chain_list works", { expect_false(is_chain_list(arr)) expect_false(is_chain_list(mat)) expect_false(is_chain_list(dframe)) expect_false(is_chain_list(dframe_multiple_chains)) expect_true(is_chain_list(chainlist)) expect_true(is_chain_list(chainlist1)) expect_true(is_chain_list(chainlist1chain)) }) test_that("validate_chain_list works", { expect_identical(validate_chain_list(chainlist), chainlist) expect_identical(validate_chain_list(chainlist1), chainlist1) expect_identical(validate_chain_list(chainlist1chain), chainlist1chain) chainlist2 <- chainlist colnames(chainlist2[[1]]) <- colnames(chainlist[[1]]) colnames(chainlist2[[1]])[1] <- "AAA" expect_error(validate_chain_list(chainlist2), "parameters for each chain") chainlist3 <- chainlist colnames(chainlist3[[1]]) <- c("", colnames(chainlist[[1]])[-1]) expect_error(validate_chain_list(chainlist3), "Some parameters are missing names") chainlist[[1]] <- chainlist[[1]][-1, ] expect_error(validate_chain_list(chainlist), "Each chain should have the same number of iterations") }) test_that("chain_list2array works", { expect_mcmc_array(chain_list2array(chainlist)) expect_mcmc_array(chain_list2array(chainlist1)) expect_mcmc_array(chain_list2array(chainlist1chain)) }) # transformations --------------------------------------------------------- test_that("validate_transformations throws correct works", { trans <- list(x = "exp", 'beta[1]' = function(x) x^2, sigma = log) expect_silent( validate_transformations(trans, pars = c("x", "beta[1]", "sigma")) ) trans2 <- trans trans2[[1]] <- match.fun(trans[[1]]) expect_equal( validate_transformations(trans, pars = c("x", "beta[1]", "sigma")), trans2 ) }) test_that("validate_transformations throws correct errors", { expect_error( validate_transformations(list("log", exp)), "must be a _named_ list" ) expect_error( validate_transformations(list(x = "log", function(x) x^2)), "Each element of 'transformations' must have a name" ) expect_error( validate_transformations(list(x = "log", 'beta[2]' = exp), pars = c("x", "beta[1]")), regexp = "don't match parameter names: beta[2]", fixed = TRUE ) }) test_that("apply_transformations works", { trans <- list('beta[1]' = "exp", sigma = function(x) x^2) arr_trans <- apply_transformations(arr, transformations = trans) expect_equal(arr_trans[,, "sigma"], arr[,, "sigma"]^2) expect_equal(arr_trans[,, "beta[1]"], exp(arr[,, "beta[1]"])) mat_trans <- apply_transformations(mat, transformations = trans) expect_equal(mat_trans[, "sigma"], mat[, "sigma"]^2) expect_equal(mat_trans[, "beta[1]"], exp(mat[, "beta[1]"])) }) test_that("transformations recycled properly if not a named list", { # if transformations is a single string naming a function x <- prepare_mcmc_array(arr, regex_pars = "beta", transformations = "exp") expect_identical(parameter_names(x), c("exp(beta[1])", "exp(beta[2])")) # if transformations is a single function x <- prepare_mcmc_array(arr, pars = c("beta[1]", "sigma"), transformations = exp) expect_identical(parameter_names(x), c("t(beta[1])", "t(sigma)")) }) # prepare_mcmc_array ------------------------------------------------------ test_that("prepare_mcmc_array errors if NAs", { arr[1,1,1] <- NA expect_error(prepare_mcmc_array(arr), "NAs not allowed") }) test_that("prepare_mcmc_array processes non-array input types correctly", { # errors are mostly covered by tests of the many internal functions above # data frame with no Chain column (treat as 1 chain or merged chains) a1 <- prepare_mcmc_array(dframe) expect_s3_class(a1, "mcmc_array") expect_equal(dim(a1), c(nrow(dframe), 1, ncol(dframe))) expect_equal(parameter_names(a1), colnames(dframe)) # data frame with Chain column a2 <- prepare_mcmc_array(dframe_multiple_chains) expect_s3_class(a2, "mcmc_array") n_chain <- max(dframe_multiple_chains$chain) expect_equal(dim(a2), c(nrow(dframe) / n_chain, n_chain, ncol(dframe))) expect_equal(parameter_names(a2), colnames(dframe)) # list of matrices with multiple chains a3 <- prepare_mcmc_array(chainlist) expect_s3_class(a3, "mcmc_array") expect_equal(dim(a3), c(nrow(chainlist[[1]]), length(chainlist), ncol(chainlist[[1]]))) expect_equal(parameter_names(a3), colnames(chainlist[[1]])) # object with acceptable as.array method if (requireNamespace("rstanarm", quietly = TRUE)) { fit <- suppressWarnings(rstanarm::stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 500, refresh = 0)) a4 <- prepare_mcmc_array(fit) expect_s3_class(a4, "mcmc_array") expect_equal(a4, prepare_mcmc_array(as.array(fit))) expect_equal(dim(a4), c(250, 2, 3)) expect_equal(parameter_names(a4), c("(Intercept)", "wt", "sigma")) } # object with unacceptable as.array method fit2 <- lm(mpg ~ wt, data = mtcars) expect_error(prepare_mcmc_array(fit2), "Arrays should have 2 or 3 dimensions.") }) test_that("prepare_mcmc_array tidy parameter selection is same as traditional selection", { pars_all <- c( "(Intercept)", "beta[1]", "beta[2]", "sigma", "b[(Intercept) XX:1]", "b[(Intercept) XX:2]", "b[(Intercept) XX:3]", "b[(Intercept) ZZ:1]", "b[(Intercept) ZZ:2]", "b[(Intercept) ZZ:3]" ) colnames(mat) <- pars_all # check easier parameters pars_char_1 <- c("(Intercept)", "beta[1]", "beta[2]", "sigma") pars_tidy_1a <- vars(`(Intercept)`, `beta[1]`, `beta[2]`, sigma) pars_tidy_1b <- vars(`(Intercept)`, contains("beta"), sigma) pars_tidy_1c <- vars("(Intercept)", param_range("beta", 1:2), "sigma") expect_identical(prepare_mcmc_array(mat, pars = pars_tidy_1a), prepare_mcmc_array(mat, pars = pars_char_1)) expect_identical(prepare_mcmc_array(mat, pars = pars_tidy_1b), prepare_mcmc_array(mat, pars = pars_char_1)) expect_identical(prepare_mcmc_array(mat, pars = pars_tidy_1c), prepare_mcmc_array(mat, pars = pars_char_1)) # check multilevel parameters pars_char_2 <- c("b[(Intercept) XX:1]", "b[(Intercept) ZZ:1]", "b[(Intercept) XX:3]", "b[(Intercept) ZZ:3]") pars_tidy_2a <- vars(param_glue("b[(Intercept) {var}:{lev}]", var = c("XX", "ZZ"), lev = c(1, 3))) expect_identical(prepare_mcmc_array(mat, pars = pars_tidy_2a), prepare_mcmc_array(mat, pars = pars_char_2)) }) test_that("tidy parameter selection throws correct errors", { expect_error(mcmc_hist(mat, pars = vars(contains("nonsense"))), "No parameters were found matching those names") expect_error(param_range("alpha", 1:3, vars = list("a", "b", "c")), "'vars' must be NULL or a character vector.") expect_error(param_glue("alpha[{lev}]", lev = 1:3, vars = 1:3, "'vars' must be NULL or a character vector.")) }) # rhat and neff helpers --------------------------------------------------- test_that("diagnostic_factor.rhat works", { rhats <- new_rhat(c(low = 0.99, low = 1, low = 1.01, ok = 1.06, ok = 1.09, ok = 1.1, high = 1.2, high = 1.7)) r <- diagnostic_factor(unname(rhats)) expect_equivalent(r, as.factor(names(rhats))) expect_identical(levels(r), c("low", "ok", "high")) }) test_that("diagnostic_factor.neff_ratio works", { ratios <- new_neff_ratio(c(low = 0.05, low = 0.01, ok = 0.2, ok = 0.49, high = 0.51, high = 0.99, high = 1)) r <- diagnostic_factor(unname(ratios)) expect_equivalent(r, as.factor(names(ratios))) expect_identical(levels(r), c("low", "ok", "high")) }) bayesplot/tests/testthat/test-ppc-scatterplots.R0000644000176200001440000000463614556465326021716 0ustar liggesuserslibrary(bayesplot) context("PPC: scatterplots") source(test_path("data-for-ppc-tests.R")) test_that("ppc_scatter returns ggplot object", { expect_gg(ppc_scatter(y, yrep[1,, drop = FALSE])) expect_gg(ppc_scatter(y, yrep[1:3, ])) expect_gg(ppc_scatter(y2, yrep2)) }) test_that("ppc_scatter_avg returns ggplot object", { expect_gg(ppc_scatter_avg(y, yrep)) expect_gg(ppc_scatter_avg(y2, yrep2)) }) test_that("ppc_scatter_avg_data same as ppc_scatter_data if nrow(yrep) = 1", { # really only a few columns are _exactly_ the same cols <- c("y_id", "y_obs", "value") d1 <- ppc_scatter_data(y2, yrep2) d2 <- ppc_scatter_avg_data(y2, yrep2) expect_equal(d1[, cols], d2[, cols]) }) test_that("ppc_scatter_avg_grouped returns a ggplot object", { expect_gg(ppc_scatter_avg_grouped(y, yrep, group)) expect_gg(ppc_scatter_avg_grouped(y, yrep, as.numeric(group))) expect_gg(ppc_scatter_avg_grouped(y, yrep, as.integer(group))) }) # Visual tests ------------------------------------------------------------ test_that("ppc_scatter renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_scatter(vdiff_y, vdiff_yrep[1:6, ]) vdiffr::expect_doppelganger("ppc_scatter (default)", p_base) p_custom <- ppc_scatter( y = vdiff_y, yrep = vdiff_yrep[1:6, ], size = 1, alpha = 1 ) vdiffr::expect_doppelganger( title = "ppc_scatter (size, alpha)", fig = p_custom) }) test_that("ppc_scatter_avg renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_scatter_avg(vdiff_y, vdiff_yrep) vdiffr::expect_doppelganger("ppc_scatter_avg (default)", p_base) p_custom <- ppc_scatter_avg( y = vdiff_y, yrep = vdiff_yrep, size = 1.5, alpha = .1 ) vdiffr::expect_doppelganger( title = "ppc_scatter_avg (size, alpha)", fig = p_custom) }) test_that("ppc_scatter_avg_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_scatter_avg_grouped(vdiff_y, vdiff_yrep, vdiff_group) vdiffr::expect_doppelganger("ppc_scatter_avg_grouped (default)", p_base) p_custom <- ppc_scatter_avg_grouped( y = vdiff_y, yrep = vdiff_yrep, group = vdiff_group, size = 3, alpha = 0.25, ref_line = FALSE ) vdiffr::expect_doppelganger( title = "ppc_scatter_avg_grouped (size, alpha, ref_line)", fig = p_custom) }) bayesplot/tests/testthat/test-mcmc-nuts.R0000644000176200001440000001157214335264325020301 0ustar liggesuserslibrary(bayesplot) context("MCMC: nuts") if (requireNamespace("rstanarm", quietly = TRUE)) { ITER <- 1000 CHAINS <- 3 fit <- rstanarm::stan_glm(mpg ~ wt + am, data = mtcars, iter = ITER, chains = CHAINS, refresh = 0) np <- nuts_params(fit) lp <- log_posterior(fit) } test_that("all mcmc_nuts_* (except energy) return gtable objects", { skip_if_not_installed("rstanarm") expect_gtable(mcmc_nuts_acceptance(np, lp)) expect_gtable(mcmc_nuts_acceptance(np, lp, chain = CHAINS)) expect_gtable(mcmc_nuts_treedepth(np, lp)) expect_gtable(mcmc_nuts_treedepth(np, lp, chain = CHAINS)) expect_gtable(mcmc_nuts_stepsize(np, lp)) expect_gtable(mcmc_nuts_stepsize(np, lp, chain = CHAINS)) np <- ensure_divergences(np) expect_gtable(mcmc_nuts_divergence(np, lp)) expect_gtable(mcmc_nuts_divergence(np, lp, chain = CHAINS)) }) test_that("all mcmc_nuts_* (except energy) error if chain argument is bad", { skip_if_not_installed("rstanarm") funs <- c("acceptance", "divergence", "treedepth", "stepsize") for (f in paste0("mcmc_nuts_", funs)) { expect_error(do.call(f, list(x=np, lp=lp, chain = CHAINS + 1)), regexp = paste("only", CHAINS, "chains found"), info = f) expect_error(do.call(f, list(x=np, lp=lp, chain = 0)), regexp = "chain >= 1", info = f) } }) test_that("mcmc_nuts_energy returns a ggplot object", { skip_if_not_installed("rstanarm") p <- mcmc_nuts_energy(np) expect_gg(p) expect_s3_class(p$facet, "FacetWrap") expect_equal(names(p$facet$params$facets), "Chain") p <- mcmc_nuts_energy(np, merge_chains = TRUE) expect_gg(p) expect_s3_class(p$facet, "FacetNull") }) test_that("mcmc_nuts_energy throws correct warnings", { skip_if_not_installed("rstanarm") expect_warning(mcmc_nuts_energy(np, chain = 1), "ignored: chain") }) test_that("validate_nuts_data_frame throws errors", { skip_if_not_installed("rstanarm") expect_error( validate_nuts_data_frame(list(Iteration = 1, Chain = 1)), "NUTS parameters should be in a data frame" ) expect_error( validate_nuts_data_frame(data.frame(Iteration = 1, apple = 2)), "NUTS parameter data frame must have columns: Chain, Iteration, Parameter, Value" ) expect_error( validate_nuts_data_frame(np, as.matrix(lp)), "lp should be in a data frame" ) lp2 <- lp colnames(lp2)[3] <- "Chains" expect_error( validate_nuts_data_frame(np, lp2), "lp data frame must have columns: Chain, Iteration, Value" ) lp2 <- subset(lp, Chain %in% 1:2) expect_error( validate_nuts_data_frame(np, lp2), "Number of chains" ) }) # Visual tests ----------------------------------------------------------------- source(test_path("data-for-mcmc-tests.R")) test_that("mcmc_nuts_acceptance renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_nuts_acceptance(vdiff_dframe_chains_np, vdiff_dframe_chains_lp) vdiffr::expect_doppelganger("mcmc_nuts_acceptance (default)", p_base) p_chain <- mcmc_nuts_acceptance(vdiff_dframe_chains_np, vdiff_dframe_chains_lp, chain = 1) vdiffr::expect_doppelganger("mcmc_nuts_acceptance (chain)", p_chain) }) test_that("mcmc_nuts_divergence renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_nuts_divergence(vdiff_dframe_chains_np, vdiff_dframe_chains_lp) vdiffr::expect_doppelganger("mcmc_nuts_divergence (default)", p_base) p_chain <- mcmc_nuts_divergence(vdiff_dframe_chains_np, vdiff_dframe_chains_lp, chain = 1) vdiffr::expect_doppelganger("mcmc_nuts_divergence (chain)", p_chain) }) test_that("mcmc_nuts_treedepth renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_nuts_treedepth(vdiff_dframe_chains_np, vdiff_dframe_chains_lp) vdiffr::expect_doppelganger("mcmc_nuts_treedepth (default)", p_base) p_chain <- mcmc_nuts_treedepth(vdiff_dframe_chains_np, vdiff_dframe_chains_lp, chain = 1) vdiffr::expect_doppelganger("mcmc_nuts_treedepth (chain)", p_chain) }) test_that("mcmc_nuts_stepsize renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_nuts_stepsize(vdiff_dframe_chains_np, vdiff_dframe_chains_lp) vdiffr::expect_doppelganger("mcmc_nuts_stepsize (default)", p_base) p_chain <- mcmc_nuts_stepsize(vdiff_dframe_chains_np, vdiff_dframe_chains_lp, chain = 1) vdiffr::expect_doppelganger("mcmc_nuts_stepsize (chain)", p_chain) }) test_that("mcmc_nuts_energy renders correctly", { skip_on_cran() skip_if_not_installed("vdiffr") p_base <- mcmc_nuts_energy(vdiff_dframe_chains_np, vdiff_dframe_chains_lp, binwidth = 10) vdiffr::expect_doppelganger("mcmc_nuts_energy (default)", p_base) p_merged <- mcmc_nuts_energy(vdiff_dframe_chains_np, vdiff_dframe_chains_lp, binwidth = 10, merge_chains = TRUE) vdiffr::expect_doppelganger("mcmc_nuts_energy (merged)", p_merged) }) bayesplot/tests/testthat/test-mcmc-intervals.R0000644000176200001440000002176514556465326021335 0ustar liggesuserslibrary(bayesplot) context("MCMC: intervals") source(test_path("data-for-mcmc-tests.R")) test_that("mcmc_intervals_data computes quantiles", { xs <- melt_mcmc(merge_chains(prepare_mcmc_array(arr, pars = "beta[1]"))) d <- mcmc_intervals_data(arr, pars = "beta[1]", prob = .3, prob_outer = .5) qs <- unlist(d[, c("ll", "l", "m", "h", "hh")]) by_hand <- quantile(xs$Value, c(.25, .35, .5, .65, .75)) expect_equivalent(qs, by_hand) expect_equal(d$parameter, factor("beta[1]")) expect_equal(d$outer_width, .5) expect_equal(d$inner_width, .3) expect_equal(d$point_est, "median") d2 <- mcmc_areas_data(arr, pars = "beta[1]", prob = .3, prob_outer = .5) sets <- split(d2, d2$interval) expect_equal(range(sets$inner$x), c(d$l, d$h)) expect_equal(range(sets$outer$x), c(d$ll, d$hh)) }) test_that("mcmc_intervals_data computes point estimates", { xs <- melt_mcmc(merge_chains(prepare_mcmc_array(arr, pars = "beta[2]"))) d <- mcmc_intervals_data(arr, pars = "beta[2]", prob = .3, prob_outer = .5, point_est = "mean") expect_equivalent(d$m, mean(xs$Value)) expect_equal(d$parameter, factor("beta[2]")) expect_equal(d$point_est, "mean") d <- mcmc_intervals_data(arr, pars = "(Intercept)", prob = .3, prob_outer = .5, point_est = "none") expect_true(!("m" %in% names(d))) expect_equal(d$point_est, "none") }) test_that("mcmc_intervals returns a ggplot object", { expect_gg(mcmc_intervals(arr, pars = "beta[1]", regex_pars = "x\\:")) expect_gg(mcmc_intervals(arr1chain, pars = "beta[1]", regex_pars = "Intercept")) expect_gg(mcmc_intervals(mat, regex_pars = "beta")) expect_gg(mcmc_intervals(dframe)) expect_gg(mcmc_intervals(dframe_multiple_chains)) expect_gg(mcmc_intervals(arr1)) expect_gg(mcmc_intervals(mat1)) expect_gg(mcmc_intervals(dframe1)) }) test_that("mcmc_areas returns a ggplot object", { expect_gg(mcmc_areas(arr, pars = "beta[2]", regex_pars = "x\\:")) expect_gg(mcmc_areas(arr1chain, regex_pars = c("beta", "x\\:"))) expect_gg(mcmc_areas(mat)) expect_gg(mcmc_areas(dframe)) expect_gg(mcmc_areas(dframe_multiple_chains)) expect_gg(mcmc_areas(arr1)) expect_gg(mcmc_areas(mat1)) expect_gg(mcmc_areas(dframe1)) }) test_that("mcmc_areas_ridges returns a ggplot object", { expect_gg(mcmc_areas_ridges(arr, pars = "beta[2]", regex_pars = "x\\:")) expect_gg(mcmc_areas_ridges(arr1chain, regex_pars = c("beta", "x\\:"))) expect_gg(mcmc_areas_ridges(mat)) expect_gg(mcmc_areas_ridges(dframe)) expect_gg(mcmc_areas_ridges(dframe_multiple_chains)) expect_gg(mcmc_areas_ridges(arr1)) expect_gg(mcmc_areas_ridges(mat1)) expect_gg(mcmc_areas_ridges(dframe1)) }) test_that("mcmc_intervals/areas with rhat", { r <- runif(ncol(mat), 0.9, 1.3) rbad <- c(NA, r[-1]) expect_error(mcmc_intervals(arr, rhat = r[-1]), "'rhat' has length") expect_error(expect_warning(mcmc_intervals(arr, rhat = rbad))) expect_gg(g <- mcmc_intervals(arr, rhat = r)) # if (utils::packageVersion("ggplot2") >= "3.0.0") { # rhat_map <- g$layers[[3]][["mapping"]] # expect_identical(rlang::as_name(rhat_map[["colour"]]), "rhat_rating") # } # areas with rhat. # layer 1 is maybe line vertical line. [skip] # layer 2 is inner interval. expect_gg(g2 <- mcmc_areas(arr, rhat = r)) if (utils::packageVersion("ggplot2") >= "3.0.0") { rhat_map2 <- g2$layers[[2]][["mapping"]] # expect_identical(rlang::as_name(rhat_map2$fill), "rhat_rating") # expect_identical(rlang::as_name(rhat_map2$colour), "rhat_rating") # layer 3 is point estimate. manually colored. [skip] # layer 4 is outer interval. rhat_map4 <- g2$layers[[4]][["mapping"]] # expect_identical(rlang::as_name(rhat_map4$colour), "rhat_rating") # layer 5 is bottom line. rhat_map5 <- g2$layers[[5]][["mapping"]] # expect_identical(rlang::as_name(rhat_map5$colour), "rhat_rating") } }) test_that("mcmc_areas_data computes density", { areas_data <- mcmc_areas_data(arr, point_est = "none") areas_data <- areas_data[areas_data$interval_width == 1, ] by_parameter <- split(areas_data, areas_data$parameter) # Manually compute the same raw_values <- melt_mcmc(merge_chains(prepare_mcmc_array(arr))) raw_values <- split(raw_values, interaction(raw_values$Parameter)) do_dens <- function(df, interval_width, n) { x <- df$Value tail_width <- (1 - interval_width) / 2 qs <- quantile(x, probs = c(tail_width, 1 - tail_width)) dens <- density(x = x, from = min(qs), to = max(qs), n = n) data.frame(Parameter = unique(df$Parameter), x = dens$x, y = dens$y) } densities <- lapply(raw_values, do_dens, 1, 1024) for (name in names(by_parameter)) { expect_equivalent(by_parameter[[name]][["density"]], densities[[name]][["y"]]) } }) test_that("compute_column_density can use density options (#118)", { # n_dens affects the number of rows in the return data-frame areas_data <- mcmc_areas_data(arr, point_est = "none", n_dens = 100) pars <- length(unique(areas_data$parameter)) intervals <- length(unique(areas_data$interval)) expect_equal(nrow(areas_data), 100 * intervals * pars) # If these raise errors, they are being evaluated expect_error(mcmc_areas_data(arr, bw = stop())) expect_error(mcmc_areas_data(arr, adjust = stop())) expect_error(mcmc_areas_data(arr, kernel = stop())) }) test_that("probabilities outside of [0,1] cause an error", { expect_error(mcmc_intervals_data(arr, prob = -0.1), "must be in \\[0,1\\]") expect_error(mcmc_intervals_data(arr, prob = 1.1), "must be in \\[0,1\\]") expect_error(mcmc_intervals_data(arr, prob_outer = -0.1), "must be in \\[0,1\\]") expect_error(mcmc_intervals_data(arr, prob_outer = 1.1), "must be in \\[0,1\\]") }) test_that("inconsistent probabilities raise warning (#138)", { expect_warning( mcmc_intervals_data(arr, prob = .9, prob_outer = .8), "`prob_outer` .* is less than `prob`" ) }) # Visual tests ----------------------------------------------------------------- test_that("mcmc_intervals renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_intervals(vdiff_dframe) vdiffr::expect_doppelganger("mcmc_intervals (default)", p_base) p_outer <- mcmc_intervals(vdiff_dframe, prob_outer = .8) vdiffr::expect_doppelganger("mcmc_intervals (outer)", p_outer) p_inner <- mcmc_intervals(vdiff_dframe, prob = .1) vdiffr::expect_doppelganger("mcmc_intervals (inner)", p_inner) rhats <- seq(from = 1, to = 1.15, length.out = 5) p_rhats <- mcmc_intervals(vdiff_dframe, rhat = rhats) vdiffr::expect_doppelganger("mcmc_intervals (rhats)", p_rhats) p_no_points <- mcmc_intervals(vdiff_dframe, point_est = "none") vdiffr::expect_doppelganger("mcmc_intervals (no points)", p_no_points) p_mean_points <- mcmc_intervals(vdiff_dframe, point_est = "mean") vdiffr::expect_doppelganger("mcmc_intervals (means)", p_mean_points) p_sizes <- mcmc_intervals(vdiff_dframe, point_size = 1, inner_size = 5, outer_size = 4) vdiffr::expect_doppelganger("mcmc_intervals (sizes)", p_sizes) }) test_that("mcmc_areas renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_areas(vdiff_dframe) vdiffr::expect_doppelganger("mcmc_areas (default)", p_base) p_equal_height <- mcmc_areas(vdiff_dframe, area_method = "equal height") vdiffr::expect_doppelganger("mcmc_areas (equal height)", p_equal_height) p_scaled_height <- mcmc_areas(vdiff_dframe, area_method = "scaled height") vdiffr::expect_doppelganger("mcmc_areas (scaled height)", p_scaled_height) p_outer <- mcmc_areas(vdiff_dframe, prob_outer = .8) vdiffr::expect_doppelganger("mcmc_areas (outer)", p_outer) p_inner <- mcmc_areas(vdiff_dframe, prob = .1) vdiffr::expect_doppelganger("mcmc_areas (inner)", p_inner) rhats <- seq(from = 1, to = 1.15, length.out = 5) p_rhats <- mcmc_areas(vdiff_dframe, rhat = rhats) vdiffr::expect_doppelganger("mcmc_areas (rhats)", p_rhats) p_no_points <- mcmc_areas(vdiff_dframe, point_est = "none") vdiffr::expect_doppelganger("mcmc_areas (no points)", p_no_points) p_mean_points <- mcmc_areas(vdiff_dframe, point_est = "mean") vdiffr::expect_doppelganger("mcmc_areas (means)", p_mean_points) p_size <- mcmc_areas(vdiff_dframe, border_size = 2) vdiffr::expect_doppelganger("mcmc_areas (size)", p_size) }) test_that("mcmc_areas_ridges renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_areas_ridges(vdiff_dframe) vdiffr::expect_doppelganger("mcmc_areas_ridges (default)", p_base) p_inner <- mcmc_areas_ridges(vdiff_dframe, prob = .5) vdiffr::expect_doppelganger("mcmc_areas_ridges (inner)", p_inner) p_outer <- mcmc_areas_ridges(vdiff_dframe, prob = .5, prob_outer = .95) vdiffr::expect_doppelganger("mcmc_areas_ridges (outer)", p_outer) p_size <- mcmc_areas_ridges(vdiff_dframe, border_size = 2) vdiffr::expect_doppelganger("mcmc_areas_ridges (size)", p_size) }) bayesplot/tests/testthat/test-mcmc-distributions.R0000644000176200001440000001511414556465326022217 0ustar liggesuserslibrary(bayesplot) context("MCMC: distributions") source(test_path("data-for-mcmc-tests.R")) get_palette <- function(ggplot, n) { scale <- ggplot$scales$get_scales("colour") scale$palette(n) } test_that("mcmc_hist returns a ggplot object", { expect_gg(mcmc_hist(arr, pars = "beta[1]", regex_pars = "x\\:", binwidth = 0.1)) expect_gg(mcmc_hist(arr1chain, regex_pars = "beta", binwidth = 0.1)) expect_gg(mcmc_hist(drawsarr, pars = "theta[1]", binwidth = 0.1)) expect_gg(mcmc_hist(drawsarr1chain, regex_pars = "theta", binwidth = 0.1)) expect_gg(mcmc_hist(mat, binwidth = 0.1)) expect_gg(mcmc_hist(dframe, binwidth = 0.1)) expect_gg(mcmc_hist(dframe, bins = 10)) expect_gg(mcmc_hist(dframe_multiple_chains, binwidth = 0.1)) expect_gg(mcmc_hist(arr1, binwidth = 0.1)) expect_gg(mcmc_hist(drawsarr1, binwidth = 0.1)) expect_gg(mcmc_hist(mat1, binwidth = 0.1)) expect_gg(mcmc_hist(dframe1, binwidth = 0.1)) }) test_that("mcmc_dens returns a ggplot object", { expect_gg(mcmc_dens(arr, pars = "beta[2]", regex_pars = "x\\:")) expect_gg(mcmc_dens(arr1chain, regex_pars = "beta")) expect_gg(mcmc_dens(drawsarr, pars = "theta[1]")) expect_gg(mcmc_dens(drawsarr1chain, regex_pars = "theta")) expect_gg(mcmc_dens(mat)) expect_gg(mcmc_dens(dframe, transformations = list(sigma = function(x) x^2))) expect_gg(mcmc_dens( dframe_multiple_chains, transformations = list(sigma = function(x) x ^ 2, 'beta[1]' = "exp") )) expect_gg(mcmc_dens(arr1)) expect_gg(mcmc_dens(drawsarr1)) expect_gg(mcmc_dens(mat1)) expect_gg(mcmc_dens(dframe1)) }) # functions that require multiple chains ---------------------------------- test_that("mcmc_hist_by_chain returns a ggplot object", { expect_gg(mcmc_hist_by_chain(arr, pars = "beta[1]", regex_pars = "x\\:", binwidth = 0.1)) expect_gg(mcmc_hist_by_chain(dframe_multiple_chains, regex_pars = c("(Intercept)", "beta"), binwidth = 0.1)) }) test_that("mcmc_dens_overlay returns a ggplot object", { expect_gg(mcmc_dens_overlay(arr, pars = "beta[1]", regex_pars = "x\\:")) expect_gg(mcmc_dens_overlay(dframe_multiple_chains, pars = c("(Intercept)", "beta[2]"))) }) test_that("mcmc_dens_chains returns a ggplot object", { p <- mcmc_dens_chains(arr, pars = "beta[1]", regex_pars = "x\\:", color_chains = FALSE) expect_gg(p) p2 <- mcmc_dens_overlay(dframe_multiple_chains, pars = c("(Intercept)", "beta[2]"), color_chains = TRUE) expect_gg(p2) }) test_that("mcmc_dens_chains/mcmc_dens_overlay color chains", { p1 <- mcmc_dens_chains(arr, pars = "beta[1]", regex_pars = "x\\:", color_chains = FALSE) p2 <- mcmc_dens_overlay(arr, pars = "beta[1]", regex_pars = "x\\:", color_chains = FALSE) # Only one color when set not to color chains expect_equal(length(unique(get_palette(p1, 4))), 1) expect_equal(length(unique(get_palette(p2, 4))), 1) p3 <- mcmc_dens_chains(arr, pars = "beta[1]", regex_pars = "x\\:", color_chains = TRUE) p4 <- mcmc_dens_overlay(arr, pars = "beta[1]", regex_pars = "x\\:", color_chains = TRUE) # Chain coloring works expect_equal(get_palette(p3, 4), chain_colors(4)) expect_equal(get_palette(p4, 4), chain_colors(4)) }) test_that("mcmc_violin returns a ggplot object", { expect_gg(mcmc_violin(arr, pars = "beta[2]", regex_pars = "x\\:")) expect_gg(mcmc_violin(dframe_multiple_chains, regex_pars = c("\\(Intercept\\)$", "beta"))) }) test_that("mcmc_* throws error if 1 chain but multiple chains required", { expect_error(mcmc_hist_by_chain(mat), "requires multiple chains") expect_error(mcmc_hist_by_chain(dframe), "requires multiple chains") expect_error(mcmc_hist_by_chain(arr1chain), "requires multiple chains") expect_error(mcmc_hist_by_chain(drawsarr1chain), "requires multiple chains") expect_error(mcmc_dens_overlay(mat), "requires multiple chains") expect_error(mcmc_dens_overlay(dframe), "requires multiple chains") expect_error(mcmc_dens_overlay(arr1chain), "requires multiple chains") expect_error(mcmc_dens_overlay(drawsarr1chain), "requires multiple chains") expect_error(mcmc_dens_chains(mat), "requires multiple chains") expect_error(mcmc_dens_chains(dframe), "requires multiple chains") expect_error(mcmc_dens_chains(arr1chain), "requires multiple chains") expect_error(mcmc_dens_chains(drawsarr1chain), "requires multiple chains") expect_error(mcmc_violin(mat), "requires multiple chains") expect_error(mcmc_violin(dframe), "requires multiple chains") expect_error(mcmc_violin(arr1chain), "requires multiple chains") expect_error(mcmc_violin(drawsarr1chain), "requires multiple chains") }) # Visual tests ------------------------------------------------------------ test_that("mcmc_hist renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_hist(vdiff_dframe, binwidth = 0.1) vdiffr::expect_doppelganger("mcmc_hist (default)", p_base) p_freq <- mcmc_hist(vdiff_dframe, freq = TRUE, binwidth = 0.1) vdiffr::expect_doppelganger("mcmc_hist (freq)", p_freq) p_alpha <- mcmc_hist(vdiff_dframe, alpha = 0, binwidth = 0.1) vdiffr::expect_doppelganger("mcmc_hist (alpha)", p_alpha) }) test_that("mcmc_dens renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_dens(vdiff_dframe) vdiffr::expect_doppelganger("mcmc_dens (default)", p_base) p_alpha <- mcmc_dens(vdiff_dframe, alpha = 0) vdiffr::expect_doppelganger("mcmc_dens (alpha)", p_alpha) }) test_that("mcmc_dens_overlay renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_dens_overlay(vdiff_dframe_chains) vdiffr::expect_doppelganger("mcmc_dens_overlay (default)", p_base) }) test_that("mcmc_dens_chains renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_dens_chains(vdiff_dframe_chains) vdiffr::expect_doppelganger("mcmc_dens_chains (default)", p_base) }) test_that("mcmc_hist_by_chain renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_hist_by_chain(vdiff_dframe_chains, binwidth = 0.5) vdiffr::expect_doppelganger("mcmc_hist_by_chain (default)", p_base) }) test_that("mcmc_violin renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_violin(vdiff_dframe_chains) vdiffr::expect_doppelganger("mcmc_violin (default)", p_base) }) bayesplot/tests/testthat/test-available_ppc.R0000644000176200001440000000462214212422623021142 0ustar liggesuserslibrary(bayesplot) context("available_mcmc and available_ppc") test_that("available_mcmc works", { a <- available_mcmc() expect_s3_class(a, "bayesplot_function_list") expect_s3_class(a, "character") all_mcmc_plots <- sort(grep("^mcmc_", getNamespaceExports("bayesplot"), value = TRUE)) all_mcmc_plots <- grep("_data", all_mcmc_plots, invert = TRUE, value = TRUE) expect_identical(as.character(a), all_mcmc_plots) b <- available_mcmc("trace|dens", plots = FALSE) expect_s3_class(b, "bayesplot_function_list") expect_identical( as.character(b), sort(grep("^mcmc_dens|^mcmc_trace", getNamespaceExports("bayesplot"), value = TRUE)) ) expect_length(available_mcmc(pattern = "99999"), 0) }) test_that("available_ppc works", { a <- available_ppc() expect_s3_class(a, "bayesplot_function_list") expect_s3_class(a, "character") all_ppc_plots <- sort(grep("^ppc_", getNamespaceExports("bayesplot"), value = TRUE)) all_ppc_plots <- grep("_data", all_ppc_plots, invert = TRUE, value = TRUE) expect_identical(as.character(a), all_ppc_plots) b <- available_ppc("grouped") expect_s3_class(b, "bayesplot_function_list") expect_identical( as.character(b), sort(grep("^ppc_.*_grouped$", getNamespaceExports("bayesplot"), value = TRUE)) ) c <- available_ppc("grouped", invert = TRUE) expect_false(any(grepl("grouped", c))) expect_length(available_ppc(pattern = "99999"), 0) }) test_that("available_ppd works", { a <- available_ppd() expect_s3_class(a, "bayesplot_function_list") expect_s3_class(a, "character") all_ppd_plots <- sort(grep("^ppd_", getNamespaceExports("bayesplot"), value = TRUE)) all_ppd_plots <- grep("_data", all_ppd_plots, invert = TRUE, value = TRUE) expect_identical(as.character(a), all_ppd_plots) a <- available_ppd(plots_only = FALSE) expect_identical(as.character(a), sort(grep("^ppd_", getNamespaceExports("bayesplot"), value = TRUE))) }) test_that("print.bayesplot_function_list works", { expect_output(print(available_ppd()), "bayesplot PPD module:") expect_output(print(available_ppc()), "bayesplot PPC module:") expect_output(print(available_mcmc()), "bayesplot MCMC module:") expect_output(print(available_ppc("ribbon")), "(matching pattern 'ribbon')") expect_output(print(available_mcmc("trace")), "trace_highlight") expect_output(print(available_ppc("grouped", invert = TRUE)), "excluding pattern 'grouped'") }) bayesplot/tests/testthat/test-convenience-functions.R0000644000176200001440000001556514556270424022705 0ustar liggesuserslibrary(bayesplot) library(ggplot2) context("Convenience functions (for ggplot objects)") # abline_01, vline_ and hline_ ------------------------------------------ test_that("abline_01 returns the correct object", { a <- abline_01(color = "green", linetype = 2) b <- geom_abline(intercept = 0, slope = 1, color = "green", linetype = 2, na.rm = TRUE) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) }) test_that("vline_* and hline_* return correct objects", { a <- vline_0(color = "red") b <- geom_vline(xintercept = 0, color = "red", na.rm = TRUE) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) a <- hline_0(linewidth = 2, linetype = 3) b <- geom_hline(yintercept = 0, linewidth = 2, linetype = 3, na.rm = TRUE) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) a <- vline_at(c(3,4), na.rm = FALSE) b <- geom_vline(xintercept = c(3,4)) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) a <- hline_at(c(3,4), na.rm = FALSE) b <- geom_hline(yintercept = c(3,4)) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) }) test_that("vline_at with 'fun' works", { x <- example_mcmc_draws(chains = 1) a <- vline_at(x, colMeans) b <- geom_vline(xintercept = colMeans(x), na.rm = TRUE) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) }) test_that("calc_v (internal function) works", { a <- 1:4 expect_identical(calc_v(a, "mean"), 2.5) expect_identical(calc_v(a, median), 2.5) expect_equal(calc_v(c(a, NA), mean), NA_real_) expect_identical(calc_v(c(a, NA), min, list(na.rm = TRUE)), 1L) expect_error(calc_v(fun = "mean"), "'v' can't be missing") }) # lbub -------------------------------------------------------------------- test_that("lbub works", { f1 <- lbub(p = 0.5) f2 <- lbub(p = 0.5, med = FALSE) expect_type(f1, "closure") expect_type(f2, "closure") expect_identical( f1(1:50), setNames(c(13.25, 25.5, 37.75), c("25%", "50%", "75%")) ) expect_identical( f2(1:50), setNames(c(13.25, 37.75), c("25%", "75%")) ) }) # plot and facet backgrounds ---------------------------------------------- test_that("grid_lines returns correct theme object", { thm <- theme_default() + grid_lines(size = 1.5, color = "purple") expect_equal(thm$panel.grid.major, element_line(linewidth = 1.5, color = "purple")) expect_equal(thm$panel.grid.minor, element_line(linewidth = 0.75, color = "purple")) }) test_that("panel_bg returns correct theme object", { bg1 <- panel_bg() bg2 <- panel_bg(fill = "blue", linetype = 2) expect_identical(bg1, theme(panel.background = element_rect())) expect_identical(bg2, theme(panel.background = element_rect(fill = "blue", linetype = 2))) expect_identical(panel_bg(on = FALSE), theme(panel.background = element_blank())) }) test_that("plot_bg returns correct theme object", { bg1 <- plot_bg() bg2 <- plot_bg(fill = "blue", linetype = 2) expect_identical(bg1, theme(plot.background = element_rect())) expect_identical(bg2, theme(plot.background = element_rect(fill = "blue", linetype = 2))) expect_identical(plot_bg(on = FALSE), theme(plot.background = element_blank())) }) test_that("facet_bg returns correct theme object", { bg1 <- facet_bg() bg2 <- facet_bg(fill = "blue", linetype = 2) expect_identical(bg1, theme(strip.background = element_rect())) expect_identical(bg2, theme(strip.background = element_rect(fill = "blue", linetype = 2))) expect_identical(facet_bg(on = FALSE), theme(strip.background = element_blank())) }) # legend position and text ------------------------------------------------ test_that("legend_none returns correct theme object", { none <- legend_none() expect_s3_class(none, "theme") expect_equivalent(none, list(legend.position = "none")) expect_false(attr(none, "complete")) }) test_that("legend_move returns correct theme object", { left <- legend_move("left") expect_s3_class(left, "theme") expect_equivalent(left, list(legend.position = "left")) expect_false(attr(left, "complete")) pos <- legend_move(c(0.25, 0.5)) expect_s3_class(pos, "theme") expect_equivalent( pos$legend.position.inside %||% pos$legend.position, c(0.25, 0.5) ) expect_false(attr(pos, "complete")) }) test_that("legend_text returns correct theme object", { expect_equal( legend_text(size = 16, color = "purple"), theme(legend.text = element_text(color = "purple", size = 16)) ) }) # axis and facet text -------------------------------------------------- test_that("xaxis_text returns correct theme object", { expect_identical(xaxis_text(FALSE), theme(axis.text.x = element_blank())) expect_equal( xaxis_text(face = "bold", angle = 30), theme(axis.text.x = element_text(face = "bold", angle = 30)) ) }) test_that("yaxis_text returns correct theme object", { expect_identical(yaxis_text(FALSE), theme(axis.text.y = element_blank())) expect_equivalent( yaxis_text(face = "bold", angle = 30), theme(axis.text.y = element_text(face = "bold", angle = 30)) ) }) test_that("facet_text returns correct theme object", { expect_identical(facet_text(FALSE), theme(strip.text = element_blank())) expect_equal( facet_text(size = 12, color = "blue"), theme(strip.text = element_text(color = "blue", size = 12)) ) }) # axis titles ------------------------------------------------------------- test_that("xaxis_title returns correct theme object", { expect_identical(xaxis_title(FALSE), xlab(NULL)) expect_equal( xaxis_title(face = "bold", angle = 30), theme(axis.title.x = element_text(face = "bold", angle = 30)) ) }) test_that("yaxis_title returns correct theme object", { expect_identical(yaxis_title(FALSE), ylab(NULL)) expect_equal( yaxis_title(face = "bold", angle = 30), theme(axis.title.y = element_text(face = "bold", angle = 30)) ) }) # tick marks -------------------------------------------------- test_that("xaxis_ticks returns correct theme object", { expect_identical(xaxis_ticks(FALSE), theme(axis.ticks.x = element_blank())) expect_equal( xaxis_ticks(linewidth = 0.5, color = "red"), theme(axis.ticks.x = element_line(linewidth = 0.5, color = "red")) ) }) test_that("yaxis_ticks returns correct theme object", { expect_identical(yaxis_ticks(FALSE), theme(axis.ticks.y = element_blank())) expect_equal( yaxis_ticks(linewidth = 0.5, color = "red"), theme(axis.ticks.y = element_line(linewidth = 0.5, color = "red")) ) }) # overlay functions ------------------------------------------------------- test_that("overlay_function returns the correct object", { expect_error(overlay_function(), 'argument "fun" is missing') a <- overlay_function(fun = "dnorm") b <- stat_function(fun = "dnorm", inherit.aes = FALSE) a$constructor <- b$constructor <- NULL expect_equal(a, b, check.environment = FALSE) }) bayesplot/tests/testthat/test-extractors.R0000644000176200001440000001057714335264325020575 0ustar liggesuserslibrary(bayesplot) context("Extractors") if (requireNamespace("rstanarm", quietly = TRUE)) { ITER <- 1000 CHAINS <- 3 fit <- rstanarm::stan_glm(mpg ~ wt + am, data = mtcars, iter = ITER, chains = CHAINS, refresh = 0) } x <- list(cbind(a = 1:3, b = rnorm(3)), cbind(a = 1:3, b = rnorm(3))) # nuts_params and log_posterior methods ----------------------------------- test_that("nuts_params.list throws errors", { x[[3]] <- c(a = 1:3, b = rnorm(3)) expect_error(nuts_params.list(x), "list elements should be matrices") x[[3]] <- cbind(a = 1:3, d = rnorm(3)) expect_error(nuts_params.list(x), "same column names") x[[3]] <- cbind(a = 1:4, b = rnorm(4)) expect_error(nuts_params.list(x), "same dimensions") }) test_that("nuts_params.list parameter selection ok", { expect_error(nuts_params.list(x, pars = "apple"), "subscript out of bounds") np <- nuts_params.list(x, pars = "b") expect_true(all(np$Parameter == "b")) }) test_that("all nuts_params methods identical", { skip_if_not_installed("rstanarm") skip_if_not_installed("rstan") expect_identical( nuts_params(fit), nuts_params(fit$stanfit) ) expect_identical( nuts_params(fit), nuts_params(rstan::get_sampler_params(fit$stanfit, inc_warmup = FALSE)) ) }) test_that("nuts_params.stanreg returns correct structure", { skip_if_not_installed("rstanarm") np <- nuts_params(fit) expect_identical(colnames(np), c("Chain", "Iteration", "Parameter", "Value")) np_names <- paste0(c("accept_stat", "stepsize", "treedepth", "n_leapfrog", "divergent", "energy"), "__") expect_identical(levels(np$Parameter), np_names) expect_equal(length(unique(np$Iteration)), floor(ITER / 2)) expect_equal(length(unique(np$Chain)), CHAINS) }) test_that("log_posterior.stanreg returns correct structure", { skip_if_not_installed("rstanarm") lp <- log_posterior(fit) expect_identical(colnames(lp), c("Chain", "Iteration", "Value")) expect_equal(length(unique(lp$Iteration)), floor(ITER / 2)) expect_equal(length(unique(lp$Chain)), CHAINS) }) test_that("rhat.stanreg returns correct structure", { skip_if_not_installed("rstanarm") r <- rhat(fit) expect_named(r) expect_equal(r, summary(fit)[1:length(r), "Rhat"]) expect_identical(names(rhat(fit, regex_pars = c("wt", "am"))), c("wt", "am")) }) test_that("neff_ratio.stanreg returns correct structure", { skip_if_not_installed("rstanarm") expect_named(neff_ratio(fit, pars = c("wt", "am")), c("wt", "am")) ratio <- neff_ratio(fit) expect_named(ratio) ans <- summary(fit)[1:length(ratio), "n_eff"] / (floor(ITER / 2) * CHAINS) expect_equal(ratio, ans, tol = 0.001) }) test_that("rhat.stanfit returns correct structure", { skip_if_not_installed("rstanarm") r <- rhat(fit$stanfit) expect_named(r) expect_equal(r, summary(fit)[, "Rhat"]) r2 <- rhat(fit$stanfit, pars = c("wt", "sigma")) expect_named(r2) expect_equal(r2, summary(fit, pars = c("wt", "sigma"))[, "Rhat"]) }) test_that("neff_ratio.stanreg returns correct structure", { skip_if_not_installed("rstanarm") denom <- floor(ITER / 2) * CHAINS ratio <- neff_ratio(fit$stanfit) expect_named(ratio) ans <- summary(fit)[, "n_eff"] / denom expect_equal(ratio, ans, tol = 0.001) ratio2 <- neff_ratio(fit$stanfit, pars = c("wt", "sigma")) expect_named(ratio2) ans2 <- summary(fit, pars = c("wt", "sigma"))[, "n_eff"] / denom expect_equal(ratio2, ans2, tol = 0.001) }) test_that("cmdstanr methods work", { skip_on_cran() skip_if_not_installed("cmdstanr") fit <- cmdstanr::cmdstanr_example("logistic", iter_sampling = 500, chains = 2) np <- nuts_params(fit) np_names <- paste0(c("treedepth", "divergent", "energy", "accept_stat", "stepsize", "n_leapfrog"), "__") expect_identical(levels(np$Parameter), np_names) expect_equal(range(np$Iteration), c(1, 500)) expect_equal(range(np$Chain), c(1, 2)) expect_true(all(np$Value[np$Parameter == "divergent__"] == 0)) lp <- log_posterior(fit) expect_named(lp, c("Chain", "Iteration", "Value")) expect_equal(range(np$Chain), c(1, 2)) expect_equal(range(np$Iteration), c(1, 500)) r <- rhat(fit) expect_named(head(r, 4), c("alpha", "beta[1]", "beta[2]", "beta[3]")) expect_true(all(round(r) == 1)) ratio <- neff_ratio(fit) expect_named(head(ratio, 4), c("alpha", "beta[1]", "beta[2]", "beta[3]")) expect_true(all(ratio > 0)) }) bayesplot/tests/testthat/data-for-ordinal.rda0000644000176200001440000000457514335264325021113 0ustar liggesusersŜݎE!)@+%cmyEɂ60ٵۯY@b^Os˧] ԏ0p~|Y}y3sLvc=>cZ83>\㙣cǘ/ۧYƐ| wwkf#~g}1r'c0>k}cX3V>8gkLr}}CL=2Vr::&>r6ԖN-r.S*w'kšg';a8*Nq|ɿW{PfRKGӳȑ|M>'b$4fu&jC"evyw֛{>Ԣq<&\uw1rgYl>f~InyVr8x/ W[z/_#jo\˟C)֕bsM^V706>з*RW7~f+3'懙{EGܴ9gd/i3O#GE_15<}C}0oIikӷ7 ksOso*[粎%~U/yװf3͹X; U'ͻ8+~y r^qv+2.H?2*7#q,39Z xk97X[jc.+_||:31VV6slͲ;y\yyk[ȳi::f?;Sqxg]3*7-W姙O=gm3=9e>She=uO{9fkM̫n.CFӷ"㶼#e"KbZϽ<;5>f6zIr޷-Ify ~Τ5^A4֖؜E׬͍ f(}avװi?xuB2Ǚ=G=ߕ^9v_r0m=j߼LfyNۼޚ&9{mMU3lvQ'ٗxj$7"cؼ&^O8mZ$_s 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sFĽ6|nXח/6vVG* ¯KX*3&dLFɨ3 &òLY SeqDY[X*,  CPaa0TX*, * CPea0TY, U* CAdaYDAdaYDAdaYj, 5 CPcas#~7C(dluCB`d7$7bayesplot/tests/testthat/test-example-draws.R0000644000176200001440000000244214127610044021130 0ustar liggesuserslibrary(bayesplot) context("Example draws") test_that("example_mcmc_draws throws correct errors", { expect_error(example_mcmc_draws(chains = 5), "chains <= 4") expect_error(example_mcmc_draws(chains = 0), "chains >= 1") expect_error(example_mcmc_draws(params = 7), "params <= 6") expect_error(example_mcmc_draws(params = 0), "params >= 1") }) test_that("example_mcmc_draws returns correct structure", { expect_identical(dim(example_mcmc_draws()), c(250L, 4L, 4L)) expect_identical(dim(example_mcmc_draws(chains = 1, params = 6)), c(250L, 6L)) expect_identical(dim(example_mcmc_draws(params = 1)), c(250L, 4L, 1L)) expect_identical(dimnames(example_mcmc_draws(4, 6))[[3]], c("alpha", "sigma", paste0("beta[", 1:4,"]"))) }) test_that("example ppc data works", { y <- example_y_data() expect_type(y, "integer") expect_true(is_vector_or_1Darray(y)) yrep <- example_yrep_draws() expect_type(yrep, "double") expect_is(yrep, "matrix") expect_equal(ncol(yrep), length(y)) group <- example_group_data() expect_s3_class(group, "factor") expect_equal(length(group), length(y)) x <- example_x_data() expect_type(x, "double") expect_true(is_vector_or_1Darray(x)) expect_equal(length(x), length(y)) }) bayesplot/tests/testthat/test-ppc-discrete.R0000644000176200001440000001245514556465326020767 0ustar liggesuserslibrary(bayesplot) # suppressPackageStartupMessages(library(rstanarm)) context("PPC: discrete") source(test_path("data-for-ppc-tests.R")) load(test_path("data-for-ordinal.rda")) # data("esoph", package = "datasets") # fit <- stan_polr(tobgp ~ agegp, data = esoph, method = "probit", prior = R2(0.2, "mean"), # init_r = 0.1, seed = 12345, chains = 1, iter = 500, refresh = 0) # y_ord <- as.integer(fit$y) # yrep_char <- posterior_predict(fit, draws = 50) # yrep_ord <- sapply(data.frame(yrep_char, stringsAsFactors = TRUE), as.integer) # group_ord <- datasets::esoph$agegp # save(y_ord, yrep_ord, group_ord, file = testthat::test_path("data-for-ordinal.rda"), version = 2) # bar plots --------------------------------------------------------------- test_that("ppc_bars & ppc_bars_grouped return a ggplot object", { expect_gg(ppc_bars(y_ord, yrep_ord)) expect_gg(ppc_bars(y_ord, yrep_ord, prob = 0)) expect_gg(ppc_bars_grouped(y_ord, yrep_ord, group = group_ord)) }) test_that("freq argument to ppc_bars works", { p_freq <- ggplot2::ggplot_build(ppc_bars(y_ord, yrep_ord, freq = TRUE)) p_prop <- ggplot2::ggplot_build(ppc_bars(y_ord, yrep_ord, freq = FALSE)) y_freq <- p_freq$data[[1]]$y y_prop <- p_prop$data[[1]]$y expect_equal(y_freq, as.integer(y_freq)) expect_true(all(y_prop < 1) && all(y_prop > 0)) }) test_that("ppc_bars works with negative integers", { y <- round(rnorm(100, -10, 1)) yrep <- round(matrix(rnorm(100 * 500, -10, 1), 500, 100)) expect_gg(ppc_bars(y, yrep)) }) test_that("ppc_bars(_grouped) errors if y/yrep not discrete", { # make continuous y_cont <- y_ord + 0.33 yrep_cont <- yrep_ord + 0.33 expect_error(ppc_bars(y_cont, yrep_ord), "ppc_bars expects 'y' to be discrete") expect_error(ppc_bars(y_ord, yrep_cont), "ppc_bars expects 'yrep' to be discrete") expect_error(ppc_bars_grouped(y_cont, yrep_ord, group = group_ord), "ppc_bars expects 'y' to be discrete") expect_error(ppc_bars_grouped(y_ord, yrep_cont, group = group_ord), "ppc_bars expects 'yrep' to be discrete") }) test_that("ppc_bars_data includes all levels", { y_ord2 <- y_ord y_ord2[y_ord2 == 1] <- 2 yrep_ord2 <- yrep_ord yrep_ord2[yrep_ord2 == 2] <- 1 tab <- as.integer(table(y_ord)) # y and yrep have save levels d <- ppc_bars_data(y_ord, yrep_ord) expect_equal(d$x, 1:4) expect_equal(d$y_obs, tab) # yrep has more unique values than y d2 <- ppc_bars_data(y_ord2, yrep_ord) expect_equal(d2$x, 1:4) expect_equal(d2$y_obs, c(NA, sum(tab[1:2]), tab[3:4])) # y has more unique values than yrep d3 <- ppc_bars_data(y_ord, yrep_ord2) expect_equal(d3$x, 1:4) expect_equal(d3$y_obs, tab) expect_equivalent(d3$l[2], NA_real_) expect_equivalent(d3$m[2], NA_real_) expect_equivalent(d3$h[2], NA_real_) }) # rootograms ----------------------------------------------------------- yrep3 <- matrix(yrep2, nrow = 5, ncol = ncol(yrep2), byrow = TRUE) test_that("ppc_rootogram returns a ggplot object", { expect_gg(ppc_rootogram(y2, yrep2)) expect_gg(ppc_rootogram(y2, yrep3, style = "hanging", prob = 0.5)) expect_gg(ppc_rootogram(y2, yrep3, style = "suspended")) }) test_that("ppc_rootogram errors if y/yrep not counts", { expect_error(ppc_rootogram(y, yrep), "ppc_rootogram expects counts as inputs to 'y'") expect_error(ppc_rootogram(y2, yrep[1:5, seq_along(y2)]), "ppc_rootogram expects counts as inputs to 'yrep'") expect_error(ppc_rootogram(y, yrep3), "ncol(yrep) must be equal to length(y)", fixed = TRUE) }) # Visual tests ------------------------------------------------------------ test_that("ppc_bars renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_bars(vdiff_y2, vdiff_yrep2) vdiffr::expect_doppelganger("ppc_bars (default)", p_base) p_custom <- ppc_bars( y = vdiff_y2, yrep = vdiff_yrep2, width = 0.5, size = 0.5, fatten = 5 ) vdiffr::expect_doppelganger( title = "ppc_bars (width, size, fatten)", fig = p_custom) p_custom_prob <- ppc_bars( y = vdiff_y2, yrep = vdiff_yrep2, prob = 0.33, width = 0.5, size = 0.5, fatten = 5 ) vdiffr::expect_doppelganger( title = "ppc_bars (prob=0.33, width, size, fatten)", fig = p_custom_prob) }) test_that("ppc_bars_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_bars_grouped(vdiff_y2, vdiff_yrep2, vdiff_group2) vdiffr::expect_doppelganger("ppc_bars_grouped (default)", p_base) p_custom <- ppc_bars_grouped( y = vdiff_y2, yrep = vdiff_yrep2, group = vdiff_group2, facet_args = list(nrow = 2), prob = 0.5, size = 0.5 ) vdiffr::expect_doppelganger( title = "ppc_bars_grouped (facet_args, prob, size)", fig = p_custom) }) test_that("ppc_rootogram renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_rootogram(vdiff_y2, vdiff_yrep2) vdiffr::expect_doppelganger("ppc_rootogram (default)", p_base) p_custom_hanging <- ppc_rootogram( y = vdiff_y2, yrep = vdiff_yrep2, prob = 2/3, size = 3, style = "hanging" ) vdiffr::expect_doppelganger( title = "ppc_rootogram (style='hanging', prob, size)", fig = p_custom_hanging) }) bayesplot/tests/testthat/data-for-mcmc-tests.R0000644000176200001440000000526114335264325021166 0ustar liggesusersset.seed(8420) # Prepare input objects arr <- array(rnorm(4000), dim = c(100, 4, 10)) arr1chain <- arr[, 1, , drop = FALSE] drawsarr <- posterior::example_draws() drawsarr1chain <- drawsarr[, 1, , drop = FALSE] mat <- matrix(rnorm(1000), nrow = 100, ncol = 10) dframe <- as.data.frame(mat) chainlist <- list(matrix(rnorm(1000), nrow = 100, ncol = 10), matrix(rnorm(1000), nrow = 100, ncol = 10)) mixed_names <- c("(Intercept)", "beta[1]", "beta[2]", "sigma", paste0("b[(Intercept) x:", 1:6, "]")) colnames(mat) <- colnames(dframe) <- colnames(chainlist[[1]]) <- colnames(chainlist[[2]]) <- mixed_names dimnames(arr) <- dimnames(arr1chain) <- list(NULL, NULL, mixed_names) chainlist1chain <- chainlist[1] # one parameter arr1 <- arr[, , 1, drop = FALSE] drawsarr1 <- drawsarr[, , 1, drop = FALSE] mat1 <- mat[, 1, drop = FALSE] dframe1 <- dframe[, 1, drop = FALSE] chainlist1 <- list(chainlist[[1]][, 1, drop=FALSE], chainlist[[2]][, 1, drop=FALSE]) # data.frame with chain column dframe_multiple_chains <- dframe dframe_multiple_chains$chain <- rep(1:4, 25) # for vdiffr visual tests set.seed(11172017) vdiff_dframe <- as.data.frame(matrix(rnorm(500), nrow = 100, ncol = 5)) vdiff_dframe_chains <- as.data.frame( matrix(rnorm(4000), nrow = 2000, ncol = 2) ) vdiff_dframe_chains$chain <- rep(1:4, each = 500) vdiff_dframe_chains_divergences <- data.frame( Iteration = rep(1:500, each = 4), Parameter = "divergent__", Value = rbinom(2000, size = 1, prob = .02), Chain = vdiff_dframe_chains$chain, stringsAsFactors = FALSE ) vdiff_dframe_chains_treedepth <- vdiff_dframe_chains_divergences vdiff_dframe_chains_treedepth$Parameter <- "treedepth__" vdiff_dframe_chains_treedepth$Value <- sample(1:10, size = 2000, replace = TRUE) vdiff_dframe_chains_acceptance <- vdiff_dframe_chains_divergences vdiff_dframe_chains_acceptance$Parameter <- "accept_stat__" vdiff_dframe_chains_acceptance$Value <- runif(2000, 0.7, 1) vdiff_dframe_chains_stepsize <- vdiff_dframe_chains_divergences vdiff_dframe_chains_stepsize$Parameter <- "stepsize__" vdiff_dframe_chains_stepsize$Value <- rep(c(0.37, 0.42, 0.33, 0.47), each = 500) vdiff_dframe_chains_energy <- vdiff_dframe_chains_divergences vdiff_dframe_chains_energy$Parameter <- "energy__" vdiff_dframe_chains_energy$Value <- rexp(2000, rate = 0.1) vdiff_dframe_chains_np <- rbind( vdiff_dframe_chains_divergences, vdiff_dframe_chains_treedepth, vdiff_dframe_chains_energy, vdiff_dframe_chains_acceptance, vdiff_dframe_chains_stepsize ) vdiff_dframe_chains_lp <- vdiff_dframe_chains_divergences vdiff_dframe_chains_lp$Parameter <- NULL vdiff_dframe_chains_lp$Value <- runif(2000, -100, -50) set.seed(seed = NULL) bayesplot/tests/testthat/test-bayesplot_grid.R0000644000176200001440000000320314335264325021372 0ustar liggesuserslibrary(bayesplot) context("bayesplot_grid") y <- example_y_data() yrep <- example_yrep_draws()[1:25, ] gr <- gridExtra::arrangeGrob(ppc_stat(y, yrep, binwidth = 1)) p1 <- ppc_scatter_avg(y, yrep) p2 <- ppc_stat(y, yrep, binwidth = 1) test_that("as_bayesplot_grid works", { expect_s3_class(as_bayesplot_grid(gr), "bayesplot_grid") expect_s3_class(as_bayesplot_grid(gr), "gtable") }) test_that("bayesplot_grid throws correct errors", { expect_error(bayesplot_grid(xlim = 2), "No plots specified") expect_error(bayesplot_grid(gr, plots = list(p1, p2)), "'...' and 'plots' can't both be specified") expect_error(bayesplot_grid(plots = gr), "'plots' must be a list of ggplot objects") expect_error(bayesplot_grid(gr), "objects in '...' must be ggplot objects.") expect_error(bayesplot_grid(p1, p2, titles = c("plot1")), "length(titles) == length(plots) is not TRUE", fixed = TRUE) expect_error(bayesplot_grid(p1, p2, subtitles = c("plot1")), "length(subtitles) == length(plots) is not TRUE", fixed = TRUE) }) test_that("bayesplot_grid works", { expect_message( a <- bayesplot_grid(p1, p2, xlim = c(-200, 200), ylim = c(0, 200)), "is already present", fixed = TRUE ) expect_silent( b <- bayesplot_grid(plots = list(p1, p2), titles = c("plot1", "plot2"), subtitles = c("plot1_sub", "plot2_sub"), legends = FALSE) ) expect_s3_class(a, "bayesplot_grid") expect_s3_class(b, "bayesplot_grid") expect_equal(length(a$grobs), 2) expect_equal(length(b$grobs), 2) }) bayesplot/tests/testthat/test-ppc-distributions.R0000644000176200001440000002312514556465326022063 0ustar liggesuserslibrary(bayesplot) context("PPC: distributions") source(test_path("data-for-ppc-tests.R")) test_that("ppc_dens_overlay returns a ggplot object", { expect_gg(ppc_dens_overlay(y, yrep)) expect_gg(ppc_dens_overlay(y2, yrep2, size = 0.5, alpha = 0.2)) # ppd versions expect_gg(ppd_dens_overlay(yrep)) expect_gg(ppd_dens_overlay(yrep2, size = 0.5, alpha = 0.2)) }) test_that("ppc_ecdf_overlay returns a ggplot object", { expect_gg(ppc_ecdf_overlay(y, yrep, size = 0.5, alpha = 0.2)) expect_gg(ppc_ecdf_overlay(y2, yrep2)) # ppd versions expect_gg(ppd_ecdf_overlay(yrep, size = 0.5, alpha = 0.2)) expect_gg(ppd_ecdf_overlay(yrep2)) }) test_that("ppc_dens,pp_hist,ppc_freqpoly,ppc_boxplot return ggplot objects", { expect_gg(ppc_hist(y, yrep[1,, drop = FALSE], binwidth = 0.1)) expect_gg(ppc_hist(y, yrep[1:8, ], binwidth = 0.1)) expect_gg(ppc_hist(y2, yrep2, binwidth = 0.1)) expect_gg(ppc_dens(y, yrep[1:8, ])) expect_gg(ppc_dens(y2, yrep2)) expect_gg(ppc_freqpoly(y, yrep[1:8, ], binwidth = 2, size = 2, alpha = 0.1)) expect_gg(ppc_freqpoly(y2, yrep2, binwidth = 0.1)) expect_gg(ppc_boxplot(y, yrep[1,, drop = FALSE])) expect_gg(ppc_boxplot(y, yrep[1:8, ])) expect_gg(ppc_boxplot(y2, yrep2, notch = FALSE)) expect_gg(p <- ppc_hist(y, yrep[1:8, ], binwidth = 3)) if (utils::packageVersion("ggplot2") >= "3.0.0") { facet_var <- vars(rep_label) expect_equal(p$facet$params$facets[[1]], facet_var[[1]]) } # ppd versions expect_gg(ppd_hist(yrep[1,, drop = FALSE], binwidth = 0.1)) expect_gg(ppd_hist(yrep[1:8, ], binwidth = 0.1)) expect_gg(ppd_hist(yrep2, binwidth = 0.1)) expect_gg(ppc_dens(y, yrep[1:8, ])) expect_gg(ppc_dens(y2, yrep2)) expect_gg(ppd_freqpoly(yrep[1:8, ], binwidth = 2, size = 2, alpha = 0.1)) expect_gg(ppd_freqpoly(yrep2, binwidth = 0.1)) expect_gg(ppd_boxplot(yrep[1,, drop = FALSE])) expect_gg(ppd_boxplot(yrep[1:8, ])) expect_gg(ppd_boxplot(yrep2, notch = FALSE)) }) test_that("ppc_pit_ecdf, ppc_pit_ecdf_grouped returns a ggplot object", { expect_gg(ppc_pit_ecdf(y, yrep, interpolate_adj = FALSE)) expect_gg(ppc_pit_ecdf_grouped(y, yrep, group = group, interpolate_adj = FALSE)) expect_message(ppc_pit_ecdf(pit = runif(100)), "'pit' specified") expect_message( ppc_pit_ecdf_grouped(pit = runif(length(group)), group = group, interpolate_adj = FALSE), "'pit' specified" ) }) test_that("ppc_freqpoly_grouped returns a ggplot object", { expect_gg(ppc_freqpoly_grouped(y, yrep[1:4, ], group)) expect_gg(ppc_freqpoly_grouped(y, yrep[1:4, ], group, freq = TRUE, alpha = 0.5)) # ppd versions expect_gg(ppd_freqpoly_grouped(yrep[1:4, ], group)) }) test_that("ppc_violin_grouped returns a ggplot object", { expect_gg(ppc_violin_grouped(y, yrep, group)) expect_gg(ppc_violin_grouped(y, yrep, as.numeric(group))) expect_gg(ppc_violin_grouped(y, yrep, as.integer(group))) expect_gg(ppc_violin_grouped(y, yrep, group, y_draw = "both", y_jitter = 0.3)) }) # Visual tests ----------------------------------------------------------------- test_that("ppc_hist renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_hist(vdiff_y, vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppc_hist (default)", p_base) p_binwidth <- ppc_hist(vdiff_y, vdiff_yrep[1:8, ], binwidth = 3) vdiffr::expect_doppelganger("ppc_hist (binwidth)", p_binwidth) # ppd versions p_base <- ppd_hist(vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppd_hist (default)", p_base) p_binwidth <- ppd_hist(vdiff_yrep[1:8, ], binwidth = 3) vdiffr::expect_doppelganger("ppd_hist (binwidth)", p_binwidth) }) test_that("ppc_freqpoly renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_freqpoly(vdiff_y, vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppc_freqpoly (default)", p_base) p_custom <- ppc_freqpoly( y = vdiff_y, yrep = vdiff_yrep[1:8, ], binwidth = 2, size = 2, alpha = 0.1) vdiffr::expect_doppelganger( title = "ppc_freqpoly (alpha, binwidth, size)", fig = p_custom) # ppd versions p_base <- ppd_freqpoly(vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppd_freqpoly (default)", p_base) p_custom <- ppd_freqpoly( ypred = vdiff_yrep[1:8, ], binwidth = 2, size = 2, alpha = 0.1) vdiffr::expect_doppelganger( title = "ppd_freqpoly (alpha, binwidth, size)", fig = p_custom) }) test_that("ppc_freqpoly_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_freqpoly_grouped(vdiff_y, vdiff_yrep[1:3, ], vdiff_group) vdiffr::expect_doppelganger("ppc_freqpoly_grouped (default)", p_base) # ppd versions p_base <- ppd_freqpoly_grouped(vdiff_yrep[1:3, ], vdiff_group) vdiffr::expect_doppelganger("ppd_freqpoly_grouped (default)", p_base) }) test_that("ppc_boxplot renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_boxplot(vdiff_y, vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppc_boxplot (default)", p_base) p_no_notch <- ppc_boxplot(vdiff_y, vdiff_yrep[1:8, ], notch = FALSE) vdiffr::expect_doppelganger("ppc_boxplot (no notch)", p_no_notch) p_custom <- ppc_boxplot(vdiff_y, vdiff_yrep[1:8, ], size = 1.5, alpha = .5) vdiffr::expect_doppelganger("ppc_boxplot (alpha, size)", p_custom) # ppd versions p_base <- ppd_boxplot(vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppd_boxplot (default)", p_base) p_no_notch <- ppd_boxplot(vdiff_yrep[1:8, ], notch = FALSE) vdiffr::expect_doppelganger("ppd_boxplot (no notch)", p_no_notch) p_custom <- ppd_boxplot(vdiff_yrep[1:8, ], size = 1.5, alpha = .5) vdiffr::expect_doppelganger("ppd_boxplot (alpha, size)", p_custom) }) test_that("ppc_ecdf_overlay renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_ecdf_overlay(vdiff_y2, vdiff_yrep2) vdiffr::expect_doppelganger("ppc_ecdf_overlay (default)", p_base) p_custom <- ppc_ecdf_overlay( vdiff_y2, vdiff_yrep2, discrete = TRUE, size = 2, alpha = .2 ) vdiffr::expect_doppelganger( "ppc_ecdf_overlay (discrete, size, alpha)", p_custom ) }) test_that("ppc_ecdf_overlay_grouped renders correctly", { testthat::skip_on_cran() p_base <- ppc_ecdf_overlay_grouped(vdiff_y2, vdiff_yrep2, vdiff_group2) vdiffr::expect_doppelganger("ppc_ecdf_overlay_grouped (default)", p_base) p_custom <- ppc_ecdf_overlay_grouped( vdiff_y2, vdiff_yrep2, vdiff_group2, discrete = TRUE, size = 2, alpha = .2 ) vdiffr::expect_doppelganger( "ppc_ecdf_overlay_grouped (discrete, size, alpha)", p_custom ) }) test_that("ppc_dens renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_dens(vdiff_y, vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppc_dens (default)", p_base) # ppd versions p_base <- ppd_dens(vdiff_yrep[1:8, ]) vdiffr::expect_doppelganger("ppd_dens (default)", p_base) }) test_that("ppc_dens_overlay renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_dens_overlay(vdiff_y, vdiff_yrep) vdiffr::expect_doppelganger("ppc_dens_overlay (default)", p_base) p_custom <- ppc_dens_overlay(vdiff_y, vdiff_yrep, size = 1, alpha = 0.2) vdiffr::expect_doppelganger("ppc_dens_overlay (alpha, size)", p_custom) # ppd versions p_base <- ppd_dens_overlay(vdiff_yrep) vdiffr::expect_doppelganger("ppd_dens_overlay (default)", p_base) p_custom <- ppd_dens_overlay(vdiff_yrep, size = 1, alpha = 0.2) vdiffr::expect_doppelganger("ppd_dens_overlay (alpha, size)", p_custom) }) test_that("ppc_dens_overlay_grouped renders correctly", { testthat::skip_on_cran() p_base <- ppc_dens_overlay_grouped(vdiff_y, vdiff_yrep, vdiff_group) vdiffr::expect_doppelganger("ppc_dens_overlay_grouped (default)", p_base) p_custom <- ppc_dens_overlay_grouped( vdiff_y, vdiff_yrep, vdiff_group, size = 1, alpha = 0.2 ) vdiffr::expect_doppelganger( "ppc_dens_overlay_grouped (alpha, size)", p_custom ) }) test_that("ppc_violin_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not(getRversion() >= "3.6.0") testthat::skip_if_not_installed("vdiffr") p_base <- ppc_violin_grouped(vdiff_y, vdiff_yrep, vdiff_group) vdiffr::expect_doppelganger("ppc_violin_grouped (default)", p_base) # lock in jitter set.seed(100) p_dots <- ppc_violin_grouped( y = vdiff_y, yrep = vdiff_yrep, group = vdiff_group, y_draw = "both") vdiffr::expect_doppelganger("ppc_violin_grouped (with points)", p_dots) p_dots_jitter <- ppc_violin_grouped( y = vdiff_y, yrep = vdiff_yrep, group = vdiff_group, y_draw = "points", y_jitter = 0.01) vdiffr::expect_doppelganger( "ppc_violin_grouped (points, low jitter)", p_dots_jitter) set.seed(seed = NULL) }) test_that("ppc_pit_ecdf, ppc_pit_ecdf_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_pit_ecdf(y, yrep, interpolate_adj = FALSE) g_base <- ppc_pit_ecdf_grouped(y, yrep, group = group, interpolate_adj = FALSE) p_diff <- ppc_pit_ecdf(y, yrep, plot_diff = TRUE, interpolate_adj = FALSE) g_diff <- ppc_pit_ecdf_grouped(y, yrep, plot_diff = TRUE, group = group, interpolate_adj = FALSE) vdiffr::expect_doppelganger("ppc_pit_ecdf (default)", p_base) vdiffr::expect_doppelganger("ppc_pit_ecdf_grouped (default)", g_base) vdiffr::expect_doppelganger("ppc_pit_ecdf (diff)", p_diff) vdiffr::expect_doppelganger("ppc_pit_ecdf_grouped (diff)", g_diff) }) bayesplot/tests/testthat/test-mcmc-combo.R0000644000176200001440000000435114556465326020415 0ustar liggesuserslibrary(bayesplot) context("MCMC: combo") source(test_path("data-for-mcmc-tests.R")) test_that("mcmc_combo returns a gtable object", { expect_gtable(mcmc_combo(arr, regex_pars = "beta")) expect_gtable(mcmc_combo(arr, regex_pars = "beta", gg_theme = ggplot2::theme_dark())) expect_gtable(mcmc_combo(drawsarr, regex_pars = "theta")) expect_gtable(mcmc_combo(mat, regex_pars = "beta", binwidth = 1/20, combo = c("dens", "hist"), facet_args = list(nrow = 2))) expect_gtable(mcmc_combo(dframe, regex_pars = "Intercept")) expect_gtable(mcmc_combo(dframe_multiple_chains, regex_pars = "Intercept", combo = c("trace_highlight", "dens_overlay"))) expect_gtable(mcmc_combo(arr1chain, regex_pars = "Intercept", combo = c("trace", "hist"))) expect_gtable(mcmc_combo(arr1, pars = "(Intercept)")) expect_gtable(mcmc_combo(drawsarr1)) expect_gtable(mcmc_combo(mat1)) expect_gtable(mcmc_combo(dframe1)) }) # functions that require multiple chains ---------------------------------- test_that("mcmc_combo throws error if 1 chain but multiple chains required", { expect_error(mcmc_combo(arr1chain, regex_pars = "beta", combo = c("trace_highlight", "dens")), "requires multiple chains") expect_error(mcmc_combo(drawsarr1chain, regex_pars = "theta", combo = c("trace_highlight", "dens")), "requires multiple chains") expect_error(mcmc_combo(mat, regex_pars = "beta", combo = c("trace_highlight", "hist")), "requires multiple chains") expect_error(mcmc_combo(dframe, regex_pars = "beta", combo = c("dens_overlay", "trace")), "requires multiple chains") }) # other errors ------------------------------------------------------------ test_that("mcmc_combo throws errors", { expect_error(mcmc_combo(arr, combo = c("trace_highlight")), "'combo' should have at least two elements") expect_error(mcmc_combo(arr, regex_pars = "beta", combo = c("animal", "hist", "tornado")), "The following functions were not found: mcmc_animal, mcmc_tornado") }) bayesplot/tests/testthat/test-helpers-shared.R0000644000176200001440000000706014127610044021266 0ustar liggesuserslibrary(bayesplot) context("Shared: misc. functions") # suggested packages ------------------------------------------------------ test_that("suggested_package throws correct errors", { expect_error(suggested_package("NOPACKAGE"), "Please install the NOPACKAGE package") expect_error(suggested_package(c("testthat", "gridExtra")), "length") expect_silent(suggested_package("testthat")) expect_silent(suggested_package("testthat", min_version = "0.0.1")) expect_error(suggested_package("testthat", min_version = "100000.0.0")) }) # check_ignored_arguments ------------------------------------------------- test_that("check_ignored_arguments throws correct warnings", { expect_warning(check_ignored_arguments(a = 1, b = "2"), "The following arguments were unrecognized and ignored: a, b") expect_warning(check_ignored_arguments(a = 1, b = "2", ok_args = c("a", "c")), "The following arguments were unrecognized and ignored: b") expect_silent(check_ignored_arguments(a = 1, b = "2", ok_args = c("a", "b", "c"))) }) # parameter selection ----------------------------------------------------- all_pars <- c("param_1", "param_2", "param[1]", "param[2]", "param[1,3,5]", "param[2,4,5]", "alpha", "beta") test_that("select_parameters throws errors if 'explicit' not found", { expect_error(select_parameters(explicit = c("alpha", "ALPHA"), complete_pars = all_pars), "don't match parameter names: ALPHA") expect_error(select_parameters(c("BETA", "ALPHA"), complete = all_pars), "don't match parameter names: BETA, ALPHA") }) test_that("select_parameters throws errors if no regex matches", { expect_error(select_parameters(explicit = c("alpha", "beta"), patterns = "tomato|apple", complete_pars = all_pars), "No matches for 'regex_pars'") }) test_that("select_parameters works with regex", { expect_identical(select_parameters(patterns = "param", complete = all_pars), all_pars[-c(7:8)]) expect_identical(select_parameters(patterns = c("param", "tomato"), complete_pars = all_pars), all_pars[-c(7:8)]) expect_identical(select_parameters(patterns = c("param\\[", "tomato"), complete_pars = all_pars), all_pars[3:6]) expect_identical(select_parameters(patterns = c("param\\_"), complete_pars = all_pars), all_pars[1:2]) }) test_that("select_parameters works without regex", { expect_identical(select_parameters(explicit = "alpha", complete_pars = all_pars), "alpha") expect_identical(select_parameters(c("alpha", "param[1,3,5]"), complete_pars = all_pars), c("alpha", "param[1,3,5]")) }) test_that("select_parameters works with both explicit and regex", { expect_identical(select_parameters(explicit = "alpha", patterns = "param", complete_pars = all_pars), c("alpha", all_pars[-c(7:8)])) expect_identical(select_parameters(explicit = "alpha", patterns = "alpha", complete_pars = all_pars), "alpha") expect_identical(select_parameters(explicit = c("alpha", "beta"), patterns = "param\\[|param\\_", complete_pars = all_pars), c(all_pars[7:8], all_pars[-c(7:8)])) }) bayesplot/tests/testthat/data-for-ppc-tests.R0000644000176200001440000000161714335264325021032 0ustar liggesusersset.seed(8420) y <- rnorm(100) yrep <- matrix(rnorm(2500), ncol = 100) group <- gl(4, 25, labels = LETTERS[1:4]) status_y <- rep_len(0:1, length.out = length(y)) y2 <- rpois(30, 1) yrep2 <- matrix(rpois(30, 1), ncol = 30) group2 <- rep(1, 30) status_y2 <- rep_len(0:1, length.out = length(y2)) # for vdiffr visual tests set.seed(11172017) vdiff_y <- rnorm(100) vdiff_yrep <- matrix(rnorm(2500), ncol = 100) vdiff_group <- gl(4, 25, labels = LETTERS[1:4]) vdiff_status_y <- rep_len(0:1, length.out = length(vdiff_y)) vdiff_y2 <- rpois(30, 1) vdiff_yrep2 <- matrix(rpois(30 * 10, 1), ncol = 30, nrow = 10) vdiff_group2 <- rep_len(c(1,2), length.out = 30) vdiff_status_y2 <- rep_len(0:1, length.out = length(vdiff_y2)) vdiff_loo_y <- rnorm(100, 30, 5) vdiff_loo_yrep <- matrix(rnorm(100 * 400, 30, 5), nrow = 400) vdiff_loo_lw <- vdiff_loo_yrep vdiff_loo_lw[] <- rnorm(100 * 400, -8, 2) set.seed(seed = NULL) bayesplot/tests/testthat/test-mcmc-traces.R0000644000176200001440000002061614335264325020570 0ustar liggesuserslibrary(bayesplot) context("MCMC: traces") source(test_path("data-for-mcmc-tests.R")) test_that("mcmc_trace returns a ggplot object", { expect_gg(mcmc_trace(arr, pars = "beta[1]", regex_pars = "x\\:")) expect_gg(mcmc_trace(arr1chain, pars = "beta[2]", regex_pars = "x\\:")) expect_gg(mcmc_trace(mat)) expect_gg(mcmc_trace(dframe)) expect_gg(mcmc_trace(dframe_multiple_chains)) expect_gg(mcmc_trace(chainlist)) expect_gg(mcmc_trace(arr1)) expect_gg(mcmc_trace(mat1)) expect_gg(mcmc_trace(dframe1)) expect_gg(mcmc_trace(chainlist1)) }) # functions that require multiple chains ---------------------------------- test_that("mcmc_trace_highlight returns a ggplot object", { expect_gg(mcmc_trace_highlight(arr, regex_pars = c("beta", "x\\:"))) expect_gg(mcmc_trace_highlight(dframe_multiple_chains, highlight = 2)) }) test_that("mcmc_trace_highlight throws error if 1 chain but multiple chains required", { expect_error(mcmc_trace_highlight(mat), "requires multiple") expect_error(mcmc_trace_highlight(dframe, highlight = 1), "requires multiple chains") expect_error(mcmc_trace_highlight(arr1chain, highlight = 1), "requires multiple chains") }) test_that("mcmc_trace_highlight throws error if highlight > number of chains", { expect_error(mcmc_trace_highlight(arr, pars = "sigma", highlight = 7), "'highlight' is 7") }) test_that("mcmc_rank_ecdf returns a ggplot object", { expect_gg(mcmc_rank_ecdf(arr, regex_pars = c("beta", "x\\:"))) expect_gg(mcmc_rank_ecdf(dframe_multiple_chains, interpolate_adj = FALSE)) }) test_that("mcmc_rank_ecdf throws error if 1 chain but multiple chains required", { expect_error(mcmc_rank_ecdf(mat), "requires multiple chains") expect_error(mcmc_rank_ecdf(dframe), "requires multiple chains") expect_error(mcmc_rank_ecdf(arr1chain), "requires multiple chains") }) # options ----------------------------------------------------------------- test_that("mcmc_trace options work", { expect_gg(g1 <- mcmc_trace(arr, regex_pars = "beta", window = c(5, 10))) coord <- g1$coordinates expect_equal(g1$coordinates$limits$x, c(5, 10)) expect_gg(g2 <- mcmc_trace(arr, regex_pars = "beta", n_warmup = 10)) ll <- g2$labels expect_true(all(c("xmin", "xmax", "ymin", "ymax") %in% names(ll))) expect_error(mcmc_trace(arr, iter1 = -1)) expect_error(mcmc_trace(arr, n_warmup = 50, iter1 = 20)) }) test_that("mcmc_rank_ecdf options work", { expect_error( mcmc_rank_ecdf(dframe_multiple_chains, interpolate_adj = TRUE), "No precomputed values" ) }) # displaying divergences in traceplot ------------------------------------- test_that("mcmc_trace 'np' argument works", { skip_if_not_installed("rstanarm") suppressPackageStartupMessages(library(rstanarm)) fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000, chains = 2, refresh = 0) draws <- as.array(fit) # divergences via nuts_params divs1 <- ensure_divergences(nuts_params(fit, pars = "divergent__")) g <- mcmc_trace(draws, pars = "sigma", np = divs1) expect_gg(g) l2_data <- g$layers[[2]]$data expect_equal(names(l2_data), "Divergent") # divergences as vector via 'divergences' arg should throw deprecation warning divs2 <- rep_len(c(0,1), length.out = nrow(draws)) expect_warning( g2 <- mcmc_trace(draws, pars = "sigma", divergences = divs2), regexp = "deprecated" ) expect_gg(g2) expect_error( mcmc_trace(draws, pars = "sigma", np = divs1, divergences = divs2), "can't both be specified" ) # check errors & messages expect_error(mcmc_trace(draws, pars = "sigma", np = 1), "length(divergences) == n_iter is not TRUE", fixed = TRUE) expect_error(mcmc_trace(draws[,1,], pars = "sigma", np = divs1), "num_chains(np) == n_chain is not TRUE", fixed = TRUE) expect_error(mcmc_trace(draws, pars = "sigma", np = divs1[1:10, ]), "num_iters(np) == n_iter is not TRUE", fixed = TRUE) divs1$Value[divs1$Parameter == "divergent__"] <- 0 expect_message(mcmc_trace(draws, pars = "sigma", np = divs1), "No divergences to plot.") }) # Visual tests ----------------------------------------------------------------- test_that("mcmc_trace renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_trace(vdiff_dframe_chains, pars = c("V1", "V2")) p_one_param <- mcmc_trace(vdiff_dframe_chains, pars = "V1") p_warmup <- mcmc_trace( vdiff_dframe_chains, pars = c("V1", "V2"), n_warmup = 200 ) p_iter1 <- mcmc_trace( vdiff_dframe_chains, pars = c("V1", "V2"), iter1 = 200 ) vdiffr::expect_doppelganger("mcmc_trace (default)", p_base) vdiffr::expect_doppelganger("mcmc_trace (one parameter)", p_one_param) vdiffr::expect_doppelganger("mcmc_trace (warmup window)", p_warmup) vdiffr::expect_doppelganger("mcmc_trace (iter1 offset)", p_iter1) }) test_that("mcmc_rank_overlay renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_rank_overlay(vdiff_dframe_chains, pars = c("V1", "V2")) p_base_ref <- mcmc_rank_overlay( vdiff_dframe_chains, pars = c("V1", "V2"), ref_line = TRUE ) p_one_param <- mcmc_rank_overlay(vdiff_dframe_chains, pars = "V1") p_one_param_wide_bins <- mcmc_rank_overlay( vdiff_dframe_chains, pars = "V1", n_bins = 4 ) vdiffr::expect_doppelganger("mcmc_rank_overlay (default)", p_base) vdiffr::expect_doppelganger( "mcmc_rank_overlay (reference line)", p_base_ref ) vdiffr::expect_doppelganger("mcmc_rank_overlay (one parameter)", p_one_param) vdiffr::expect_doppelganger( "mcmc_rank_overlay (wide bins)", p_one_param_wide_bins ) }) test_that("mcmc_rank_hist renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_rank_hist(vdiff_dframe_chains, pars = c("V1", "V2")) p_base_ref <- mcmc_rank_hist( vdiff_dframe_chains, pars = c("V1", "V2"), ref_line = TRUE ) p_one_param <- mcmc_rank_hist(vdiff_dframe_chains, pars = "V1") p_one_param_wide_bins <- mcmc_rank_hist( vdiff_dframe_chains, pars = "V1", n_bins = 4 ) vdiffr::expect_doppelganger("mcmc_rank_hist (default)", p_base) vdiffr::expect_doppelganger( "mcmc_rank_hist (reference line)", p_base_ref ) vdiffr::expect_doppelganger( "mcmc_rank_hist (one parameter)", p_one_param ) vdiffr::expect_doppelganger( "mcmc_rank_hist (wide bins)", p_one_param_wide_bins ) }) test_that("mcmc_trace_highlight renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_trace_highlight( vdiff_dframe_chains, pars = "V1", highlight = 1 ) p_2 <- mcmc_trace_highlight( vdiff_dframe_chains, pars = "V1", highlight = 2 ) p_alpha <- mcmc_trace_highlight( vdiff_dframe_chains, pars = "V1", highlight = 1, alpha = .1 ) vdiffr::expect_doppelganger("mcmc_trace_highlight (default)", p_base) vdiffr::expect_doppelganger("mcmc_trace_highlight (other chain)", p_2) vdiffr::expect_doppelganger("mcmc_trace_highlight (alpha)", p_alpha) }) test_that("mcmc_rank_ecdf renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_rank_ecdf(vdiff_dframe_chains, pars = c("V1", "V2")) p_one_param <- mcmc_rank_ecdf(vdiff_dframe_chains, pars = "V1") p_diff <- mcmc_rank_ecdf( vdiff_dframe_chains, pars = c("V1", "V2"), plot_diff = TRUE ) p_diff_one_param <- mcmc_rank_ecdf( vdiff_dframe_chains, pars = "V1", plot_diff = TRUE ) vdiffr::expect_doppelganger("mcmc_rank_ecdf (default)", p_base) vdiffr::expect_doppelganger("mcmc_rank_ecdf (one parameter)", p_one_param) vdiffr::expect_doppelganger("mcmc_rank_ecdf (diff)", p_diff) vdiffr::expect_doppelganger( "mcmc_rank_ecdf (one param, diff)", p_diff_one_param ) }) test_that("mcmc_trace with 'np' renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- mcmc_trace( vdiff_dframe_chains, pars = "V1", np = vdiff_dframe_chains_divergences ) new_style <- trace_style_np(div_color = "black") p_np_style <- mcmc_trace( vdiff_dframe_chains, pars = "V1", np = vdiff_dframe_chains_divergences, np_style = new_style ) vdiffr::expect_doppelganger("mcmc_trace divergences (default)", p_base) vdiffr::expect_doppelganger("mcmc_trace divergences (custom)", p_np_style) }) bayesplot/tests/testthat/test-aesthetics.R0000644000176200001440000001662414335264325020532 0ustar liggesuserslibrary(bayesplot) context("Aesthetics") # color scheme stuff ------------------------------------------------------ prepare_colors_for_test <- function(scheme) { setNames( bayesplot:::master_color_list[[scheme]], bayesplot:::scheme_level_names() ) } test_that("getting and setting the color scheme works", { color_scheme_set("red") expect_equivalent(color_scheme_get(), prepare_colors_for_test("red")) expect_named(prepare_colors_for_test("blue"), scheme_level_names()) expect_named(color_scheme_get(), scheme_level_names()) for (clr in names(master_color_list)) { color_scheme_set(clr) expect_equivalent(color_scheme_get(), prepare_colors_for_test(clr), info = clr) expect_named(color_scheme_get(), scheme_level_names()) } expect_output(print(color_scheme_get("mix-blue-green")), "mix-blue-green") expect_gg(plot(color_scheme_get("mix-blue-green"))) color_scheme_set("blue") expect_equivalent(color_scheme_get("teal"), prepare_colors_for_test("teal")) # error if not character expect_error(color_scheme_set(7), "'scheme' should be a character vector of length 1 or 6") }) test_that("color_scheme_get with i argument works", { a <- color_scheme_get("green", i = 1) expect_equal(length(a), 1) expect_named(a, "light") b <- color_scheme_get("purple", i = c(2, 4, 5)) expect_equal(length(b), 3) expect_named(b, c("light_highlight", "mid_highlight", "dark")) expect_error( color_scheme_get(i = 1:7), "all(i %in% seq_along(scheme)) is not TRUE", fixed = TRUE ) expect_error( color_scheme_get(i = c(1, 3, 3)), "length(unique(i)) == length(i) is not TRUE", fixed = TRUE ) # if is character it should behave the same as internal 'get_color' expect_identical(color_scheme_get(i = c("l", "dh")), get_color(c("l", "dh"))) }) test_that("setting mixed scheme works", { color_scheme_set("mix-gray-blue") expect_equivalent(color_scheme_get(), mixed_scheme("gray", "blue")) color_scheme_set("mix-blue-gray") expect_equivalent(color_scheme_get(), mixed_scheme("blue", "gray")) expect_error(color_scheme_set("mix-green-reds"), "should be one of") expect_error(color_scheme_set("mix-greens-red"), "should be one of") }) test_that("setting brewer scheme works", { skip_if_not_installed("RColorBrewer") color_scheme_set("brewer-Blues") expect_equivalent(unlist(color_scheme_get()), RColorBrewer::brewer.pal(6, "Blues")) color_scheme_set("brewer-Spectral") expect_equivalent(unlist(color_scheme_get()), RColorBrewer::brewer.pal(6, "Spectral")) expect_error(color_scheme_set("brewer-FAKE"), "FAKE is not a valid palette") }) orange_scheme_bad <- orange_scheme_ok <- c("not_a_color1", "#ffcc80", "#ffad33", "#e68a00", "#995c00", "not_a_color2") orange_scheme_ok[c(1, 6)] <- c("#ffebcc", "#663d00") test_that("color_scheme_set throws correct errors for custom schemes ", { expect_error(color_scheme_set(orange_scheme_bad), "not found: not_a_color1, not_a_color2") expect_error(color_scheme_set(c("red", "blue")), "should be a character vector of length 1 or 6") expect_error(prepare_custom_colors(c("red", "blue")), "Custom color schemes must contain exactly 6 colors") }) test_that("mixed_scheme internal function doesn't error", { x <- mixed_scheme("green", "red") expect_equal(length(x), 6) expect_true(all(sapply(x, is.character))) }) test_that("custom color schemes work", { color_scheme_set(orange_scheme_ok) expect_named(color_scheme_get()) expect_equivalent(unlist(color_scheme_get()), orange_scheme_ok) random_scheme <- colors()[sample(length(colors()), 6)] color_scheme_set(random_scheme) expect_equivalent(unlist(color_scheme_get()), random_scheme) }) test_that("get_color returns correct color values", { scheme <- color_scheme_set("green") levs <- scheme_level_names() ans <- unlist(prepare_colors_for_test("green")[levs], use.names = FALSE) expect_identical(get_color(levs), ans) for (lev in levs) expect_identical(get_color(lev), scheme[[lev]], info = lev) }) test_that("color_scheme_view returns correct ggplot object", { color_scheme_set("red") a <- color_scheme_view() b <- color_scheme_view("green") expect_gg(a) expect_gg(b) expect_identical(a$plot_env$x, color_scheme_get()) expect_identical(b$plot_env$x, color_scheme_get("green")) }) test_that("color_scheme_view returns gtable if length(scheme) >= 1", { expect_gtable(color_scheme_view(c("red", "gray"))) expect_gtable(color_scheme_view(c("red", "gray", "blue"))) }) # ggplot themes ------------------------------------------------------------ default <- theme_default() minimal <- ggplot2::theme_minimal() dark <- ggplot2::theme_dark() test_that("theme_default creates ggplot theme", { expect_type(default, "list") expect_s3_class(default, "theme") thm2 <- theme_default(base_size = 13) expect_type(thm2, "list") expect_s3_class(thm2, "theme") expect_equal(thm2[["text"]][["size"]], 13) }) test_that("bayesplot_theme_set warns of missing theme elements", { dark2 <- ggplot2::theme_dark() dark2$line <- NULL expect_warning( bayesplot_theme_set(dark2), "New theme missing the following elements: line" ) bayesplot_theme_set() }) test_that("bayesplot_theme_set/get work", { bayesplot_theme_set() expect_identical(bayesplot_theme_get(), default) expect_identical(bayesplot_theme_set(), default) old <- bayesplot_theme_set(minimal) expect_identical(old, default) expect_identical(bayesplot_theme_get(), minimal) }) test_that("bayesplot_theme_update/replace work", { bayesplot_theme_set(minimal) old <- bayesplot_theme_update(axis.text.x = ggplot2::element_text(color = "red")) expect_identical(old, minimal) thm <- bayesplot_theme_get() expect_identical(thm, minimal + xaxis_text(color = "red")) expect_equal(thm$axis.text.x$colour, "red") expect_null(thm$axis.text.x$size) bayesplot_theme_update(axis.text.x = ggplot2::element_text(size = 13)) thm <- bayesplot_theme_get() expect_equal(thm$axis.text.x$colour, "red") expect_equal(thm$axis.text.x$size, 13) old <- bayesplot_theme_replace(axis.text.x = ggplot2::element_text(color = "green")) expect_identical(old, thm) thm <- bayesplot_theme_get() expect_equal(thm$axis.text.x$colour, "green") expect_null(thm$axis.text.x$size) }) test_that("ggplot2::theme_set overrides bayesplot theme", { ggplot2::theme_set(dark) bayesplot_theme_set() expect_identical(ggplot2::theme_get(), dark) expect_identical(bayesplot_theme_get(), default) ggplot2::theme_set(minimal) expect_identical(bayesplot_theme_get(), minimal) }) bayesplot_theme_set(bayesplot::theme_default()) color_scheme_set() # Visual tests ------------------------------------------------------------ test_that("color_scheme_view renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") color_scheme_set() p_default <- color_scheme_view() vdiffr::expect_doppelganger("color_scheme_view (default)", p_default) p_red <- color_scheme_view("red") vdiffr::expect_doppelganger("color_scheme_view (scheme specified)", p_red) p_mix <- color_scheme_view("mix-red-blue") vdiffr::expect_doppelganger("color_scheme_view (mixed scheme)", p_mix) p_brewer <- color_scheme_view("brewer-Spectral") vdiffr::expect_doppelganger("color_scheme_view (brewer palette)", p_brewer) color_scheme_set() }) bayesplot_theme_set(bayesplot::theme_default()) color_scheme_set() bayesplot/tests/testthat/test-ppc-errors.R0000644000176200001440000001044514556465326020476 0ustar liggesuserslibrary(bayesplot) context("PPC: predictive errors") source(test_path("data-for-ppc-tests.R")) test_that("ppc_error_hist and ppc_error_scatter return ggplot object", { expect_gg(ppc_error_hist(y, yrep[1:5, ], binwidth = 0.1)) expect_gg(ppc_error_scatter(y, yrep[1:5, ])) expect_gg(ppc_error_hist(y, yrep[1,, drop = FALSE], binwidth = 0.1)) expect_gg(ppc_error_scatter(y, yrep[1,, drop = FALSE])) expect_gg(ppc_error_hist(y2, yrep2, binwidth = 0.1)) expect_gg(ppc_error_scatter(y2, yrep2)) }) test_that("ppc_error_hist_grouped returns ggplot object", { expect_gg(ppc_error_hist_grouped(y, yrep[1:5, ], group, binwidth = 0.1)) expect_gg(ppc_error_hist_grouped(y, yrep[1,, drop = FALSE], group, freq = FALSE, binwidth = 1)) }) test_that("ppc_error_scatter_avg returns ggplot2 object", { expect_gg(ppc_error_scatter_avg(y, yrep)) expect_gg(ppc_error_scatter_avg(y, yrep[1:5, ])) }) test_that("ppc_error_scatter_avg same as ppc_error_scatter if nrow(yrep) = 1", { p1 <- ppc_error_scatter_avg(y2, yrep2) p2 <- ppc_error_scatter(y2, yrep2) d1 <- p1$data d2 <- p2$data # really only a few columns are _exactly_ the same cols <- c("y_id", "y_obs", "value") expect_equal(d1[, cols], d2[, cols]) }) test_that("ppc_error_scatter_avg_vs_x returns ggplot2 object", { expect_gg(ppc_error_scatter_avg_vs_x(y, yrep, x = rnorm(length(y)))) expect_gg(ppc_error_scatter_avg_vs_x(y, yrep[1:5, ], x = rnorm(length(y)))) }) test_that("ppc_error_binned returns ggplot object", { load(test_path("data-for-binomial.rda")) expect_gg(ppc_error_binned(y, Ey)) expect_gg(ppc_error_binned(y[1:5], Ey[, 1:5])) expect_gg(ppc_error_binned(rep(y, 2), cbind(Ey, Ey))) }) test_that("bin_errors works for edge cases", { ans <- data.frame( ey_bar = c(1, NaN), err_bar = c(0, NaN), se2 = c(0, NaN), bin = c(1, 2) ) val <- bin_errors(rep(1, 10), rep(0, 10), bins = 1) expect_equal(ans, val) }) # Visual tests ----------------------------------------------------------------- test_that("ppc_error_hist renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_error_hist(vdiff_y, vdiff_yrep[1:3, ]) vdiffr::expect_doppelganger("ppc_error_hist (default)", p_base) }) test_that("ppc_error_hist_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_error_hist_grouped(vdiff_y, vdiff_yrep[1:3, ], vdiff_group) vdiffr::expect_doppelganger("ppc_error_hist_grouped (default)", p_base) }) test_that("ppc_error_scatter renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_error_scatter(vdiff_y, vdiff_yrep[1:3, ]) vdiffr::expect_doppelganger("ppc_error_scatter (default)", p_base) }) test_that("ppc_error_scatter_avg renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_error_scatter_avg(vdiff_y, vdiff_yrep) vdiffr::expect_doppelganger("ppc_error_scatter_avg (default)", p_base) }) test_that("ppc_error_scatter_avg_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_error_scatter_avg_grouped(vdiff_y, vdiff_yrep, vdiff_group) vdiffr::expect_doppelganger("ppc_error_scatter_avg_grouped (default)", p_base) }) test_that("ppc_error_scatter_avg_vs_x renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_error_scatter_avg_vs_x(vdiff_y, vdiff_yrep, x = seq_along(vdiff_y)) vdiffr::expect_doppelganger("ppc_error_scatter_avg_vs_x (default)", p_base) }) test_that("ppc_error_binned renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") rbeta2 <- function(n, mu, phi) { a <- mu * phi b <- (1 - mu) * phi rbeta(n, a, b) } set.seed(100) y <- rbeta(50, shape1 = 1, shape2 = 10) four_draws <- structure( c(-2.118, -2.061, -2.069, -2.011, 7.604, 9.720, 9.7186, 10.1888), .Dim = c(4L, 2L), .Dimnames = list( iterations = NULL, parameters = c("(Intercept)", "(phi)") ) ) y_rep <- t(apply(four_draws, 1, function(x) rbeta2(50, plogis(x[1]), x[2]))) p_base <- ppc_error_binned(y, y_rep) vdiffr::expect_doppelganger("ppc_error_binned (default)", p_base) }) bayesplot/tests/testthat/test-ppc-intervals.R0000644000176200001440000001720314333522162021151 0ustar liggesuserslibrary(bayesplot) context("PPC: intervals & ribbon") source(test_path("data-for-ppc-tests.R")) test_that("ppc_intervals returns ggplot object", { expect_gg(ppc_intervals(y, yrep)) expect_gg(ppc_intervals(y, yrep, size = 2, fatten = 1)) expect_gg(ppc_intervals(y, yrep, x = seq(1, 2 * length(y), by = 2))) expect_gg(ppc_intervals(y2, yrep2)) # ppd versions expect_gg(ppd_intervals(yrep, x = seq(1, 2 * length(y), by = 2))) expect_gg(ppd_intervals(yrep2)) }) test_that("ppc_ribbon returns ggplot object", { expect_gg(ppc_ribbon(y, yrep, prob = 0.5)) expect_gg(ppc_ribbon(y, yrep, alpha = 0, size = .5)) expect_gg(ppc_ribbon(y2, yrep2, x = rnorm(length(y2)), prob = 0.5)) # ppd versions expect_gg(ppd_ribbon(yrep, prob = 0.5)) expect_gg(ppd_ribbon(yrep2, x = rnorm(length(y2)), prob = 0.5)) }) y <- rnorm(50) yrep <- matrix(rnorm(500, 0, 2), ncol = 50) x <- rep(1:10, each = 5) grp <- gl(5, 1, length = 50, labels = LETTERS[1:5]) d <- ppc_intervals_data(y, yrep, x = 1:length(y), prob = .9) d_group <- ppc_intervals_data(y, yrep, x, grp) test_that("ppc_intervals_grouped returns ggplot object", { expect_gg(ppc_intervals_grouped(y, yrep, x, grp)) # ppd versions expect_gg(ppd_intervals_grouped(yrep, x, grp)) }) test_that("ppc_ribbon_grouped returns ggplot object", { expect_gg(ppc_ribbon_grouped(y, yrep, x, grp)) expect_gg(ppc_ribbon_grouped(y, yrep, x, grp, facet_args = list(scales = "fixed"))) # ppd versions expect_gg(ppd_ribbon_grouped(yrep, x, grp, facet_args = list(scales = "fixed"))) }) test_that("ppc_intervals_data returns correct structure", { expect_named(d, c("y_id", "y_obs", "x", "outer_width", "inner_width", "ll", "l", "m", "h", "hh")) expect_named(d_group, c("y_id", "y_obs", "group", "x", "outer_width", "inner_width", "ll", "l", "m", "h", "hh")) expect_error( ppc_intervals_data(y, yrep, x = 1:length(y), prob = 0), "prob") expect_error( ppc_intervals_data(y, yrep, x = 1:length(y), prob = 1.01), "prob") expect_error( ppc_intervals_data(y, yrep, x = 1:length(y), prob_outer = 0), "prob_outer") expect_error( ppc_intervals_data(y, yrep, x = 1:length(y), prob_outer = 1.01), "prob_outer") }) test_that("ppd_intervals_data + y_obs column same as ppc_intervals_data", { d2 <- ppd_intervals_data(yrep, x = 1:length(y), prob = .9) d_group2 <- ppd_intervals_data(yrep, x, grp) expect_equal(tibble::add_column(d2, y_obs = d$y_obs, .after = "y_id"), d) expect_equal(tibble::add_column(d_group2, y_obs = d_group$y_obs, .after = "y_id"), d_group) }) test_that("ppc_intervals_data does math correctly", { d <- ppc_intervals_data(y, yrep, prob = .4, prob_outer = .8) qs <- unname(quantile(yrep[, 1], c(.1, .3, .5, .7, .9))) expect_equal(d$ll[1], qs[1]) expect_equal(d$l[1], qs[2]) expect_equal(d$m[1], qs[3]) expect_equal(d$h[1], qs[4]) expect_equal(d$hh[1], qs[5]) # Testing groups and known quantiles y <- rep(10, 4) group <- c("a", "a", "b", "b") yrep_g1 <- matrix(rep((0:20), 2), ncol = 2) yrep_g2 <- yrep_g1 - 10 yrep <- cbind(yrep_g1, yrep_g2) d <- ppc_intervals_data(y, yrep, group = group, prob = .5, prob_outer = .9) expect_equal(unique(d$inner_width), .5) expect_equal(unique(d$outer_width), .9) expect_equal(d$ll, c( 1, 1, -9, -9)) expect_equal(d$l, c( 5, 5, -5, -5)) expect_equal(d$m, c(10, 10, 0, 0)) expect_equal(d$h, c(15, 15, 5, 5)) expect_equal(d$hh, c(19, 19, 9, 9)) }) # Visual tests ----------------------------------------------------------------- test_that("ppc_intervals renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_intervals(vdiff_y, vdiff_yrep) vdiffr::expect_doppelganger("ppc_intervals (default)", p_base) p_x <- ppc_intervals(vdiff_y, vdiff_yrep, x = vdiff_y) vdiffr::expect_doppelganger("ppc_intervals (x values)", p_x) p_50 <- ppc_intervals(vdiff_y, vdiff_yrep, prob = .50) vdiffr::expect_doppelganger("ppc_intervals (interval width)", p_50) # ppd versions p_base <- ppd_intervals(vdiff_yrep) vdiffr::expect_doppelganger("ppd_intervals (default)", p_base) p_x <- ppd_intervals(vdiff_yrep, x = vdiff_y) vdiffr::expect_doppelganger("ppd_intervals (x values)", p_x) p_50 <- ppd_intervals(vdiff_yrep, prob = .50) vdiffr::expect_doppelganger("ppd_intervals (interval width)", p_50) }) test_that("ppc_intervals_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_intervals_grouped(vdiff_y, vdiff_yrep, group = vdiff_group) vdiffr::expect_doppelganger("ppc_intervals_grouped (default)", p_base) p_x <- ppc_intervals_grouped( y = vdiff_y, yrep = vdiff_yrep, x = vdiff_y, group = vdiff_group) vdiffr::expect_doppelganger("ppc_intervals_grouped (x values)", p_x) # ppd versions p_base <- ppd_intervals_grouped(vdiff_yrep, group = vdiff_group) vdiffr::expect_doppelganger("ppd_intervals_grouped (default)", p_base) p_x <- ppd_intervals_grouped( ypred = vdiff_yrep, x = vdiff_y, group = vdiff_group) vdiffr::expect_doppelganger("ppd_intervals_grouped (x values)", p_x) }) test_that("ppc_ribbon renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_ribbon(vdiff_y, vdiff_yrep) vdiffr::expect_doppelganger("ppc_ribbon (default)", p_base) p_x <- ppc_ribbon(vdiff_y, vdiff_yrep, x = vdiff_y) vdiffr::expect_doppelganger("ppc_ribbon (x values)", p_x) p_50 <- ppc_ribbon(vdiff_y, vdiff_yrep, prob = 0.5) vdiffr::expect_doppelganger("ppc_ribbon (interval width)", p_50) p_line <- ppc_ribbon(vdiff_y, vdiff_yrep, y_draw = "line") vdiffr::expect_doppelganger("ppc_ribbon (y_draw = line)", p_line) p_point <- ppc_ribbon(vdiff_y, vdiff_yrep, y_draw = "point") vdiffr::expect_doppelganger("ppc_ribbon (y_draw = point)", p_point) p_both <- ppc_ribbon(vdiff_y, vdiff_yrep, y_draw = "both") vdiffr::expect_doppelganger("ppc_ribbon (y_draw = both)", p_both) # ppd versions p_base <- ppd_ribbon(vdiff_yrep) vdiffr::expect_doppelganger("ppd_ribbon (default)", p_base) p_x <- ppd_ribbon(vdiff_yrep, x = vdiff_y) vdiffr::expect_doppelganger("ppd_ribbon (x values)", p_x) p_50 <- ppd_ribbon(vdiff_yrep, prob = 0.5) vdiffr::expect_doppelganger("ppd_ribbon (interval width)", p_50) }) test_that("ppc_ribbon_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_ribbon_grouped(vdiff_y, vdiff_yrep, group = vdiff_group) vdiffr::expect_doppelganger("ppc_ribbon_grouped (default)", p_base) p_line <- ppc_ribbon_grouped(vdiff_y, vdiff_yrep, group = vdiff_group, y_draw = "line") vdiffr::expect_doppelganger("ppc_ribbon_grouped (y_draw = line)", p_line) p_point <- ppc_ribbon_grouped(vdiff_y, vdiff_yrep, group = vdiff_group, y_draw = "point") vdiffr::expect_doppelganger("ppc_ribbon_grouped (y_draw = point)", p_point) p_both <- ppc_ribbon_grouped(vdiff_y, vdiff_yrep, group = vdiff_group, y_draw = "both") vdiffr::expect_doppelganger("ppc_ribbon_grouped (y_draw = both)", p_both) p_x <- ppc_ribbon_grouped( y = vdiff_y, yrep = vdiff_yrep, x = vdiff_y, group = vdiff_group) vdiffr::expect_doppelganger("ppc_ribbon_grouped (x values)", p_x) # ppd versions p_base <- ppd_ribbon_grouped(vdiff_yrep, group = vdiff_group) vdiffr::expect_doppelganger("ppd_ribbon_grouped (default)", p_base) p_x <- ppd_ribbon_grouped( ypred = vdiff_yrep, x = vdiff_y, group = vdiff_group) vdiffr::expect_doppelganger("ppd_ribbon_grouped (x values)", p_x) }) bayesplot/tests/testthat/test-ppc-test-statistics.R0000644000176200001440000001756214556465326022340 0ustar liggesuserslibrary(bayesplot) context("PPC: test-statistics") source(test_path("data-for-ppc-tests.R")) q25 <- function(x) quantile(x, 0.25) prop0 <- function(x) mean(x == 0) test_that("ppc_stat throws errors if function not found", { expect_error(ppc_stat(y, yrep, stat = "9999"), "not found") expect_error(ppc_stat_freqpoly(y, yrep, stat = "9999"), "not found") expect_error(ppc_stat_grouped(y, yrep, group, stat = "9999"), "not found") expect_error(ppc_stat_freqpoly_grouped(y, yrep, group, stat = "9999"), "not found") }) test_that("ppc_stat throws errors if 'stat' wrong length", { expect_error(ppc_stat(y, yrep, stat = c("mean", "sd")), "length(stat) == 1 is not TRUE", fixed = TRUE) expect_error(ppc_stat_grouped(y, yrep, group, stat = c("mean", "sd")), "length(stat) == 1 is not TRUE", fixed = TRUE) expect_error(ppc_stat_freqpoly(y, yrep, stat = c("mean", "sd")), "length(stat) == 1 is not TRUE", fixed = TRUE) expect_error(ppc_stat_freqpoly_grouped(y, yrep, group, stat = c(mean, sd)), "length(stat) == 1 is not TRUE", fixed = TRUE) }) test_that("ppc_stat returns ggplot object", { expect_gg(ppc_stat(y, yrep, binwidth = 0.05)) expect_gg(ppc_stat(y, yrep, stat = "sd", binwidth = 0.05)) expect_gg(ppc_stat(y, yrep, stat = sd, binwidth = 0.05)) expect_gg(ppc_stat(y, yrep, stat = "q25", binwidth = 0.05)) expect_gg(ppc_stat(y, yrep, stat = q25, binwidth = 0.05)) expect_gg(ppc_stat(y, yrep, stat = function(x) median(x), binwidth = 0.05)) expect_gg(ppc_stat(y2, yrep2, binwidth = 0.05)) expect_gg(ppc_stat(y2, yrep2, stat = "prop0", binwidth = 0.05)) # ppd versions expect_gg(ppd_stat(yrep, stat = "q25", binwidth = 0.05)) expect_gg(ppd_stat(yrep, stat = q25, binwidth = 0.05)) expect_gg(ppd_stat(yrep2, stat = "prop0", binwidth = 0.05)) }) test_that("ppc_stat_2d returns ggplot object", { expect_gg(ppc_stat_2d(y, yrep)) expect_gg(ppc_stat_2d(y, yrep, stat = c("q25", "median"))) expect_gg(ppc_stat_2d(y, yrep, stat = c("q25", median))) expect_gg(ppc_stat_2d(y, yrep, stat = c(function(x) mean(x), function(y) sd(y)))) expect_gg(ppc_stat_2d(y2, yrep2)) # ppd versions expect_gg(ppd_stat_2d(yrep, stat = c("q25", median))) expect_gg(ppd_stat_2d(yrep, stat = c(function(x) mean(x), function(y) sd(y)))) expect_gg(ppd_stat_2d(yrep2)) }) test_that("ppc_stat_2d errors if more than 2 stats", { expect_error(ppc_stat_2d(y, yrep, stat = c("mean", "sd", "var")), "argument must have length 2") }) test_that("ppc_stat_grouped returns ggplot object", { expect_gg(ppc_stat_grouped(y, yrep, group, binwidth = 0.05)) expect_gg(ppc_stat_grouped(y, yrep, as.numeric(group), stat = function(z) var(z), binwidth = 0.05)) expect_gg(ppc_stat_grouped(y, yrep, as.integer(group), stat = "sd", binwidth = 0.05)) }) test_that("ppc_stat_freqpoly_grouped returns ggplot object", { expect_gg(ppc_stat_freqpoly_grouped(y, yrep, group, stat = "sd", freq = FALSE, binwidth = 0.05)) expect_gg(ppc_stat_freqpoly_grouped(y, yrep, group, stat = function(x) sd(x), freq = TRUE, binwidth = 0.05)) # ppd version expect_gg(ppd_stat_freqpoly_grouped(yrep, group, stat = "sd", freq = FALSE, binwidth = 0.05)) }) test_that("ppc_stat_data without the y values equal to ppd_stat_data", { d <- ppc_stat_data(y, yrep, group, stat = "median") d2 <- ppd_stat_data(yrep, group, stat = median) expect_equal(d$value[d$variable != "y"], d2$value) expect_equal(d$group[d$variable != "y"], d2$group) # with 2 stats d <- ppc_stat_data(y, yrep, group, stat = c(mean, median)) d2 <- ppd_stat_data(yrep, group, stat = c("mean", "median")) expect_equal(d$value[d$variable != "y"], d2$value) expect_equal(d$value2[d$variable != "y"], d2$value2) expect_equal(d$group[d$variable != "y"], d2$group) }) test_that("ppc_stat_data and ppd_stat_data throw correct errors", { expect_error(ppc_stat_data(y, yrep, stat = letters), "'stat' must have length 1 or 2") expect_error(ppd_stat_data(yrep, stat = letters), "'stat' must have length 1 or 2") expect_error(ppd_stat_data(yrep, stat = "not_a_known_function"), "object 'not_a_known_function' of mode 'function' was not found") }) # Visual tests ------------------------------------------------------------ test_that("ppc_stat renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_stat(vdiff_y, vdiff_yrep, binwidth = 0.05) + yaxis_text() vdiffr::expect_doppelganger("ppc_stat (default)", p_base) p_custom <- ppc_stat( y = vdiff_y, yrep = vdiff_yrep, stat = "mad", binwidth = 0.05, freq = FALSE ) + yaxis_text() vdiffr::expect_doppelganger( title = "ppc_stat (stat, binwidth, freq)", fig = p_custom) # ppd versions p_base <- ppd_stat(vdiff_yrep, binwidth = 0.05) + yaxis_text() vdiffr::expect_doppelganger("ppd_stat (default)", p_base) p_custom <- ppd_stat( ypred = vdiff_yrep, stat = "mad", binwidth = 0.05, freq = FALSE ) + yaxis_text() vdiffr::expect_doppelganger( title = "ppd_stat (stat, binwidth, freq)", fig = p_custom) }) test_that("ppc_stat_2d renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_stat_2d(vdiff_y, vdiff_yrep) vdiffr::expect_doppelganger("ppc_stat_2d (default)", p_base) p_custom <- ppc_stat_2d( y = vdiff_y, yrep = vdiff_yrep, stat = c("median", "mad"), size = 5, alpha = 1 ) vdiffr::expect_doppelganger( title = "ppc_stat_2d (stat, size, alpha)", fig = p_custom) # ppd versions p_base <- ppd_stat_2d(vdiff_yrep) vdiffr::expect_doppelganger("ppd_stat_2d (default)", p_base) p_custom <- ppd_stat_2d( ypred = vdiff_yrep, stat = c("median", "mad"), size = 5, alpha = 1 ) vdiffr::expect_doppelganger( title = "ppd_stat_2d (stat, size, alpha)", fig = p_custom) }) test_that("ppc_stat_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_stat_grouped(vdiff_y, vdiff_yrep, vdiff_group, binwidth = 0.05) vdiffr::expect_doppelganger("ppc_stat_grouped (default)", p_base) p_custom <- ppc_stat_grouped( y = vdiff_y, yrep = vdiff_yrep, group = vdiff_group, stat = stats::var, facet_args = list(scales = "fixed", ncol = 1), binwidth = 0.25 ) vdiffr::expect_doppelganger( title = "ppc_stat_grouped (stat, facet_args, binwidth)", fig = p_custom) # ppd versions p_base <- ppd_stat_grouped(vdiff_yrep, vdiff_group, binwidth = 0.05) vdiffr::expect_doppelganger("ppd_stat_grouped (default)", p_base) p_custom <- ppd_stat_grouped( ypred = vdiff_yrep, group = vdiff_group, stat = stats::var, facet_args = list(scales = "fixed", ncol = 1), binwidth = 0.25 ) vdiffr::expect_doppelganger( title = "ppd_stat_grouped (stat, facet_args, binwidth)", fig = p_custom) }) test_that("ppc_stat_freqpoly_grouped renders correctly", { testthat::skip_on_cran() testthat::skip_if_not_installed("vdiffr") p_base <- ppc_stat_freqpoly_grouped(vdiff_y, vdiff_yrep, vdiff_group, binwidth = 0.05) vdiffr::expect_doppelganger("ppc_stat_freqpoly_grouped (default)", p_base) p_custom <- ppc_stat_freqpoly_grouped( y = vdiff_y, yrep = vdiff_yrep, group = vdiff_group, stat = "sum", facet_args = list(scales = "fixed", ncol = 1), binwidth = 0.5 ) vdiffr::expect_doppelganger( title = "ppc_stat_freqpoly_grouped (stat, facets, bw)", fig = p_custom) # ppd versions p_base <- ppd_stat_freqpoly_grouped(vdiff_yrep, vdiff_group, binwidth = 0.05) vdiffr::expect_doppelganger("ppd_stat_freqpoly_grouped (default)", p_base) p_custom <- ppd_stat_freqpoly_grouped( ypred = vdiff_yrep, group = vdiff_group, stat = "sum", facet_args = list(scales = "fixed", ncol = 1), binwidth = 0.5 ) vdiffr::expect_doppelganger( title = "ppd_stat_freqpoly_grouped (stat, facets, bw)", fig = p_custom) }) bayesplot/tests/testthat.R0000644000176200001440000000037214127610044015402 0ustar liggesuserslibrary(testthat) library(bayesplot) Sys.unsetenv("R_TESTS") test_check("bayesplot") # Alternative interactive tester that doesn't bail after 24 failures # pr <- testthat::ProgressReporter$new() # pr$max_fail = 1000 # devtools::test(reporter = pr) bayesplot/vignettes/0000755000176200001440000000000014563223213014265 5ustar liggesusersbayesplot/vignettes/plotting-mcmc-draws.Rmd0000644000176200001440000003043414556465326020647 0ustar liggesusers--- title: "Plotting MCMC draws using the bayesplot package" author: "Jonah Gabry" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") vignette: > %\VignetteIndexEntry{Plotting MCMC draws} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, child="children/SETTINGS-knitr.txt"} ``` ```{r pkgs, include=FALSE} library("ggplot2") library("rstanarm") ``` ## Introduction This vignette focuses on plotting parameter estimates from MCMC draws. MCMC diagnostic plots are covered in the separate vignette [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html), and graphical posterior predictive model checking is covered in the vignette [_Graphical posterior predictive checks_](https://mc-stan.org/bayesplot/articles/graphical-ppcs.html). ### Setup In addition to __bayesplot__ we'll load the following packages: * __ggplot2__, in case we want to customize the ggplot objects created by __bayesplot__ * __rstanarm__, for fitting the example models used throughout the vignette ```{r, eval=FALSE} library("bayesplot") library("ggplot2") library("rstanarm") ``` ### Example model The **bayesplot** package provides various plotting functions for visualizing Markov chain Monte Carlo (MCMC) draws from the posterior distribution of the parameters of a Bayesian model. In this vignette we demonstrate a few of these functions. Example usage of the functions not demonstrated here can be found in the package documentation. For demonstration we will use draws obtained using the `stan_glm` function in the **rstanarm** package (Gabry and Goodrich, 2017), but MCMC draws from using any package can be used with the functions in the **bayesplot** package. See, for example, **brms**, which, like **rstanarm**, calls the **rstan** package internally to use [Stan](https://mc-stan.org/)'s MCMC sampler. ```{r mtcars} head(mtcars) # see help("mtcars") ``` ```{r, eval=FALSE} # linear regression model using stan_glm # using '~ .' to include all variables fit <- stan_glm(mpg ~ ., data = mtcars, seed = 1111) print(fit) ``` ```{r stan_glm, include=FALSE} fit <- stan_glm(mpg ~ ., data = mtcars, QR = TRUE, seed = 1111) ``` ```{r print-fit, echo=FALSE} print(fit) ``` To use the posterior draws with the functions in the **bayesplot** package we'll extract them from the fitted model object: ```{r get-draws} posterior <- as.array(fit) dim(posterior) dimnames(posterior) ``` We've used `as.array` above (as opposed to `as.matrix`) because it keeps the Markov chains separate (`stan_glm` runs four chains by default). Most of the plots don't actually need the chains to be separate, but for a few of the plots we make in this vignette we'll want to show the chains individually.
## Posterior uncertainty intervals For models fit using MCMC we can compute posterior uncertainty intervals (sometimes called "credible intervals") in various ways. **bayesplot** currently provides plots of central intervals based on quantiles, although additional options may be provided in future releases (e.g., HDIs, which can be useful in particular cases). **Documentation:** * `help("MCMC-intervals")` * [mc-stan.org/bayesplot/reference/MCMC-intervals](https://mc-stan.org/bayesplot/reference/MCMC-intervals.html) ------ #### mcmc_intervals, mcmc_areas Central posterior uncertainty intervals can be plotted using the `mcmc_intervals` function. ```{r mcmc_intervals} color_scheme_set("red") mcmc_intervals(posterior, pars = c("cyl", "drat", "am", "sigma")) ``` The default is to show 50% intervals (the thick segments) and 90% intervals (the thinner outer lines). These defaults can be changed using the `prob` and `prob_outer` arguments, respectively. The points in the above plot are posterior medians. The `point_est` argument can be used to select posterior means instead or to omit the point estimates. To show the uncertainty intervals as shaded areas under the estimated posterior density curves we can use the `mcmc_areas` function. ```{r mcmc_areas} mcmc_areas( posterior, pars = c("cyl", "drat", "am", "sigma"), prob = 0.8, # 80% intervals prob_outer = 0.99, # 99% point_est = "mean" ) ```
## Univariate marginal posterior distributions **bayesplot** provides functions for looking at histograms or kernel density estimates of marginal posterior distributions, either with all Markov chains combined or with the chains separate. **Documentation:** * `help("MCMC-distributions")` * [mc-stan.org/bayesplot/reference/MCMC-distributions](https://mc-stan.org/bayesplot/reference/MCMC-distributions.html) ------ #### mcmc_hist The `mcmc_hist` function plots marginal posterior distributions (combining all chains): ```{r mcmc_hist, message=FALSE} color_scheme_set("green") mcmc_hist(posterior, pars = c("wt", "sigma")) ``` If we want to plot `log(sigma)` rather than `sigma` we can either transform the draws in advance or use the `transformations` argument. ```{r mcmc_hist-transform, message=FALSE} color_scheme_set("blue") mcmc_hist(posterior, pars = c("wt", "sigma"), transformations = list("sigma" = "log")) ``` Most of the other functions for plotting MCMC draws also have a `transformations` argument. #### mcmc_hist_by_chain To view separate histograms of each of the four Markov chains we can use `mcmc_hist_by_chain`, which plots each chain in a separate facet in the plot. ```{r mcmc_hist_by_chain, message=FALSE} color_scheme_set("brightblue") mcmc_hist_by_chain(posterior, pars = c("wt", "sigma")) ``` #### mcmc_dens The `mcmc_dens` function is similar to `mcmc_hist` but plots kernel density estimates instead of histograms. ```{r mcmc_dens, message=FALSE} color_scheme_set("purple") mcmc_dens(posterior, pars = c("wt", "sigma")) ``` #### mcmc_dens_overlay Like `mcmc_hist_by_chain`, the `mcmc_dens_overlay` function separates the Markov chains. But instead of plotting each chain individually, the density estimates are overlaid. ```{r mcmc_dens_overlay, message=FALSE} mcmc_dens_overlay(posterior, pars = c("wt", "sigma")) ``` #### mcmc_violin The `mcmc_violin` function plots the density estimates of each chain as violins and draws horizontal line segments at user-specified quantiles. ```{r mcmc_violin} color_scheme_set("teal") mcmc_violin(posterior, pars = c("wt", "sigma"), probs = c(0.1, 0.5, 0.9)) ```
## Bivariate plots Various functions are available for plotting bivariate marginal posterior distributions. Some of these functions also take optional arguments for adding MCMC diagnostic information to the plots. That additional functionality is discussed in the separate [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette. **Documentation:** * `help("MCMC-scatterplots")` * [mc-stan.org/bayesplot/reference/MCMC-scatterplots](https://mc-stan.org/bayesplot/reference/MCMC-scatterplots.html) ------ #### mcmc_scatter The `mcmc_scatter` function creates a simple scatterplot of two parameters. ```{r mcmc_scatter} color_scheme_set("gray") mcmc_scatter(posterior, pars = c("(Intercept)", "wt"), size = 1.5, alpha = 0.5) ``` #### mcmc_hex The `mcmc_hex` function creates a similar plot but using hexagonal binning, which can be useful to avoid overplotting. ```{r mcmc_hex} # requires hexbin package if (requireNamespace("hexbin", quietly = TRUE)) { mcmc_hex(posterior, pars = c("(Intercept)", "wt")) } ``` #### mcmc_pairs In addition to `mcmc_scatter` and `mcmc_hex`, __bayesplot__ now provides an `mcmc_pairs` function for creating pairs plots with more than two parameters. ```{r mcmc_pairs, message=FALSE} color_scheme_set("pink") mcmc_pairs(posterior, pars = c("(Intercept)", "wt", "sigma"), off_diag_args = list(size = 1.5)) ``` The univariate marginal posteriors are shown along the diagonal as histograms, but this can be changed to densities by setting `diag_fun="dens"`. Bivariate plots are displayed above and below the diagonal as scatterplots, but it is also possible to use hex plots by setting `off_diag_fun="hex"`. By default, `mcmc_pairs` shows some of the Markov chains (half, if an even number of chains) above the diagonal and the others below. There are many more options for controlling how the draws should be split between the plots above and below the diagonal (see the documentation for the `condition` argument), but they are more useful when MCMC diagnostic information is included. This is discussed in the [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette.
## Trace plots Trace plots are time series plots of Markov chains. In this vignette we show the standard trace plots that **bayesplot** can make. For models fit using any Stan interface (or Hamiltonian Monte Carlo in general), the [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette provides an example of also adding information about divergences to trace plots. **Documentation:** * `help("MCMC-traces")` * [mc-stan.org/bayesplot/reference/MCMC-traces](https://mc-stan.org/bayesplot/reference/MCMC-traces.html) ------- #### mcmc_trace The `mcmc_trace` function creates standard trace plots: ```{r mcmc_trace} color_scheme_set("blue") mcmc_trace(posterior, pars = c("wt", "sigma")) ``` If it's hard to see the difference between the chains we can change to a mixed color scheme, for example: ```{r change-scheme} color_scheme_set("mix-blue-red") mcmc_trace(posterior, pars = c("wt", "sigma"), facet_args = list(ncol = 1, strip.position = "left")) ``` The code above also illustrates the use of the `facet_args` argument, which is a list of parameters passed to `facet_wrap` in __ggplot2__. Specifying `ncol=1` means the trace plots will be stacked in a single column rather than placed side by side, and `strip.position="left"` moves the facet labels to the y-axis (instead of above each facet). The [`"viridis"` color scheme](https://CRAN.R-project.org/package=viridis) is also useful for trace plots because it is comprised of very distinct colors: ```{r viridis-scheme, eval=FALSE} color_scheme_set("viridis") mcmc_trace(posterior, pars = "(Intercept)") ``` #### mcmc_trace_highlight The `mcmc_trace_highlight` function uses points instead of lines and reduces the opacity of all but a single chain (which is specified using the `highlight` argument). ```{r mcmc_trace_highlight} mcmc_trace_highlight(posterior, pars = "sigma", highlight = 3) ```
## References Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.15.3. https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. \doi:10.1111/rssa.12378. ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [arXiv preprint](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper)) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third edition. Stan Development Team. (2017). *Stan Modeling Language Users Guide and Reference Manual*. https://mc-stan.org/users/documentation/ bayesplot/vignettes/graphical-ppcs.Rmd0000644000176200001440000003634314335264530017643 0ustar liggesusers--- title: "Graphical posterior predictive checks using the bayesplot package" author: "Jonah Gabry" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") vignette: > %\VignetteIndexEntry{Graphical posterior predictive checks} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, child="children/SETTINGS-knitr.txt"} ``` ```{r pkgs, include=FALSE} library("ggplot2") library("rstanarm") set.seed(840) ``` ## Introduction This vignette focuses on graphical posterior predictive checks (PPC). Plots of parameter estimates from MCMC draws are covered in the separate vignette [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html), and MCMC diagnostics are covered in the [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette. ### Graphical posterior predictive checks (PPCs) The **bayesplot** package provides various plotting functions for _graphical posterior predictive checking_, that is, creating graphical displays comparing observed data to simulated data from the posterior predictive distribution ([Gabry et al, 2019](#gabry2019)). The idea behind posterior predictive checking is simple: if a model is a good fit then we should be able to use it to generate data that looks a lot like the data we observed. To generate the data used for posterior predictive checks (PPCs) we simulate from the _posterior predictive distribution_. This is the distribution of the outcome variable implied by a model after using the observed data $y$ (a vector of $N$ outcome values) to update our beliefs about unknown model parameters $\theta$. The posterior predictive distribution for observation $\widetilde{y}$ can be written as $$p(\widetilde{y} \,|\, y) = \int p(\widetilde{y} \,|\, \theta) \, p(\theta \,|\, y) \, d\theta.$$ Typically we will also condition on $X$ (a matrix of predictor variables). For each draw (simulation) $s = 1, \ldots, S$ of the parameters from the posterior distribution, $\theta^{(s)} \sim p(\theta \,|\, y)$, we draw an entire vector of $N$ outcomes $\widetilde{y}^{(s)}$ from the posterior predictive distribution by simulating from the data model conditional on parameters $\theta^{(s)}$. The result is an $S \times N$ matrix of draws $\widetilde{y}$. When simulating from the posterior predictive distribution we can use either the same values of the predictors $X$ that we used when fitting the model or new observations of those predictors. When we use the same values of $X$ we denote the resulting simulations by $y^{rep}$, as they can be thought of as replications of the outcome $y$ rather than predictions for future observations ($\widetilde{y}$ using predictors $\widetilde{X}$). This corresponds to the notation from Gelman et al. (2013) and is the notation used throughout the package documentation. Using the replicated datasets drawn from the posterior predictive distribution, the functions in the **bayesplot** package create various graphical displays comparing the observed data $y$ to the replications. The names of the **bayesplot** plotting functions for posterior predictive checking all have the prefix `ppc_`. ### Setup In addition to **bayesplot** we'll load the following packages: * __ggplot2__, in case we want to customize the ggplot objects created by __bayesplot__ * __rstanarm__, for fitting the example models used throughout the vignette ```{r, eval=FALSE} library("bayesplot") library("ggplot2") library("rstanarm") ``` ### Example models To demonstrate some of the various PPCs that can be created with the **bayesplot** package we'll use an example of comparing Poisson and Negative binomial regression models from one of the **rstanarm** [package vignettes](https://mc-stan.org/rstanarm/articles/count.html) (Gabry and Goodrich, 2017). > We want to make inferences about the efficacy of a certain pest management system at reducing the number of roaches in urban apartments. [...] The regression predictors for the model are the pre-treatment number of roaches `roach1`, the treatment indicator `treatment`, and a variable `senior` indicating whether the apartment is in a building restricted to elderly residents. Because the number of days for which the roach traps were used is not the same for all apartments in the sample, we include it as an exposure [...]. First we fit a Poisson regression model with outcome variable `y` representing the roach count in each apartment at the end of the experiment. ```{r roaches-data} head(roaches) # see help("rstanarm-datasets") roaches$roach100 <- roaches$roach1 / 100 # pre-treatment number of roaches (in 100s) ``` ```{r roaches-model-pois, message=FALSE} # using rstanarm's default priors. For details see the section on default # weakly informative priors at https://mc-stan.org/rstanarm/articles/priors.html fit_poisson <- stan_glm( y ~ roach100 + treatment + senior, offset = log(exposure2), family = poisson(link = "log"), data = roaches, seed = 1111, refresh = 0 # suppresses all output as of v2.18.1 of rstan ) ``` ```{r print-pois} print(fit_poisson) ``` We'll also fit the negative binomial model that we'll compare to the Poisson: ```{r roaches-model-nb, message=FALSE} fit_nb <- update(fit_poisson, family = "neg_binomial_2") ``` ```{r print-nb} print(fit_nb) ``` ### Defining `y` and `yrep` In order to use the PPC functions from the **bayesplot** package we need a vector `y` of outcome values, ```{r y} y <- roaches$y ``` and a matrix `yrep` of draws from the posterior predictive distribution, ```{r yrep} yrep_poisson <- posterior_predict(fit_poisson, draws = 500) yrep_nb <- posterior_predict(fit_nb, draws = 500) dim(yrep_poisson) dim(yrep_nb) ``` Each row of the matrix is a draw from the posterior predictive distribution, i.e. a vector with one element for each of the data points in `y`. Since we fit the models using __rstanarm__ we used its special `posterior_predict` function, but if we were using a model fit with the __rstan__ package we could create `yrep` in the `generated quantities` block of the Stan program or by doing simulations in R after fitting the model. Draws from the posterior predictive distribution can be used with **bayesplot** regardless of whether or not the model was fit using an interface to Stan. **bayesplot** just requires a `yrep` matrix that has `number_of_draws` rows and `number_of_observations` columns.
## Histograms and density estimates #### ppc_dens_overlay The first PPC we'll look at is a comparison of the distribution of `y` and the distributions of some of the simulated datasets (rows) in the `yrep` matrix. ```{r ppc_dens_overlay} color_scheme_set("brightblue") ppc_dens_overlay(y, yrep_poisson[1:50, ]) ``` In the plot above, the dark line is the distribution of the observed outcomes `y` and each of the 50 lighter lines is the kernel density estimate of one of the replications of `y` from the posterior predictive distribution (i.e., one of the rows in `yrep`). This plot makes it easy to see that this model fails to account for the large proportion of zeros in `y`. That is, the model predicts fewer zeros than were actually observed. To see the discrepancy at the lower values of more clearly we can use the `xlim` function from **ggplot2** to restrict the range of the x-axis: ```{r ppc_dens_overlay-2, message=FALSE, warning=FALSE} ppc_dens_overlay(y, yrep_poisson[1:50, ]) + xlim(0, 150) ``` See Figure 6 in [Gabry et al. (2019)](#gabry2019) for another example of using `ppc_dens_overlay`. #### ppc_hist We could see the same thing from a different perspective by looking at separate histograms of `y` and some of the `yrep` datasets using the `ppc_hist` function: ```{r ppc_hist, message=FALSE} ppc_hist(y, yrep_poisson[1:5, ]) ``` The same plot for the negative binomial model looks much different: ```{r ppc_hist-nb, message=FALSE} ppc_hist(y, yrep_nb[1:5, ]) ``` The negative binomial model does better handling the number of zeros in the data, but it occasionally predicts values that are way too large, which is why the x-axes extend to such high values in the plot and make it difficult to read. To see the predictions for the smaller values more clearly we can zoom in: ```{r ppc_hist-nb-2, message=FALSE} ppc_hist(y, yrep_nb[1:5, ], binwidth = 20) + coord_cartesian(xlim = c(-1, 300)) ```
## Distributions of test statistics Another way to see that the Poisson model predicts too few zeros is to look at the distribution of the proportion of zeros over the replicated datasets from the posterior predictive distribution in `yrep` and compare to the proportion of observed zeros in `y`. #### ppc_stat First we define a function that takes a vector as input and returns the proportion of zeros: ```{r prop_zero} prop_zero <- function(x) mean(x == 0) prop_zero(y) # check proportion of zeros in y ``` The `stat` argument to `ppc_stat` accepts a function or the name of a function for computing a test statistic from a vector of data. In our case we can specify `stat = "prop_zero"` since we've already defined the `prop_zero` function, but we also could have used `stat = function(x) mean(x == 0)`. ```{r ppc_stat, message=FALSE} ppc_stat(y, yrep_poisson, stat = "prop_zero", binwidth = 0.005) ``` The dark line is at the value $T(y)$, i.e. the value of the test statistic computed from the observed $y$, in this case `prop_zero(y)`. The lighter area on the left is actually a histogram of the proportion of zeros in in the `yrep` simulations, but it can be hard to see because almost none of the simulated datasets in `yrep` have any zeros. Here's the same plot for the negative binomial model: ```{r ppc_stat-nb, message=FALSE} ppc_stat(y, yrep_nb, stat = "prop_zero") ``` Again we see that the negative binomial model does a much better job predicting the proportion of observed zeros than the Poisson. However, if we look instead at the distribution of the maximum value in the replications, we can see that the Poisson model makes more realistic predictions than the negative binomial: ```{r ppc_stat-max, message=FALSE} ppc_stat(y, yrep_poisson, stat = "max") ppc_stat(y, yrep_nb, stat = "max") ppc_stat(y, yrep_nb, stat = "max", binwidth = 100) + coord_cartesian(xlim = c(-1, 5000)) ``` See Figure 7 in [Gabry et al. (2019)](#gabry2019) for another example of using `ppc_stat`.
## Other PPCs and PPCs by group There are many additional PPCs available, including plots of predictive intervals, distributions of predictive errors, and more. For links to the documentation for all of the various PPC plots see `help("PPC-overview")` from R or the [online documentation](https://mc-stan.org/bayesplot/reference/index.html#section-ppc) on the Stan website. The `available_ppc` function can also be used to list the names of all PPC plotting functions: ```{r available_ppc} available_ppc() ``` Many of the available PPCs can also be carried out within levels of a grouping variable. Any function for PPCs by group will have a name ending in `_grouped` and will accept an additional argument `group`. The full list of currently available `_grouped` functions is: ```{r available_ppc-grouped} available_ppc(pattern = "_grouped") ``` #### ppc_stat_grouped For example, `ppc_stat_grouped` is the same as `ppc_stat` except that the test statistic is computed within levels of the grouping variable and a separate plot is made for each level: ```{r ppc_stat_grouped, message=FALSE} ppc_stat_grouped(y, yrep_nb, group = roaches$treatment, stat = "prop_zero") ``` See Figure 8 in [Gabry et al. (2019)](#gabry2019) for another example of using `ppc_stat_grouped`.
## Providing an interface to bayesplot PPCs from another package The **bayesplot** package provides the S3 generic function `pp_check`. Authors of R packages for Bayesian inference are encouraged to define methods for the fitted model objects created by their packages. This will hopefully be convenient for both users and developers and contribute to the use of the same naming conventions across many of the R packages for Bayesian data analysis. To provide an interface to **bayesplot** from your package, you can very easily define a `pp_check` method (or multiple `pp_check` methods) for the fitted model objects created by your package. All a `pp_check` method needs to do is provide the `y` vector and `yrep` matrix arguments to the various plotting functions included in **bayesplot**. ### Defining a `pp_check` method Here is an example for how to define a simple `pp_check` method in a package that creates fitted model objects of class `"foo"`. We will define a method `pp_check.foo` that extracts the data `y` and the draws from the posterior predictive distribution `yrep` from an object of class `"foo"` and then calls one of the plotting functions from **bayesplot**. Suppose that objects of class `"foo"` are lists with named components, two of which are `y` and `yrep`. Here's a simple method `pp_check.foo` that offers the user the option of two different plots: ```{r pp_check.foo} # @param object An object of class "foo". # @param type The type of plot. # @param ... Optional arguments passed on to the bayesplot plotting function. pp_check.foo <- function(object, type = c("multiple", "overlaid"), ...) { type <- match.arg(type) y <- object[["y"]] yrep <- object[["yrep"]] stopifnot(nrow(yrep) >= 50) samp <- sample(nrow(yrep), size = ifelse(type == "overlaid", 50, 5)) yrep <- yrep[samp, ] if (type == "overlaid") { ppc_dens_overlay(y, yrep, ...) } else { ppc_hist(y, yrep, ...) } } ``` To try out `pp_check.foo` we can just make a list with `y` and `yrep` components and give it class `foo`: ```{r foo-object} x <- list(y = rnorm(200), yrep = matrix(rnorm(1e5), nrow = 500, ncol = 200)) class(x) <- "foo" ``` ```{r pp_check-1, message=FALSE} color_scheme_set("purple") pp_check(x, type = "multiple", binwidth = 0.3) ``` ```{r pp_check-2} color_scheme_set("darkgray") pp_check(x, type = "overlaid") ``` ### Examples of `pp_check` methods in other packages Several packages currently use this approach to provide an interface to **bayesplot**'s graphical posterior predictive checks. See, for example, the `pp_check` methods in the [**rstanarm**](https://CRAN.R-project.org/package=rstanarm) and [**brms**](https://CRAN.R-project.org/package=brms) packages.
## References Buerkner, P. (2017). brms: Bayesian Regression Models using Stan. R package version 1.7.0. https://CRAN.R-project.org/package=brms Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.15.3. https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. \doi:10.1111/rssa.12378. ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [arXiv preprint](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper)) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third edition. Stan Development Team. _Stan Modeling Language Users Guide and Reference Manual_. https://mc-stan.org/users/documentation/ bayesplot/vignettes/children/0000755000176200001440000000000014127610044016053 5ustar liggesusersbayesplot/vignettes/children/SETTINGS-knitr.txt0000644000176200001440000000045714127610044021207 0ustar liggesusers```{r, SETTINGS-knitr, include=FALSE} stopifnot(require("knitr")) library("bayesplot") knitr::opts_chunk$set( dev = "png", dpi = 150, fig.asp = 0.618, fig.width = 5, out.width = "60%", fig.align = "center", comment = NA, eval = if (isTRUE(exists("params"))) params$EVAL else FALSE ) ``` bayesplot/vignettes/visual-mcmc-diagnostics.Rmd0000644000176200001440000007745514556270424021510 0ustar liggesusers--- title: "Visual MCMC diagnostics using the bayesplot package" author: "Jonah Gabry and Martin Modrák" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") && !(isTRUE(as.logical(Sys.getenv("CI"))) && .Platform$OS.type == "windows") vignette: > %\VignetteIndexEntry{Visual MCMC diagnostics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, child="children/SETTINGS-knitr.txt"} ``` ```{r pkgs, include=FALSE} library("bayesplot") library("ggplot2") library("rstan") library("dplyr") #Used only for consistency checks rstan_options(auto_write = TRUE) #Helpful throughout development bayesplot_theme_set() ``` ## Introduction This vignette focuses on MCMC diagnostic plots, in particular on diagnosing divergent transitions and on the `n_eff` and `Rhat` statistics that help you determine that the chains have mixed well. Plots of parameter estimates from MCMC draws are covered in the separate vignette [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html), and graphical posterior predictive model checking is covered in the [_Graphical posterior predictive checks_](https://mc-stan.org/bayesplot/articles/graphical-ppcs.html) vignette. Note that most of these plots can also be browsed interactively using the [shinystan](https://mc-stan.org/shinystan/) package. ### Setup In addition to __bayesplot__ we'll load the following packages: * __ggplot2__, in case we want to customize the ggplot objects created by __bayesplot__ * __rstan__, for fitting the example models used throughout the vignette ```{r setup, eval=FALSE} library("bayesplot") library("ggplot2") library("rstan") ``` ### Example model Before we delve into the actual plotting we need to fit a model to have something to work with. In this vignette we'll use the eight schools example, which is discussed in many places, including Rubin (1981), Gelman et al. (2013), and the [RStan Getting Started](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started#how-to-use-rstan) wiki. This is a simple hierarchical meta-analysis model with data consisting of point estimates `y` and standard errors `sigma` from analyses of test prep programs in `J=8` schools. Ideally we would have the full data from each of the previous studies, but in this case we only have the these estimates. ```{r schools_dat} schools_dat <- list( J = 8, y = c(28, 8, -3, 7, -1, 1, 18, 12), sigma = c(15, 10, 16, 11, 9, 11, 10, 18) ) ``` The model is: $$ \begin{align*} y_j &\sim {\rm Normal}(\theta_j, \sigma_j), \quad j = 1,\dots,J \\ \theta_j &\sim {\rm Normal}(\mu, \tau), \quad j = 1, \dots, J \\ \mu &\sim {\rm Normal}(0, 10) \\ \tau &\sim {\rm half-Cauchy}(0, 10), \end{align*} $$ with the normal distribution parameterized by the mean and standard deviation, not the variance or precision. In Stan code: ```{stan stancode1, output.var = "schools_mod_cp"} // Saved in 'schools_mod_cp.stan' data { int J; vector[J] y; vector[J] sigma; } parameters { real mu; real tau; vector[J] theta; } model { mu ~ normal(0, 10); tau ~ cauchy(0, 10); theta ~ normal(mu, tau); y ~ normal(theta, sigma); } ``` This parameterization of the model is referred to as the centered parameterization (CP). We'll also fit the same statistical model but using the so-called non-centered parameterization (NCP), which replaces the vector $\theta$ with a vector $\eta$ of a priori _i.i.d._ standard normal parameters and then constructs $\theta$ deterministically from $\eta$ by scaling by $\tau$ and shifting by $\mu$: $$ \begin{align*} \theta_j &= \mu + \tau \,\eta_j, \quad j = 1,\dots,J \\ \eta_j &\sim N(0,1), \quad j = 1,\dots,J. \end{align*} $$ The Stan code for this model is: ```{stan, stancode2, output.var = "schools_mod_ncp"} // Saved in 'schools_mod_ncp.stan' data { int J; vector[J] y; vector[J] sigma; } parameters { real mu; real tau; vector[J] eta; } transformed parameters { vector[J] theta; theta = mu + tau * eta; } model { mu ~ normal(0, 10); tau ~ cauchy(0, 10); eta ~ normal(0, 1); // implies theta ~ normal(mu, tau) y ~ normal(theta, sigma); } ``` The centered and non-centered are two parameterizations of the same statistical model, but they have very different practical implications for MCMC. Using the __bayesplot__ diagnostic plots, we'll see that, for this data, the NCP is required in order to properly explore the posterior distribution. To fit both models we first translate the Stan code to C++ and compile it using the `stan_model` function. ```{r compile-models, eval=FALSE} schools_mod_cp <- stan_model("schools_mod_cp.stan") schools_mod_ncp <- stan_model("schools_mod_ncp.stan") ``` We then fit the model by calling Stan's MCMC algorithm using the `sampling` function (the increased `adapt_delta` param is to make the sampler a bit more "careful" and avoid false positive divergences), ```{r fit-models-hidden, results='hide', message=FALSE} fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214055, control = list(adapt_delta = 0.9)) fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9)) ``` and extract a `iterations x chains x parameters` array of posterior draws with `as.array`, ```{r extract-draws} # Extract posterior draws for later use posterior_cp <- as.array(fit_cp) posterior_ncp <- as.array(fit_ncp) ``` You may have noticed the warnings about divergent transitions for the centered parameterization fit. Those are serious business and in most cases indicate that something is wrong with the model and the results should not be trusted. For an explanation of these warnings see [Divergent transitions after warmup](https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup). We'll have a look at diagnosing the source of the divergences first and then dive into some diagnostics that should be checked even if there are no warnings from the sampler.
## Diagnostics for the No-U-Turn Sampler The No-U-Turn Sampler (NUTS, Hoffman and Gelman, 2014) is the variant of Hamiltonian Monte Carlo (HMC) used by [Stan](https://mc-stan.org/) and the various R packages that depend on Stan for fitting Bayesian models. The **bayesplot** package has special functions for visualizing some of the unique diagnostics permitted by HMC, and NUTS in particular. See Betancourt (2017), Betancourt and Girolami (2013), and Stan Development Team (2017) for more details on the concepts. **Documentation:** * `help("MCMC-nuts")` * [mc-stan.org/bayesplot/reference/MCMC-nuts](https://mc-stan.org/bayesplot/reference/MCMC-nuts.html) ------ The special **bayesplot** functions for NUTS diagnostics are ```{r available_mcmc-nuts} available_mcmc(pattern = "_nuts_") ``` Those functions require more information than simply the posterior draws, in particular the log of the posterior density for each draw and some NUTS-specific diagnostic values may be needed. The **bayesplot** package provides generic functions `log_posterior` and `nuts_params` for extracting this information from fitted model objects. Currently methods are provided for models fit using the **rstan**, **rstanarm** and **brms** packages, although it is not difficult to define additional methods for the objects returned by other R packages. For the Stan models we fit above we can use the `log_posterior` and `nuts_params` methods for stanfit objects: ```{r extract-nuts-info} lp_cp <- log_posterior(fit_cp) head(lp_cp) np_cp <- nuts_params(fit_cp) head(np_cp) # for the second model lp_ncp <- log_posterior(fit_ncp) np_ncp <- nuts_params(fit_ncp) ``` ```{r echo=FALSE, warning=FALSE} # On rare occasions, the fits may not be illustrative. Currently the seed is # fixed, but if something in Stan changes and the fixed seeds produce unexpected # results (which should be rare), we want to know. n_divergent_cp <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() n_divergent_ncp <- np_ncp %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() if(n_divergent_cp < 10 || n_divergent_cp > 2000) { stop("Unexpected number of divergences in the CP model. Change seed?") } if(n_divergent_ncp > 0) { stop("Divergences in the NCP model. Fix a bug / change seed?") } ``` In addition to the NUTS-specific plotting functions, some of the general MCMC plotting functions demonstrated in the [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html) vignette also take optional arguments that can be used to display important HMC/NUTS diagnostic information. We'll see examples of this in the next section on divergent transitions. ### Divergent transitions When running the Stan models above, there were warnings about divergent transitions. Here we'll look at diagnosing the source of divergences through visualizations. #### mcmc_parcoord The `mcmc_parcoord` plot shows one line per iteration, connecting the parameter values at this iteration. This lets you see global patterns in the divergences. This function works in general without including information about the divergences, but if the optional `np` argument is used to pass NUTS parameter information, then divergences will be colored in the plot (by default in red). ```{r mcmc_parcoord-1} color_scheme_set("darkgray") mcmc_parcoord(posterior_cp, np = np_cp) ``` Here, you may notice that divergences in the centered parameterization happen exclusively when `tau`, the hierarchical standard deviation, goes near zero and the values of the `theta`s are essentially fixed. This makes `tau` immediately suspect. See [Gabry et al. (2019)](#gabry2019) for another example of the parallel coordinates plot. #### mcmc_pairs The `mcmc_pairs` function can also be used to look at multiple parameters at once, but unlike `mcmc_parcoord` (which works well even when including several dozen parameters) `mcmc_pairs` is more useful for up to ~8 parameters. It shows univariate histograms and bivariate scatter plots for selected parameters and is especially useful in identifying collinearity between variables (which manifests as narrow bivariate plots) as well as the presence of multiplicative non-identifiabilities (banana-like shapes). Let's look at how `tau` interacts with other variables, using only one of the `theta`s to keep the plot readable: ```{r mcmc_pairs} mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"), off_diag_args = list(size = 0.75)) ``` Note that each bivariate plot is present twice -- by default each of those contain half of the chains, so you also get to see if the chains produced similar results (see the documentation for the `condition` argument for other options). Here, the interaction of `tau` and `theta[1]` seems most interesting, as it concentrates the divergences into a tight region. Further examples of pairs plots and instructions for using the various optional arguments to `mcmc_pairs` are provided via `help("mcmc_pairs")`. #### mcmc_scatter Using the `mcmc_scatter` function (with optional argument `np`) we can look at a single bivariate plot to investigate it more closely. For hierarchical models, a good place to start is to plot a "local" parameter (`theta[j]`) against a "global" scale parameter on which it depends (`tau`). We will also use the `transformations` argument to look at the log of `tau`, as this is what Stan is doing under the hood for parameters like `tau` that have a lower bound of zero. That is, even though the draws for `tau` returned from Stan are all positive, the parameter space that the Markov chains actual explore is unconstrained. Transforming `tau` is not strictly necessary for the plot (often the plot is still useful without it) but plotting in the unconstrained is often even more informative. First the plot for the centered parameterization: ```{r mcmc_scatter-1} # assign to an object so we can reuse later scatter_theta_cp <- mcmc_scatter( posterior_cp, pars = c("theta[1]", "tau"), transform = list(tau = "log"), # can abbrev. 'transformations' np = np_cp, size = 1 ) scatter_theta_cp ``` The shape of this bivariate distribution resembles a funnel (or tornado). This one in particular is essentially the same as an example referred to as Neal's funnel (details in the Stan manual) and it is a clear indication that the Markov chains are struggling to explore the tip of the funnel, which is narrower than the rest of the space. The main problem is that large steps are required to explore the less narrow regions efficiently, but those steps become too large for navigating the narrow region. The required step size is connected to the value of `tau`. When `tau` is large it allows for large variation in `theta` (and requires large steps) while small `tau` requires small steps in `theta`. The non-centered parameterization avoids this by sampling the `eta` parameter which, unlike `theta`, is _a priori independent_ of `tau`. Then `theta` is computed deterministically from the parameters `eta`, `mu` and `tau` afterwards. Here's the same plot as above, but with `eta[1]` from non-centered parameterization instead of `theta[1]` from the centered parameterization: ```{r mcmc_scatter-2} scatter_eta_ncp <- mcmc_scatter( posterior_ncp, pars = c("eta[1]", "tau"), transform = list(tau = "log"), np = np_ncp, size = 1 ) scatter_eta_ncp ``` We can see that the funnel/tornado shape is replaced by a somewhat Gaussian blob/cloud and the divergences go away. [Gabry et al. (2019)](#gabry2019) has further discussion of this example. Ultimately we only care about `eta` insofar as it enables the Markov chains to better explore the posterior, so let's directly examine how much more exploration was possible after the reparameterization. For the non-centered parameterization we can make the same scatterplot but use the values of `theta[1] = mu + eta[1] * tau` instead of `eta[1]`. Below is a side by side comparison with the scatterplot of `theta[1]` vs `log(tau)` from the centered parameterization that we made above. We will also force the plots to have the same $y$-axis limits, which will make the most important difference much more apparent: ```{r mcmc_scatter-3} # A function we'll use several times to plot comparisons of the centered # parameterization (cp) and the non-centered parameterization (ncp). See # help("bayesplot_grid") for details on the bayesplot_grid function used here. compare_cp_ncp <- function(cp_plot, ncp_plot, ncol = 2, ...) { bayesplot_grid( cp_plot, ncp_plot, grid_args = list(ncol = ncol), subtitles = c("Centered parameterization", "Non-centered parameterization"), ... ) } scatter_theta_ncp <- mcmc_scatter( posterior_ncp, pars = c("theta[1]", "tau"), transform = list(tau = "log"), np = np_ncp, size = 1 ) compare_cp_ncp(scatter_theta_cp, scatter_theta_ncp, ylim = c(-8, 4)) ``` Once we transform the `eta` values into `theta` values we actually see an even more pronounced funnel/tornado shape than we have with the centered parameterization. But this is precisely what we want! The non-centered parameterization allowed us to obtain draws from the funnel distribution without having to directly navigate the curvature of the funnel. With the centered parameterization the chains never could make it into the neck of funnel and we see a clustering of divergences and no draws in the tail of the distribution. #### mcmc_trace Another useful diagnostic plot is the trace plot, which is a time series plot of the Markov chains. That is, a trace plot shows the evolution of parameter vector over the iterations of one or many Markov chains. The `np` argument to the `mcmc_trace` function can be used to add a rug plot of the divergences to a trace plot of parameter draws. Typically we can see that at least one of the chains is getting stuck wherever there is a cluster of many red marks. Here is the trace plot for the `tau` parameter from the centered parameterization: ```{r mcmc_trace} color_scheme_set("mix-brightblue-gray") mcmc_trace(posterior_cp, pars = "tau", np = np_cp) + xlab("Post-warmup iteration") ``` The first thing to note is that all chains seem to be exploring the same region of parameter values, which is a good sign. But the plot is too crowded to help us diagnose divergences. We may however zoom in to investigate, using the `window` argument: ```{r echo=FALSE} #A check that the chosen window still relevant n_divergent_in_window <- np_cp %>% filter(Parameter == "divergent__" & Value == 1 & Iteration >= 300 & Iteration <= 500) %>% nrow() if(n_divergent_in_window < 6) { divergences <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% select(Iteration) %>% get("Iteration", .) %>% sort() %>% paste(collapse = ",") stop(paste("Too few divergences in the selected window for traceplot zoom. Change the window or the random seed.\nDivergences happened at: ", divergences)) } ``` ```{r mcmc_trace_zoom} mcmc_trace(posterior_cp, pars = "tau", np = np_cp, window = c(300,500)) + xlab("Post-warmup iteration") ``` What we see here is that chains can get stuck as `tau` approaches zero and spend substantial time in the same region of the parameter space. This is just another indication that there is problematic geometry at $\tau \simeq 0$ -- healthy chains jump up and down frequently. #### mcmc_nuts_divergence To understand how the divergences interact with the model globally, we can use the `mcmc_nuts_divergence` function: ```{r mcmc_nuts_divergence} color_scheme_set("red") mcmc_nuts_divergence(np_cp, lp_cp) ``` In the top panel we see the distribution of the log-posterior when there was no divergence vs the distribution when there was a divergence. Divergences often indicate that some part of the posterior isn't being explored and the plot confirms that `lp|Divergence` indeed has lighter tails than `lp|No divergence`. The bottom panel shows the same thing but instead of the log-posterior the NUTS acceptance statistic is shown. Specifying the optional `chain` argument will overlay the plot just for a particular Markov chain on the plot for all chains combined: ```{r mcmc_nuts_divergence-chain} mcmc_nuts_divergence(np_cp, lp_cp, chain = 4) ``` For the non-centered parameterization we may get a few warnings about divergences but if we do we'll have far fewer of them to worry about. ```{r mcmc_nuts_divergence-2} mcmc_nuts_divergence(np_ncp, lp_ncp) ``` If there are only a few divergences we can often get rid of them by increasing the target acceptance rate (`adapt_delta`, the upper limit is 1), which has the effect of lowering the step size used by the sampler and allowing the Markov chains to explore more complicated curvature in the target distribution. ```{r fit-adapt-delta, results='hide', message=FALSE} fit_cp_2 <- sampling(schools_mod_cp, data = schools_dat, control = list(adapt_delta = 0.999), seed = 978245244) fit_ncp_2 <- sampling(schools_mod_ncp, data = schools_dat, control = list(adapt_delta = 0.999), seed = 843256842) ``` ```{r echo=FALSE, warning=FALSE} # On rare occasions, the fits may not be illustrative. Currently the seed is fixed, but if something in Stan changes and the fixed seeds produce unexpected results (which should be rare), we want to know. n_divergent_cp_2 <- fit_cp_2 %>% nuts_params() %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() n_divergent_ncp_2 <- fit_ncp_2 %>% nuts_params() %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() if(n_divergent_cp_2 <= 0) { stop("No divergences in CP with increased adapt.delta. Change seed?") } if(n_divergent_ncp_2 > 0) { stop("Divergences in the NCP model. Fix a bug / change seed?") } ``` For the first model and this particular data, increasing `adapt_delta` will not solve the problem and a reparameterization is required. ```{r mcmc_nuts_divergence-3} mcmc_nuts_divergence(nuts_params(fit_cp_2), log_posterior(fit_cp_2)) mcmc_nuts_divergence(nuts_params(fit_ncp_2), log_posterior(fit_ncp_2)) ``` ### Energy and Bayesian fraction of missing information The `mcmc_nuts_energy` function creates plots similar to those presented in Betancourt (2017). While `mcmcm_nuts_divergence` can identify light tails and incomplete exploration of the target distribution, the `mcmc_nuts_energy` function can identify overly heavy tails that are also challenging for sampling. Informally, the energy diagnostic for HMC (and the related energy-based Bayesian fraction of missing information) quantifies the heaviness of the tails of the posterior distribution. #### mcmc_nuts_energy The plot created by `mcmc_nuts_energy` shows overlaid histograms of the (centered) marginal energy distribution $\pi_E$ and the first-differenced distribution $\pi_{\Delta E}$, ```{r mcmc_nuts_energy-1, message=FALSE} color_scheme_set("red") mcmc_nuts_energy(np_cp) ``` The two histograms ideally look the same (Betancourt, 2017), which is only the case for the non-centered parameterization (right): ```{r mcmc_nuts_energy-3, message=FALSE, fig.width=8} compare_cp_ncp( mcmc_nuts_energy(np_cp, binwidth = 1/2), mcmc_nuts_energy(np_ncp, binwidth = 1/2) ) ``` The difference between the parameterizations is even more apparent if we force the step size to a smaller value and help the chains explore more of the posterior: ```{r mcmc_nuts_energy-4, message=FALSE, fig.width=8} np_cp_2 <- nuts_params(fit_cp_2) np_ncp_2 <- nuts_params(fit_ncp_2) compare_cp_ncp( mcmc_nuts_energy(np_cp_2), mcmc_nuts_energy(np_ncp_2) ) ``` See Betancourt (2017) for more on this particular example as well as the general theory behind the energy plots.
## General MCMC diagnostics A Markov chain generates draws from the target distribution only after it has converged to an equilibrium. Unfortunately, this is only guaranteed in the limit in theory. In practice, diagnostics must be applied to monitor whether the Markov chain(s) have converged. The __bayesplot__ package provides various plotting functions for visualizing Markov chain Monte Carlo (MCMC) diagnostics after fitting a Bayesian model. MCMC draws from any package can be used, although there are a few diagnostic plots that we will see later in this vignette that are specifically intended to be used for [Stan](https://mc-stan.org/) models (or models fit using the same algorithms as Stan). **Documentation:** * `help("MCMC-diagnostics")` * [mc-stan.org/bayesplot/reference/MCMC-diagnostics](https://mc-stan.org/bayesplot/reference/MCMC-diagnostics.html) ------ ### Rhat: potential scale reduction statistic One way to monitor whether a chain has converged to the equilibrium distribution is to compare its behavior to other randomly initialized chains. This is the motivation for the potential scale reduction statistic, split-$\hat{R}$. The split-$\hat{R}$ statistic measures the ratio of the average variance of draws within each chain to the variance of the pooled draws across chains; if all chains are at equilibrium, these will be the same and $\hat{R}$ will be one. If the chains have not converged to a common distribution, the $\hat{R}$ statistic will be greater than one (see Gelman et al. 2013, Stan Development Team 2018). The **bayesplot** package provides the functions `mcmc_rhat` and `mcmc_rhat_hist` for visualizing $\hat{R}$ estimates. First we'll quickly fit one of the models above again, this time intentionally using too few MCMC iterations and allowing more dispersed initial values. This should lead to some high $\hat{R}$ values. ```{r fit_cp_bad_rhat, results='hide'} fit_cp_bad_rhat <- sampling(schools_mod_cp, data = schools_dat, iter = 50, init_r = 10, seed = 671254821) ``` **bayesplot** provides a generic `rhat` extractor function, currently with methods defined for models fit using the **rstan**, **rstanarm** and **brms** packages. But regardless of how you fit your model, all **bayesplot** needs is a vector of $\hat{R}$ values. ```{r print-rhats} rhats <- rhat(fit_cp_bad_rhat) print(rhats) ``` #### mcmc_rhat, mcmc_rhat_hist We can visualize the $\hat{R}$ values with the `mcmc_rhat` function: ```{r echo=FALSE} #Check that the fit we got is a sensible example if(all(rhats < 1.3)) { stop("All rhats for the short chain run are low. Change seed?") } ``` ```{r mcmc_rhat-1} color_scheme_set("brightblue") # see help("color_scheme_set") mcmc_rhat(rhats) ``` In the plot, the points representing the $\hat{R}$ values are colored based on whether they are less than $1.05$, between $1.05$ and $1.1$, or greater than $1.1$. There is no theoretical reason to trichotomize $\hat{R}$ values using these cutoffs, so keep in mind that this is just a heuristic. The $y$-axis text is off by default for this plot because it's only possible to see the labels clearly for models with very few parameters. We can see the names of the parameters with the concerning $\hat{R}$ values using the `yaxis_text` convenience function (which passes arguments like `hjust` to `ggplot2::element_text`): ```{r mcmc_rhat-2} mcmc_rhat(rhats) + yaxis_text(hjust = 1) ``` If we look at the same model fit using longer Markov chains we should see all $\hat{R} < 1.1$, and all points in the plot the same (light) color: ```{r mcmc_rhat-3} mcmc_rhat(rhat = rhat(fit_cp)) + yaxis_text(hjust = 0) ``` We can see the same information shown by `mcmc_rhat` but in histogram form using the `mcmc_rhat_hist` function. See the **Examples** section in `help("mcmc_rhat_hist")` for examples. ### Effective sample size The effective sample size is an estimate of the number of independent draws from the posterior distribution of the estimand of interest. The $n_{eff}$ metric used in Stan is based on the ability of the draws to estimate the true mean value of the parameter, which is related to (but not necessarily equivalent to) estimating other functions of the draws. Because the draws within a Markov chain are _not_ independent if there is autocorrelation, the effective sample size, $n_{eff}$, is usually smaller than the total sample size, $N$ (although it may be larger in some cases[^1]). The larger the ratio of $n_{eff}$ to $N$ the better (see Gelman et al. 2013, Stan Development Team 2018 for more details) . [^1]: $n_{eff} > N$ indicates that the mean estimate of the parameter computed from Stan draws approaches the true mean faster than the mean estimate computed from independent samples from the true posterior (the estimate from Stan has smaller variance). This is possible when the draws are anticorrelated - draws above the mean tend to be well matched with draws below the mean. Other functions computed from draws (quantiles, posterior intervals, tail probabilities) may not necessarily approach the true posterior faster. Google "antithetic sampling" or visit [the relevant forum thread](https://discourse.mc-stan.org/t/n-eff-bda3-vs-stan/2608/19) for some further explanation. The **bayesplot** package provides a generic `neff_ratio` extractor function, currently with methods defined for models fit using the **rstan**, **rstanarm** and **brms** packages. But regardless of how you fit your model, all **bayesplot** needs is a vector of $n_{eff}/N$ values. The `mcmc_neff` and `mcmc_neff_hist` can then be used to plot the ratios. #### mcmc_neff, mcmc_neff_hist ```{r print-neff-ratios} ratios_cp <- neff_ratio(fit_cp) print(ratios_cp) mcmc_neff(ratios_cp, size = 2) ``` In the plot, the points representing the values of $n_{eff}/N$ are colored based on whether they are less than $0.1$, between $0.1$ and $0.5$, or greater than $0.5$. These particular values are arbitrary in that they have no particular theoretical meaning, but a useful heuristic is to worry about any $n_{eff}/N$ less than $0.1$. One important thing to keep in mind is that these ratios will depend not only on the model being fit but also on the particular MCMC algorithm used. One reason why we have such high ratios of $n_{eff}$ to $N$ is that the No-U-Turn sampler used by **rstan** generally produces draws from the posterior distribution with much lower autocorrelations compared to draws obtained using other MCMC algorithms (e.g., Gibbs). Even for models fit using **rstan** the parameterization can make a big difference. Here are the $n_{eff}/N$ plots for `fit_cp` and `fit_ncp` side by side. ```{r mcmc_neff-compare} neff_cp <- neff_ratio(fit_cp, pars = c("theta", "mu", "tau")) neff_ncp <- neff_ratio(fit_ncp, pars = c("theta", "mu", "tau")) compare_cp_ncp(mcmc_neff(neff_cp), mcmc_neff(neff_ncp), ncol = 1) ``` Because of the difference in parameterization, the effective sample sizes are much better for the second model, the non-centered parameterization. ### Autocorrelation As mentioned above, $n_{eff}/N$ decreases as autocorrelation becomes more extreme. We can visualize the autocorrelation using the `mcmc_acf` (line plot) or `mcmc_acf_bar` (bar plot) functions. For the selected parameters, these functions show the autocorrelation for each Markov chain separately up to a user-specified number of lags. Positive autocorrelation is bad (it means the chain tends to stay in the same area between iterations) and you want it to drop quickly to zero with increasing lag. Negative autocorrelation is possible and it is useful as it indicates fast convergence of sample mean towards true mean. #### `mcmc_acf`, `mcmc_acf_bar` Here we can again see a difference when comparing the two parameterizations of the same model. For model 1, $\theta_1$ is the primitive parameter for school 1, whereas for the non-centered parameterization in model 2 the primitive parameter is $\eta_1$ (and $\theta_1$ is later constructed from $\eta_1$, $\mu$, and $\tau$): ```{r mcmc_acf, out.width = "70%"} compare_cp_ncp( mcmc_acf(posterior_cp, pars = "theta[1]", lags = 10), mcmc_acf(posterior_ncp, pars = "eta[1]", lags = 10) ) ```
## References Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/abs/1701.02434 Betancourt, M. (2016). Diagnosing suboptimal cotangent disintegrations in Hamiltonian Monte Carlo. https://arxiv.org/abs/1604.00695 Betancourt, M. and Girolami, M. (2013). Hamiltonian Monte Carlo for hierarchical models. https://arxiv.org/abs/1312.0906 Gabry, J., and Goodrich, B. (2018). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.17.4. https://mc-stan.org/rstanarm/ Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. \doi:10.1111/rssa.12378. ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [arXiv preprint](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper)) Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. *Statistical Science*. 7(4): 457--472. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third edition. Hoffman, M. D. and Gelman, A. (2014). The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. *Journal of Machine Learning Research*. 15:1593--1623. Rubin, D. B. (1981). Estimation in Parallel Randomized Experiments. *Journal of Educational and Behavioral Statistics*. 6:377--401. Stan Development Team. _Stan Modeling Language Users Guide and Reference Manual_. https://mc-stan.org/users/documentation/ Stan Development Team. (2018). RStan: the R interface to Stan. R package version 2.17.3. https://mc-stan.org/rstan/ bayesplot/R/0000755000176200001440000000000014560773547012477 5ustar liggesusersbayesplot/R/mcmc-combo.R0000644000176200001440000000704514127610043014620 0ustar liggesusers#' Combination plots #' #' @name MCMC-combos #' @family MCMC #' #' @template args-mcmc-x #' @param ... Arguments passed to the plotting functions named in `combo`. #' @param combo A character vector with at least two elements. Each element of #' `combo` corresponds to a column in the resulting graphic and should be the #' name of one of the available [MCMC][MCMC-overview] functions (omitting the #' `mcmc_` prefix). #' @param widths A numeric vector the same length as `combo` specifying #' relative column widths. For example, if the plot has two columns, then #' `widths = c(2, 1)` will allocate more space for the first column by a #' factor of 2 (as would `widths = c(.3, .15)`, etc.). The default, #' `NULL`, allocates the same horizontal space for each column. #' @param gg_theme Unlike most of the other **bayesplot** functions, #' `mcmc_combo` returns a gtable object rather than a ggplot object, and #' so theme objects can't be added directly to the returned plot object. The #' `gg_theme` argument helps get around this problem by accepting a #' **ggplot2** [theme][ggplot2::theme] object that is added to each of the #' plots *before* combining them into the gtable object that is returned. #' This can be a theme object created by a call to [ggplot2::theme()] or #' one of the **bayesplot** convenience functions, e.g. #' [legend_none()] (see the **Examples** section, below). #' #' @return A gtable object (the result of calling #' [gridExtra::arrangeGrob()]) with `length(combo)` columns and #' a row for each parameter. #' #' @examples #' # some parameter draws to use for demonstration #' x <- example_mcmc_draws() #' dim(x) #' dimnames(x) #' #' mcmc_combo(x, pars = c("alpha", "sigma")) #' mcmc_combo(x, pars = c("alpha", "sigma"), widths = c(1, 2)) #' #' \donttest{ #' # change second plot, show log(sigma) instead of sigma, #' # and remove the legends #' color_scheme_set("mix-blue-red") #' mcmc_combo( #' x, #' combo = c("dens_overlay", "trace"), #' pars = c("alpha", "sigma"), #' transformations = list(sigma = "log"), #' gg_theme = legend_none() #' ) #' #' # same thing but this time also change the entire ggplot theme #' mcmc_combo( #' x, #' combo = c("dens_overlay", "trace"), #' pars = c("alpha", "sigma"), #' transformations = list(sigma = "log"), #' gg_theme = ggplot2::theme_gray() + legend_none() #' ) #' } #' NULL #' @rdname MCMC-combos #' @export mcmc_combo <- function(x, combo = c("dens", "trace"), ..., widths = NULL, gg_theme = NULL) { suggested_package("gridExtra") if (length(combo) < 2) { abort("'combo' should have at least two elements.") } plotfuns <- paste0("mcmc_", combo) not_found <- setdiff(plotfuns, available_mcmc()) if (length(not_found)) { abort(paste( "The following functions were not found:", paste(not_found, collapse = ", ") )) } plotfuns <- lapply(plotfuns, function(x) get(x, pos = asNamespace("bayesplot"), mode = "function")) args <- list(x = x, ...) if (is.list(args$facet_args)) { args$facet_args[["ncol"]] <- 1 args$facet_args[["nrow"]] <- NULL } else { args$facet_args <- list(ncol = 1, nrow = NULL) } plots <- lapply(plotfuns, function(f) suppressWarnings(do.call(f, args))) plots <- lapply(plots, function(x) x + bayesplot_theme_get()) if (!is.null(gg_theme)) plots <- lapply(plots, function(x) x + gg_theme) bayesplot_grid( plots = plots, grid_args = list(ncol = length(combo), widths = widths) ) } bayesplot/R/mcmc-scatterplots.R0000644000176200001440000007736314556270424016275 0ustar liggesusers#' Scatterplots of MCMC draws #' #' Scatterplots, hexagonal heatmaps, and pairs plots from MCMC draws. See the #' **Plot Descriptions** section, below, for details. #' #' @name MCMC-scatterplots #' @family MCMC #' #' @template args-mcmc-x #' @template args-pars #' @template args-regex_pars #' @template args-transformations #' @param ... Currently ignored. #' @param size,alpha For `mcmc_scatter()`, passed to #' [ggplot2::geom_point()] to control the appearance of the points. #' @param bins,binwidth For `mcmc_hex()`, an optional numeric vector of #' *length two* passed to [ggplot2::geom_hex()] to override the #' default binwidth in both the vertical and horizontal directions. #' #' @param np Optionally, a data frame of NUTS sampler parameters, either created #' by [nuts_params()] or in the same form as the object returned by #' [nuts_params()]. The colors, shapes, and sizes of the #' superimposed points can be customized using the `np_style` argument. #' @param np_style If `np` is specified, `np_style` can be a call to #' the `scatter_style_np()` helper function (for `mcmc_scatter()`) or #' the `pairs_style_np()` helper function (for `mcmc_pairs()`) to specify #' arguments controlling the appearance of superimposed points representing #' NUTS diagnostic information. (Note: for `pairs_style_np()` the #' `size` arguments are interpreted as scaling factors). #' #' @return `mcmc_scatter()` and `mcmc_hex()` return a ggplot object that #' can be further customized using the **ggplot2** package. #' #' `mcmc_pairs()` returns many ggplot objects organized into a grid via #' [bayesplot_grid()]. #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_scatter()`}{ #' Bivariate scatterplot of posterior draws. If using a very large number of #' posterior draws then `mcmc_hex()` may be preferable to avoid #' overplotting. For models fit using [NUTS] the `np`, #' and `np_style` arguments can be used to add additional information in #' the plot (in this case the approximate location of divergences). #' For more on why the scatter plot with divergences is a useful #' diagnostic tool see [Gabry et al. (2019)](https://github.com/jgabry/bayes-vis-paper#readme). #' } #' \item{`mcmc_hex()`}{ #' Hexagonal heatmap of 2-D bin counts. This plot is useful in cases where #' the posterior sample size is large enough that `mcmc_scatter()` suffers #' from overplotting. #' } #' \item{`mcmc_pairs()`}{ #' A square plot matrix with univariate marginal distributions along the #' diagonal (as histograms or kernel density plots) and bivariate #' distributions off the diagonal (as scatterplots or hex heatmaps). #' #' For the off-diagonal plots, the default is to split the chains so that #' (roughly) half are displayed above the diagonal and half are below (all #' chains are always merged together for the plots along the diagonal). Other #' possibilities are available by setting the `condition` argument. #' #' Additionally, extra diagnostic information for models fit using #' [NUTS] can be added to the pairs plot using the `lp`, #' `np`, and `np_style` arguments. If `np` is specified (and #' `condition` is *not* `"divergent__"`), then points (red, by #' default) will be superimposed onto the off-diagonal plots indicating which #' (if any) iterations encountered a divergent transition. Also, if both #' `np` and `max_treedepth` are specified then points (yellow, by #' default) will be superimposed to indicate a transition that hit the #' maximum treedepth rather than terminated its evolution normally. The #' `np_style` argument can be used with the `pairs_style_np()` #' convenience function to change the appearance of these overlaid points. #' See the **Examples** section. #' } #' } #' #' @template reference-vis-paper #' #' @examples #' library("ggplot2") #' #' # some parameter draws to use for demonstration #' x <- example_mcmc_draws(params = 6) #' dimnames(x) #' #' # scatterplot of alpha vs log(sigma) #' color_scheme_set("teal") #' (p <- mcmc_scatter(x, pars = c("alpha", "sigma"), #' transform = list(sigma = "log"))) #' p + #' labs( #' title = "Insert your own headline-grabbing title", #' subtitle = "with a provocative subtitle", #' caption = "and a controversial caption", #' x = expression(alpha), #' y = expression(log(sigma)) #' ) #' #' # add ellipse #' p + stat_ellipse(level = 0.9, color = "gray20", size = 1) #' #' # add contour #' color_scheme_set("red") #' p2 <- mcmc_scatter(x, pars = c("alpha", "sigma"), size = 3.5, alpha = 0.25) #' p2 + stat_density_2d(color = "black", size = .5) #' #' # can also add lines/smooths #' color_scheme_set("pink") #' (p3 <- mcmc_scatter(x, pars = c("alpha", "beta[3]"), alpha = 0.25, size = 3)) #' p3 + geom_smooth(method = "lm", se = FALSE, color = "gray20", #' size = .75, linetype = 2) #' #' \donttest{ #' if (requireNamespace("hexbin", quietly = TRUE)) { #' # hexagonal heatmap #' color_scheme_set("brightblue") #' (p <- mcmc_hex(x, pars = c("sigma", "alpha"), transform = list(sigma = "log"))) #' p + plot_bg(fill = "gray95") #' p + plot_bg(fill = "gray95") + panel_bg(fill = "gray70") #' } #' } NULL #' @rdname MCMC-scatterplots #' @export mcmc_scatter <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., size = 2.5, alpha = 0.8, np = NULL, np_style = scatter_style_np()) { check_ignored_arguments(...) .mcmc_scatter( x, pars = pars, regex_pars = regex_pars, transformations = transformations, size = size, alpha = alpha, hex = FALSE, binwidth = NULL, np = np, np_style = np_style ) } #' @rdname MCMC-scatterplots #' @export #' mcmc_hex <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., bins = 30, binwidth = NULL) { suggested_package("scales") suggested_package("hexbin") check_ignored_arguments(...) .mcmc_scatter( x, pars = pars, regex_pars = regex_pars, transformations = transformations, hex = TRUE, bins = bins, binwidth = binwidth, size = NULL, alpha = NULL ) } #' @rdname MCMC-scatterplots #' @export #' @param condition For `mcmc_pairs()`, a call to the `pairs_condition()` #' helper function, which is used to specify a criterion for determining which #' chains (or iterations) are shown in the plots above the diagonal and which #' are shown in the plots below the diagonal. The histograms (or density #' plots) along the diagonal are always made using all chains and iterations, #' but the scatterplots (or hex plots) above and below the diagonal show #' different combinations of chains/iterations depending on `condition`. #' The default is a call to `pairs_condition()` with none of its arguments #' specified. In this case half of the chains (or roughly half if there are an #' odd number) will be used in the plots above the diagonal and the rest in #' the plots below the diagonal. The `chains`, `draws`, and #' `nuts` arguments to `pairs_condition()`, which are documented #' below, can be used to change this default. #' #' @param lp For `mcmc_pairs()`, a molten data frame of draws of the #' log-posterior or, more commonly, of a quantity equal to the log-posterior #' up to a constant. `lp` should either be created via #' [log_posterior()] or be an object with the same form as the #' object returned by [log_posterior()]. #' @param max_treedepth For `mcmc_pairs()`, an integer representing the #' maximum treedepth allowed when fitting the model (if fit using NUTS). This #' is only needed for detecting which transitions (if any) hit the maximum #' treedepth. #' @param diag_fun,off_diag_fun For `mcmc_pairs()`, the plotting function to #' use for the plots along the diagonal and for the off-diagonal plots, #' respectively. Currently `diag_fun` can be `"hist"` for histogram #' or `"dens"` for density, and `off_diag_fun` can be #' `"scatter"` for scatterplot or `"hex"` for a hexagonal heatmap. #' @param diag_args,off_diag_args For `mcmc_pairs()`, optional named lists of #' arguments to pass to the functions implied by the `diag_fun` and #' `off_diag_fun` arguments, respectively. For example, if #' `off_diag_fun` is `"scatter"` then `off_diag_args` could #' include optional arguments to `mcmc_scatter()` like `size` and #' `alpha`. #' @param grid_args,save_gg_objects For `mcmc_pairs()`, arguments to pass to #' [bayesplot_grid()]. For example, since `mcmc_pairs()` returns #' more than a single ggplot object, using [ggtitle()] afterwards #' will not work. But you you can still add a title to the plot using #' `grid_args = list(top="My title")`. #' #' @examples #' \donttest{ #' color_scheme_set("purple") #' #' # pairs plots #' # default of condition=NULL implies splitting chains between upper and lower panels #' mcmc_pairs(x, pars = "alpha", regex_pars = "beta\\[[1,4]\\]", #' off_diag_args = list(size = 1, alpha = 0.5)) #' #' # change to density plots instead of histograms and hex plots instead of #' # scatterplots #' mcmc_pairs(x, pars = "alpha", regex_pars = "beta\\[[1,4]\\]", #' diag_fun = "dens", off_diag_fun = "hex") #' #' # plot chain 1 above diagonal and chains 2, 3, and 4 below #' color_scheme_set("brightblue") #' mcmc_pairs(x, pars = "alpha", regex_pars = "beta\\[[1,4]\\]", #' diag_fun = "dens", off_diag_fun = "hex", #' condition = pairs_condition(chains = list(1, 2:4))) #' } #' #' \dontrun{ #' ### Adding NUTS diagnostics to scatterplots and pairs plots #' #' # examples using rstanarm package #' library(rstanarm) #' #' # for demonstration purposes, intentionally fit a model that #' # will (almost certainly) have some divergences #' fit <- stan_glm( #' mpg ~ ., data = mtcars, #' iter = 1000, refresh = 0, #' # this combo of prior and adapt_delta should lead to some divergences #' prior = hs(), #' adapt_delta = 0.9 #' ) #' posterior <- as.array(fit) #' np <- nuts_params(fit) #' #' # mcmc_scatter with divergences highlighted #' color_scheme_set("brightblue") #' mcmc_scatter(posterior, pars = c("wt", "sigma"), np = np) #' #' color_scheme_set("darkgray") #' div_style <- scatter_style_np(div_color = "green", div_shape = 4, div_size = 4) #' mcmc_scatter(posterior, pars = c("sigma", "(Intercept)"), #' np = np, np_style = div_style) #' #' # split the draws according to above/below median accept_stat__ #' # and show approximate location of divergences (red points) #' color_scheme_set("brightblue") #' mcmc_pairs( #' posterior, #' pars = c("wt", "cyl", "sigma"), #' off_diag_args = list(size = 1, alpha = 1/3), #' condition = pairs_condition(nuts = "accept_stat__"), #' np = np #' ) #' #' # more customizations: #' # - transform sigma to log(sigma) #' # - median log-posterior as 'condition' #' # - hex instead of scatter for off-diagonal plots #' # - show points where max treedepth hit in blue #' color_scheme_set("darkgray") #' mcmc_pairs( #' posterior, #' pars = c("wt", "cyl", "sigma"), #' transform = list(sigma = "log"), #' off_diag_fun = "hex", #' condition = pairs_condition(nuts = "lp__"), #' lp = log_posterior(fit), #' np = np, #' np_style = pairs_style_np(div_color = "firebrick", #' td_color = "blue", #' td_size = 2), #' # for demonstration purposes, set max_treedepth to a value that will #' # result in at least a few max treedepth warnings #' max_treedepth = with(np, -1 + max(Value[Parameter == "treedepth__"])) #' ) #' } #' mcmc_pairs <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., diag_fun = c("hist", "dens"), off_diag_fun = c("scatter", "hex"), diag_args = list(), off_diag_args = list(), condition = pairs_condition(), lp = NULL, np = NULL, np_style = pairs_style_np(), max_treedepth = NULL, grid_args = list(), save_gg_objects = TRUE) { check_ignored_arguments(...) stopifnot( is.list(diag_args), is.list(off_diag_args), inherits(np_style, "nuts_style"), inherits(condition, "pairs_condition") ) diag_fun <- match.arg(diag_fun) off_diag_fun <- match.arg(off_diag_fun) plot_diagonal <- pairs_plotfun(diag_fun) plot_off_diagonal <- pairs_plotfun(off_diag_fun) x <- prepare_mcmc_array(x, pars, regex_pars, transformations) x <- drop_constants_and_duplicates(x) n_iter <- num_iters(x) n_chain <- num_chains(x) n_param <- num_params(x) pars <- parameter_names(x) if (n_chain == 1) { warn("Only one chain in 'x'. This plot is more useful with multiple chains.") } if (n_param < 2) { abort("This plot requires at least two parameters in 'x'.") } no_np <- is.null(np) no_lp <- is.null(lp) no_max_td <- is.null(max_treedepth) if (!no_np) { param <- sym("Parameter") val <- sym("Value") np <- validate_nuts_data_frame(np, lp) divs <- dplyr::filter(np, UQ(param) == "divergent__") %>% pull(UQ(val)) divergent__ <- matrix(divs, nrow = n_iter * n_chain, ncol = n_param)[, 1] if (!no_max_td) { gt_max_td <- (dplyr::filter(np, UQ(param) == "treedepth__") %>% pull(UQ(val))) >= max_treedepth max_td_hit__ <- matrix(gt_max_td, nrow = n_iter * n_chain, ncol = n_param)[, 1] } } cond <- handle_condition(x, condition, np, lp) x <- merge_chains(cond[["x"]]) mark <- cond[["mark"]] all_pairs <- expand.grid(pars, pars, stringsAsFactors = FALSE, KEEP.OUT.ATTRS = FALSE) plots <- vector("list", length = nrow(all_pairs)) use_default_binwidth <- is.null(diag_args[["binwidth"]]) for (j in seq_len(nrow(all_pairs))) { pair <- as.character(all_pairs[j,]) if (identical(pair[1], pair[2])) { # Diagonal diag_args[["x"]] <- x[, pair[1], drop = FALSE] # silence ggplot2's "Pick better value with `binwidth`" message if (diag_fun == "hist" && use_default_binwidth) diag_args[["binwidth"]] <- diff(range(diag_args[["x"]]))/30 plots[[j]] <- do.call(plot_diagonal, diag_args) + labs(subtitle = pair[1]) + theme(axis.line.y = element_blank(), plot.subtitle = element_text(hjust = 0.5)) } else { # Off-diagonal # use mark if above diagonal and !mark if below the diagonal mark2 <- if (is_lower_tri(j, n_param)) !mark else mark x_j <- x[mark2, pair, drop = FALSE] if (!no_np) { divs_j <- divergent__[mark2] max_td_hit_j <- if (no_max_td) NULL else max_td_hit__[mark2] } else { divs_j <- max_td_hit_j <- NULL } off_diag_args[["x"]] <- x_j plots[[j]] <- do.call(plot_off_diagonal, off_diag_args) if (isTRUE(any(divs_j == 1))) { divs_j_fac <- factor(as.logical(divs_j), levels = c(FALSE, TRUE), labels = c("NoDiv", "Div")) plots[[j]] <- plots[[j]] + geom_point( aes(color = divs_j_fac, size = divs_j_fac), shape = np_style$shape[["div"]], alpha = np_style$alpha[["div"]], na.rm = TRUE ) } if (isTRUE(any(max_td_hit_j == 1))) { max_td_hit_j_fac <- factor(max_td_hit_j, levels = c(FALSE, TRUE), labels = c("NoHit", "Hit")) plots[[j]] <- plots[[j]] + geom_point( aes(color = max_td_hit_j_fac, size = max_td_hit_j_fac), shape = np_style$shape[["td"]], alpha = np_style$alpha[["td"]], na.rm = TRUE ) } if (isTRUE(any(divs_j == 1)) || isTRUE(any(max_td_hit_j == 1))) plots[[j]] <- format_nuts_points(plots[[j]], np_style) } } plots <- lapply(plots, function(x) x + xaxis_title(FALSE) + yaxis_title(FALSE)) bayesplot_grid(plots = plots, legends = FALSE, grid_args = grid_args, save_gg_objects = save_gg_objects) } #' @rdname MCMC-scatterplots #' @export #' @param div_color,div_shape,div_size,div_alpha,td_color,td_shape,td_size,td_alpha #' Optional arguments to the `scatter_style_np()` or `pairs_style_np()` #' helper functions that are eventually passed to #' [ggplot2::geom_point()].The default values are displayed in the #' **Usage** section above. scatter_style_np <- function(div_color = "red", div_shape = 16, div_size = 2.5, div_alpha = 1) { stopifnot( is.numeric(div_shape) || is.character(div_shape), is.character(div_color), is.numeric(div_size), is.numeric(div_alpha) && div_alpha >= 0 && div_alpha <= 1 ) style <- list( color = c(div = div_color), shape = c(div = div_shape), size = c(div = div_size), alpha = c(div = div_alpha) ) structure(style, class = c(class(style), "nuts_style")) } #' @rdname MCMC-scatterplots #' @export pairs_style_np <- function(div_color = "red", div_shape = 4, div_size = 1, div_alpha = 1, td_color = "yellow2", td_shape = 3, td_size = 1, td_alpha = 1) { stopifnot( is.numeric(div_shape) || is.character(div_shape), is.numeric(td_shape) || is.character(td_shape), is.character(div_color), is.character(td_color), is.numeric(div_size), is.numeric(td_size), is.numeric(div_alpha) && div_alpha >= 0 && div_alpha <= 1, is.numeric(td_alpha) && td_alpha >= 0 && td_alpha <= 1 ) style <- list( color = c(div = div_color, td = td_color), shape = c(div = div_shape, td = td_shape), size = c(div = div_size, td = td_size), alpha = c(div = div_alpha, td = td_alpha) ) structure(style, class = c(class(style), "nuts_style")) } #' @rdname MCMC-scatterplots #' @export #' @param chains,draws,nuts Optional arguments to the `pairs_condition()` #' helper function, which is used to specify the `condition` argument for #' `mcmc_pairs()`. #' \itemize{ #' \item The `chains` argument can be used to select some subset of the #' chains. If `chains` is an integer vector then the behavior is the same #' as the default (half the chains above the diagonal and half below) except #' using only the specified subset of chains. Alternatively, `chains` can #' be a list of two integer vectors with the first specifying the chains to be #' shown in the plots above the diagonal and the second for below the #' diagonal. #' \item The `draws` argument to `pairs_condition()` can be used to #' directly specify which realizations are plotted above and below the #' diagonal. `draws` can be a single proportion, which is interpreted as #' the proportion of realizations (among all chains) to plot in the lower #' panel starting with the first realization in each chain, with the #' complement (from the end of each chain) plotted in the upper panel. #' Alternatively `draws` can be a logical vector with length equal to the #' product of the number of iterations and the number of chains, in which case #' realizations corresponding to `FALSE` and `TRUE` will be plotted #' in the lower and upper panels, respectively. #' \item For models fit using NUTS, the `nuts` argument to #' `pairs_condition()` can be used. It takes a (possibly abbreviated) #' string to select among `"accept_stat__"`, `"stepsize__"`, #' `"treedepth__"`, `"n_leapfrog__"`, `"divergent__"`, #' `"energy__"`, and `"lp__"`. These are the sampler parameters #' associated with [NUTS()] (and `"lp__"` is the log-posterior #' up to an additive constant). In this case, plots below the diagonal will #' contain realizations that are below the median of the indicated variable #' (or are zero in the case of `"divergent__"`), and plots above the #' diagonal will contain realizations that are greater than or equal to the #' median of the indicated variable (or are one in the case of #' `"divergent__"`). If `"lp__"` is used then the `lp` #' argument to `mcmc_pairs` must also be specified. For the other NUTS #' parameters the `np` argument to `mcmc_pairs()` must also be #' specified. #' } #' pairs_condition <- function(chains = NULL, draws = NULL, nuts = NULL) { .ignore_args <- function(..., why = NULL) { dots <- list(...) nms <- names(dots)[!sapply(dots, is.null)] if (length(nms)) { inform(paste0( "The following specified arguments were ignored by 'pairs_condition' ", "because ", why, ": ", paste(sQuote(nms), collapse = ", ") )) } } .error_duplicate_chains <- function() { abort("Each chain can only be specified once in the 'chains' argument to 'pairs_condition'.") } if (is.null(chains) && is.null(draws) && is.null(nuts)) { # default: half of the chains above diag, half below cond <- list() cond_type <- "default" } else if (!is.null(chains)) { # Using 'chains' argument .ignore_args( draws = draws, nuts = nuts, why = "they are superseded by 'chains'" ) if (is.list(chains)) { # list of two integer vectors, each specifying a subset of the chains stopifnot(length(chains) == 2) chain_vec <- unlist(chains, use.names = FALSE) if (length(chain_vec) != length(unique(chain_vec))) .error_duplicate_chains() cond <- list(upper = as.integer(chains[[1]]), lower = as.integer(chains[[2]])) cond_type <- "chain_list" } else if (is.numeric(chains)) { # single vector specifying a subset of chains stopifnot(NCOL(chains) == 1, all(chains == as.integer(chains))) if (length(chains) != length(unique(chains))) .error_duplicate_chains() cond <- as.integer(chains) cond_type <- "chain_vector" } else { abort(paste( "The 'chains' argument to 'pairs_condition' must be", "an integer vector or a list of two integer vectors." )) } } else if (!is.null(draws)) { # Using 'draws' argument .ignore_args(nuts = nuts, why = "they are superseded by 'draws'") if (is.numeric(draws)) { # proportion of realizations (among all chains) to plot in the lower panel stopifnot(draws > 0 && draws < 1) cond <- draws cond_type <- "draws_proportion" } else if (is.logical(draws)) { # T/F for each iteration to split into upper/lower panels cond <- draws cond_type <- "draws_selection" } else { abort(paste( "The 'draws' argument to 'pairs_condition' must be", "a single proportion or a logical vector." )) } } else { # Using 'nuts' argument if (!is.character(nuts) || length(nuts) > 1) { abort("The 'nuts' argument to 'pairs_condition' must be a single string.") } cond_type <- "nuts" cond <- match.arg(nuts, several.ok = FALSE, choices = c("accept_stat__", "stepsize__", "treedepth__", "n_leapfrog__", "divergent__", "energy__", "lp__")) } structure( cond, class = c(class(cond), "pairs_condition"), type = cond_type # this attribute is used later by handle_condition() ) } # internal ---------------------------------------------------------------- #' @importFrom dplyr pull .mcmc_scatter <- function(x, pars = character(), regex_pars = character(), transformations = list(), hex = FALSE, size = 2.5, alpha = 0.8, bins = 30, binwidth = NULL, np = NULL, np_style = scatter_style_np()) { x <- prepare_mcmc_array(x, pars, regex_pars, transformations) if (num_params(x) != 2) { abort(paste( "For 'mcmc_scatter' and 'mcmc_hex' exactly 2 parameters must be selected.", "'mcmc_pairs' can be used for more than 2 parameters." )) } x <- merge_chains(x) parnames <- colnames(x)[1:2] has_divs <- !is.null(np) xydata <- data.frame(x = c(x[, 1]), y = c(x[, 2])) if (has_divs) { if (hex) { warn("'np' is currently ignored for hex plots.") } stopifnot(inherits(np_style, "nuts_style")) np <- validate_nuts_data_frame(np) param <- sym("Parameter") val <- sym("Value") divg <- sym("Divergent") xydata$Divergent <- np %>% dplyr::filter(UQ(param) == "divergent__") %>% pull(UQ(val)) divdata <- dplyr::filter(xydata, UQ(divg) == 1) xydata <- dplyr::filter(xydata, UQ(divg) == 0) } graph <- ggplot(data = xydata, aes(x = .data$x, y = .data$y)) + bayesplot_theme_get() if (!hex) { # scatterplot graph <- graph + geom_point( shape = 21, color = get_color("dh"), fill = get_color("d"), size = size, alpha = alpha ) if (has_divs) { graph <- graph + geom_point( data = divdata, color = np_style$color[["div"]], size = np_style$size[["div"]], alpha = np_style$alpha[["div"]], shape = np_style$shape[["div"]] ) } } else { # hex binning graph <- graph + geom_hex( aes(fill = scales::rescale(after_stat(density))), bins = bins, binwidth = binwidth ) + scale_fill_gradientn( "Density", colors = unlist(color_scheme_get()), breaks = c(.1, .9), labels = c("low", "high") ) } graph + labs(x = parnames[1], y = parnames[2]) } # internal for mcmc_pairs ------------------------------------------------- #' Get plotting functions from user-specified #' `diag_fun` and `off_diag_fun` arguments #' #' @noRd #' @param x User specified `diag_fun` or `off_diag_fun` argument to `mcmc_pairs()` pairs_plotfun <- function(x) { fun <- paste0("mcmc_", x) utils::getFromNamespace(fun, "bayesplot") } #' Unstack molten data frame #' #' @noRd #' @param df A data frame (from `nuts_params()`, `log_posterior()`, etc) #' @param .form Same as `form` arg to `utils::unstack()` unstack_to_matrix <- function(df, .form) { x <- utils::unstack(df, form = .form) as.matrix(x) } #' Check if off-diagonal plot is above or below the diagonal #' #' @noRd #' @param j integer (index) #' @param n Number of parameters (number of plots = `n^2`) #' @return `TRUE` if below the diagonal, `FALSE` if above the diagonal is_lower_tri <- function(j, n) { idx <- array_idx_j(j, n) lower_tri <- lower_tri_idx(n) row_match_found(idx, lower_tri) } #' Get array indices of the jth element in the plot matrix #' #' @noRd #' @param j integer (index) #' @param n number of parameters (number of plots = n^2) #' @return rwo vector (1-row matrix) containing the array indices of the jth #' element in the plot matrix array_idx_j <- function(j, n) { jj <- matrix(seq_len(n^2), nrow = n, byrow = TRUE)[j] arrayInd(jj, .dim = c(n, n)) } #' Get indices of lower triangular elements of a square matrix #' @noRd #' @param n number of rows (columns) in the square matrix lower_tri_idx <- function(n) { a <- rev(abs(sequence(seq.int(n - 1)) - n) + 1) b <- rep.int(seq.int(n - 1), rev(seq.int(n - 1))) cbind(row = a, col = b) } #' Find which (if any) row in y is a match for x #' @noRd #' @param x a row vector (i.e., a matrix with 1 row) #' @param y a matrix #' @return either a row number in `y` or `NA` if no match row_match_found <- function(x, y) { stopifnot(is.matrix(x), is.matrix(y), nrow(x) == 1) x <- as.data.frame(x) y <- as.data.frame(y) res <- match( do.call(function(...) paste(..., sep=":::"), x), do.call(function(...) paste(..., sep=":::"), y) ) isTRUE(!is.na(res) && length(res) == 1) } #' Drop any constant or duplicate variables #' @noRd #' @param x 3-D array drop_constants_and_duplicates <- function(x) { x2 <- drop_consts(x) x2 <- drop_dupes(x2) class(x2) <- c(class(x2), "mcmc_array") x2 } drop_consts <- function(x) { varying <- apply(x, 3, FUN = function(y) length(unique(c(y))) > 1) if (all(varying)) return(x) warn(paste( "The following parameters were dropped because they are constant:", paste(names(varying)[!varying], collapse = ", ") )) x[, , varying, drop = FALSE] } drop_dupes <- function(x) { dupes <- duplicated(x, MARGIN = 3) if (!any(dupes)) return(x) warn(paste( "The following parameters were dropped because they are duplicative:", paste(parameter_names(x)[dupes], collapse = ", ") )) x[, , !dupes, drop = FALSE] } #' Handle user's specified `condition` #' @noRd #' @param x 3-D mcmc array. #' @param condition Object returned by `pairs_condition()`. #' @param np,lp User-specified arguments to `mcmc_pairs()`. #' @return A named list containing `"x"` (`x`, possibly modified) and `"mark"` #' (logical or interger vector for eventually splitting `x`). handle_condition <- function(x, condition=NULL, np=NULL, lp=NULL) { n_iter <- num_iters(x) n_chain <- num_chains(x) no_np <- is.null(np) no_lp <- is.null(lp) cond_type <- attr(condition, "type") if (cond_type == "default") { k <- ncol(x) %/% 2 mark <- c(rep(FALSE, n_iter * k), rep(TRUE, n_iter * (n_chain - k))) } else if (cond_type == "chain_vector") { x <- x[, condition, , drop = FALSE] k <- ncol(x) %/% 2 n_chain <- length(condition) mark <- c(rep(FALSE, n_iter * k), rep(TRUE, n_iter * (n_chain - k))) } else if (cond_type == "chain_list") { x <- x[, c(condition[[1]], condition[[2]]), , drop = FALSE] k1 <- length(condition[[1]]) k2 <- length(condition[[2]]) mark <- c(rep(TRUE, n_iter * k1), rep(FALSE, n_iter * k2)) } else if (cond_type == "draws_proportion") { mark <- rep(1:n_iter > (condition * n_iter), times = n_chain) } else if (cond_type == "draws_selection") { # T/F for each iteration to split into upper and lower stopifnot(length(condition) == (n_iter * n_chain)) mark <- !condition } else if (cond_type == "nuts") { # NUTS sampler param or lp__ if (no_np && condition != "lp__") abort(paste( "To use this value of 'condition' the 'np' argument", "to 'mcmc_pairs' must also be specified." )) if (condition == "lp__") { if (no_lp) abort(paste( "If 'condition' is 'lp__' then the 'lp' argument", "to 'mcmc_pairs' must also be specified." )) mark <- unstack_to_matrix(lp, Value ~ Chain) } else { param <- sym("Parameter") mark <- dplyr::filter(np, UQ(param) == condition) mark <- unstack_to_matrix(mark, Value ~ Chain) } if (condition == "divergent__") { mark <- as.logical(mark) } else { mark <- c(mark) >= median(mark) } if (length(unique(mark)) == 1) abort(paste(condition, "is constant so it cannot be used as a condition.")) } list(x = x, mark = mark) } #' Apply scale_color_manual and scale_size_manual if plotting divergences and #' hitting max_treedepth #' #' @noRd #' @param graph ggplot object #' @param np_args list of style arguments returned by `pairs_style_np()` #' @return `graph`, updated format_nuts_points <- function(graph, np_args) { graph + scale_color_manual( values = set_names(c(NA, np_args$color[["div"]], NA, np_args$color[["td"]]), c("NoDiv", "Div", "NoHit", "Hit")) ) + scale_size_manual( values = set_names(c(NA, rel(np_args$size[["div"]]), NA, rel(np_args$size[["td"]])), c("NoDiv", "Div", "NoHit", "Hit")) ) } bayesplot/R/ppd-intervals.R0000644000176200001440000002176214335264324015406 0ustar liggesusers#' PPD intervals #' #' Medians and central interval estimates of posterior or prior predictive #' distributions. Each of these functions makes the same plot as the #' corresponding [`ppc_`][PPC-intervals] function but without plotting any #' observed data `y`. The **Plot Descriptions** section at [PPC-intervals] has #' details on the individual plots. #' #' @name PPD-intervals #' @family PPDs #' #' @template args-ypred #' @inheritParams PPC-intervals #' #' @template return-ggplot-or-data #' #' @template reference-vis-paper #' #' @examples #' color_scheme_set("brightblue") #' ypred <- example_yrep_draws() #' x <- example_x_data() #' group <- example_group_data() #' #' ppd_intervals(ypred[, 1:50]) #' ppd_intervals(ypred[, 1:50], fatten = 0) #' ppd_intervals(ypred[, 1:50], fatten = 0, linewidth = 2) #' ppd_intervals(ypred[, 1:50], prob_outer = 0.75, fatten = 0, linewidth = 2) #' #' # put a predictor variable on the x-axis #' ppd_intervals(ypred[, 1:100], x = x[1:100], fatten = 1) + #' ggplot2::labs(y = "Prediction", x = "Some variable of interest") #' #' # with a grouping variable too #' ppd_intervals_grouped( #' ypred = ypred[, 1:100], #' x = x[1:100], #' group = group[1:100], #' size = 2, #' fatten = 0, #' facet_args = list(nrow = 2) #' ) #' #' # even reducing size, ppd_intervals is too cluttered when there are many #' # observations included (ppd_ribbon is better) #' ppd_intervals(ypred, size = 0.5, fatten = 0.1, linewidth = 0.5) #' ppd_ribbon(ypred) #' ppd_ribbon(ypred, size = 0) # remove line showing median prediction #' NULL #' @rdname PPD-intervals #' @export ppd_intervals <- function(ypred, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppd_intervals_data( ypred = ypred, x = x, group = dots$group, prob = prob, prob_outer = prob_outer ) ggplot(data, mapping = intervals_inner_aes( needs_y = TRUE, color = "ypred", fill = "ypred" )) + geom_linerange( mapping = intervals_outer_aes(color = "ypred"), alpha = alpha, size = size, linewidth = linewidth ) + geom_pointrange( shape = 21, stroke = 0.5, size = size, fatten = fatten, linewidth = linewidth ) + scale_color_ppd() + scale_fill_ppd() + intervals_axis_labels(has_x = !is.null(x)) + bayesplot_theme_get() + legend_none() } #' @rdname PPD-intervals #' @export ppd_intervals_grouped <- function(ypred, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppd_intervals", call), parent.frame()) g + intervals_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPD-intervals #' @export ppd_ribbon <- function(ypred, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppd_intervals_data( ypred = ypred, x = x, group = dots$group, prob = prob, prob_outer = prob_outer ) ggplot(data, mapping = intervals_inner_aes(color = "ypred", fill = "ypred")) + geom_ribbon( mapping = intervals_outer_aes(fill = "ypred", color = "ypred"), color = NA, size = 0.2 * size, alpha = alpha ) + geom_ribbon( mapping = intervals_outer_aes(), fill = NA, color = get_color("mh"), size = 0.2 * size, alpha = 1 ) + geom_ribbon(size = 0.5 * size) + geom_line( mapping = aes(y = .data$m), color = get_color("d"), linewidth = size ) + scale_color_ppd() + scale_fill_ppd() + intervals_axis_labels(has_x = !is.null(x)) + bayesplot_theme_get() + legend_none() } #' @export #' @rdname PPD-intervals ppd_ribbon_grouped <- function(ypred, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppd_ribbon", call), parent.frame()) g + intervals_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPD-intervals #' @export ppd_intervals_data <- function(ypred, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9) { check_ignored_arguments(...) ypred <- validate_predictions(ypred) x <- validate_x(x, ypred[1,]) if (!is.null(group)) { group <- validate_group(group, ncol(ypred)) } .ppd_intervals_data( predictions = ypred, y = NULL, x = x, group = group, prob = prob, prob_outer = prob_outer ) } #' @rdname PPD-intervals #' @export ppd_ribbon_data <- ppd_intervals_data # internal ---------------------------------------------------------------- #' Back end for both `ppd_intervals_data()` and `ppc_intervals_data()` #' #' @noRd #' @param predictions SxN matrix of predictions (`ypred` or `yrep`) already validated. #' @param y `NULL` or user's `y` argument already validated. #' @param group `NULL` or user's `group` argument, already validated. #' @param x User's `x` argument, already validated. #' @return A molten data frame of prediction intervals, possibly including `y`. #' #' @importFrom dplyr group_by ungroup summarise .ppd_intervals_data <- function(predictions, y = NULL, x = NULL, group = NULL, prob, prob_outer) { stopifnot(prob > 0 && prob < 1) stopifnot(prob_outer > 0 && prob_outer <= 1) probs <- sort(c(prob, prob_outer)) prob <- probs[1] prob_outer <- probs[2] alpha <- (1 - probs) / 2 probs <- sort(c(alpha, 0.5, 1 - alpha)) has_group <- !is.null(group) has_y <- !is.null(y) has_x <- !is.null(x) long_d <- melt_predictions(predictions) if (has_y) { long_d$y_obs <- y[long_d$y_id] } if (!has_x) { x <- seq_len(ncol(predictions)) } long_d$x <- x[long_d$y_id] if (has_group) { long_d$group <- group[long_d$y_id] } group_by_vars <- syms(c("y_id", if (has_y) "y_obs", if (has_group) "group", "x")) long_d %>% group_by(!!!group_by_vars) %>% summarise( outer_width = prob_outer, inner_width = prob, ll = unname(quantile(.data$value, probs = probs[1])), l = unname(quantile(.data$value, probs = probs[2])), m = unname(quantile(.data$value, probs = probs[3])), h = unname(quantile(.data$value, probs = probs[4])), hh = unname(quantile(.data$value, probs = probs[5])) ) %>% ungroup() } #' Aesthetic mapping for interval and ribbon plots #' #' @param needs_y Whether to include `y` in call to `aes()`. Needed for #' `geom_pointrange()`. #' @param ... Aguments to pass to `aes()` other than `x`,`y`,`ymin`,`ymax`. #' @return Object returned by `aes()`. Always sets at least `x`, `ymin`, `ymax`. #' @noRd intervals_inner_aes <- function(needs_y = FALSE, ...) { mapping <- aes( x = .data$x, ymin = .data$l, ymax = .data$h, ... ) if (!needs_y) { return(mapping) } modify_aes(mapping, y = .data$m) } intervals_outer_aes <- function(needs_y = FALSE, ...) { mapping <- aes( x = .data$x, ymin = .data$ll, ymax = .data$hh, ... ) if (!needs_y) { return(mapping) } modify_aes(mapping, y = .data$m) } #' Create the facet layer for grouped interval and ribbon plots #' #' @param facet_args User's `facet_args` argument. #' @param scales_default String to use for `scales` argument to `facet_wrap()` #' if not specified by user. Defaults to `"free"`, unlike `facet_wrap()`. #' @return Object returned by `facet_wrap()`. #' @noRd intervals_group_facets <- function(facet_args, scales_default = "free") { facet_args[["facets"]] <- "group" facet_args[["scales"]] <- facet_args[["scales"]] %||% scales_default do.call("facet_wrap", facet_args) } #' Set the axis labels for interval and ribbon plots #' #' @param has_x Did the user provide an `x` argument (T/F)? #' @return Object returned by `labs()`. 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Ebayesplot/R/example-data.R0000644000176200001440000000633114335264324015153 0ustar liggesusers#' Example draws to use in demonstrations and tests #' #' These functions return various objects containing data used in the examples #' throughout the **bayesplot** package documentation. #' #' @name example-data #' @keywords internal #' #' @return See **Details**. #' #' @details #' Each of these functions returns an object containing data, parameter draws, or #' predictions corresponding to a basic linear regression model with data #' `y` (outcome vector) and `X` (predictor matrix), and parameters #' `alpha` (intercept), `beta` (coefficient vector), and `sigma` #' (error sd). #' #' \describe{ #' \item{`example_mcmc_draws()`}{ #' If `chains > 1`, a `250` (iterations) by `chains` by #' `params` array or, if `chains = 1`, a `250` by `params` #' matrix of MCMC draws from the posterior distribution of the parameters in #' the linear regression model described above. If `params = 1` then only #' the draws for `alpha` are included in the returned object. If #' `params >= 2` then draws for `sigma` are also included. And if #' `params` is between `3` and the maximum of `6` then draws #' for regression coefficients `beta[k]` (`k` in `1:(params-2)`) #' are also included. #' } #' \item{`example_y_data()`}{ #' A numeric vector with `434` observations of the outcome variable in the #' linear regression model. #' } #' \item{`example_x_data()`}{ #' A numeric vector with `434` observations of one of the predictor #' variables in the linear regression model. #' } #' \item{`example_group_data()`}{ #' A factor variable with `434` observations of a grouping variable with #' two levels. #' } #' \item{`example_yrep_draws()`}{ #' A `500` (draws) by `434` (data points) matrix of draws from the #' posterior predictive distribution. Each row represents a full dataset drawn #' from the posterior predictive distribution of the outcome `y` after #' fitting the linear regression model mentioned above. #' } #' } #' #' NULL #' @rdname example-data #' @export #' @param chains An integer between 1 and 4 indicating the desired number of #' chains. #' @param params An integer between 1 and 6 indicating the desired number of #' parameters. #' #' @examples #' draws <- example_mcmc_draws() #' dim(draws) #' dimnames(draws) #' #' draws <- example_mcmc_draws(1, 2) #' dim(draws) #' colnames(draws) #' #' draws <- example_mcmc_draws(params = 6) #' dimnames(draws)[[3]] #' example_mcmc_draws <- function(chains = 4, params = 4) { stopifnot( chains >= 1 && chains <= 4, params >= 1 && params <= 6 ) x <- .ex_draws[, seq_len(chains), ] if (chains > 1) { x[, , seq_len(params), drop = FALSE] } else { x[, seq_len(params), drop = FALSE] } } #' @rdname example-data #' @export #' @examples #' y <- example_y_data() #' x <- example_x_data() #' group <- example_group_data() #' length(y) #' length(x) #' length(group) #' tail(data.frame(y, x, group), 5) #' #' yrep <- example_yrep_draws() #' dim(yrep) # ncol(yrep) = length(y) = length(x) = length(group) #' example_yrep_draws <- function() { .ex_yrep } #' @rdname example-data #' @export example_y_data <- function() { .ex_y } #' @rdname example-data #' @export example_x_data <- function() { .ex_x } #' @rdname example-data #' @export example_group_data <- function() { .ex_group } bayesplot/R/zzz.R0000644000176200001440000000123714335264324013446 0ustar liggesusers.onAttach <- function(...) { ver <- utils::packageVersion("bayesplot") packageStartupMessage("This is bayesplot version ", ver) packageStartupMessage("- Online documentation and vignettes at mc-stan.org/bayesplot") packageStartupMessage("- bayesplot theme set to bayesplot::theme_default()") packageStartupMessage(" * Does _not_ affect other ggplot2 plots") packageStartupMessage(" * See ?bayesplot_theme_set for details on theme setting") if (utils::packageVersion("ggplot2") < "3.4.0") { packageStartupMessage("\nbayesplot works best with ggplot2 version >= 3.4.0. ", "\nUse update.packages('ggplot2') to update.") } } bayesplot/R/ppc-loo.R0000644000176200001440000006046614556465326014205 0ustar liggesusers#' LOO predictive checks #' #' Leave-One-Out (LOO) predictive checks. See the **Plot Descriptions** section, #' below, and [Gabry et al. (2019)](https://github.com/jgabry/bayes-vis-paper#readme) #' for details. #' #' @name PPC-loo #' @family PPCs #' @template args-y-yrep #' @param ... Currently unused. #' @param lw A matrix of (smoothed) log weights with the same dimensions as #' `yrep`. See [loo::psis()] and the associated `weights()` method as well as #' the **Examples** section, below. If `lw` is not specified then #' `psis_object` can be provided and log weights will be extracted. #' @param psis_object If using **loo** version `2.0.0` or greater, an #' object returned by the `psis()` function (or by the `loo()` function #' with argument `save_psis` set to `TRUE`). #' @param alpha,size,fatten,linewidth Arguments passed to code geoms to control plot #' aesthetics. For `ppc_loo_pit_qq()` and `ppc_loo_pit_overlay()`, `size` and #' `alpha` are passed to [ggplot2::geom_point()] and #' [ggplot2::geom_density()], respectively. For `ppc_loo_intervals()`, `size` #' `linewidth` and `fatten` are passed to [ggplot2::geom_pointrange()]. For #' `ppc_loo_ribbon()`, `alpha` and `size` are passed to #' [ggplot2::geom_ribbon()]. #' #' @template return-ggplot #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_loo_pit_overlay()`, `ppc_loo_pit_qq()`}{ #' The calibration of marginal predictions can be assessed using probability #' integral transformation (PIT) checks. LOO improves the check by avoiding the #' double use of data. See the section on marginal predictive checks in Gelman #' et al. (2013, p. 152--153) and section 5 of Gabry et al. (2019) for an #' example of using **bayesplot** for these checks. #' #' The LOO PIT values are asymptotically uniform (for continuous data) if the #' model is calibrated. The `ppc_loo_pit_overlay()` function creates a plot #' comparing the density of the LOO PITs (thick line) to the density estimates #' of many simulated data sets from the standard uniform distribution (thin #' lines). See Gabry et al. (2019) for an example of interpreting the shape of #' the miscalibration that can be observed in these plots. #' #' The `ppc_loo_pit_qq()` function provides an alternative visualization of #' the miscalibration with a quantile-quantile (Q-Q) plot comparing the LOO #' PITs to the standard uniform distribution. Comparing to the uniform is not #' good for extreme probabilities close to 0 and 1, so it can sometimes be #' useful to set the `compare` argument to `"normal"`, which will #' produce a Q-Q plot comparing standard normal quantiles calculated from the #' PIT values to the theoretical standard normal quantiles. This can help see #' the (mis)calibration better for the extreme values. However, in most cases #' we have found that the overlaid density plot (`ppc_loo_pit_overlay()`) #' function will provide a clearer picture of calibration problems than the #' Q-Q plot. #' } #' \item{`ppc_loo_intervals()`, `ppc_loo_ribbon()`}{ #' Similar to [ppc_intervals()] and [ppc_ribbon()] but the intervals are for #' the LOO predictive distribution. #' } #' } #' #' @templateVar bdaRef (p. 152--153) #' @template reference-bda #' @template reference-vis-paper #' @template reference-loo #' #' @examples #' #' \dontrun{ #' library(rstanarm) #' library(loo) #' #' head(radon) #' fit <- stan_lmer( #' log_radon ~ floor + log_uranium + floor:log_uranium #' + (1 + floor | county), #' data = radon, #' iter = 100, #' chains = 2, #' cores = 2 #' ) #' y <- radon$log_radon #' yrep <- posterior_predict(fit) #' #' loo1 <- loo(fit, save_psis = TRUE, cores = 4) #' psis1 <- loo1$psis_object #' lw <- weights(psis1) # normalized log weights #' #' # marginal predictive check using LOO probability integral transform #' color_scheme_set("orange") #' ppc_loo_pit_overlay(y, yrep, lw = lw) #' #' ppc_loo_pit_qq(y, yrep, lw = lw) #' ppc_loo_pit_qq(y, yrep, lw = lw, compare = "normal") #' #' # can use the psis object instead of lw #' ppc_loo_pit_qq(y, yrep, psis_object = psis1) #' #' # loo predictive intervals vs observations #' keep_obs <- 1:50 #' ppc_loo_intervals(y, yrep, psis_object = psis1, subset = keep_obs) #' #' color_scheme_set("gray") #' ppc_loo_intervals(y, yrep, psis_object = psis1, subset = keep_obs, #' order = "median") #' } #' NULL #' @rdname PPC-loo #' @export #' @param pit For `ppc_loo_pit_overlay()` and `ppc_loo_pit_qq()`, optionally a #' vector of precomputed PIT values that can be specified instead of `y`, #' `yrep`, and `lw` (these are all ignored if `pit` is specified). If not #' specified the PIT values are computed internally before plotting. #' @param samples For `ppc_loo_pit_overlay()`, the number of data sets (each #' the same size as `y`) to simulate from the standard uniform #' distribution. The default is 100. The density estimate of each dataset is #' plotted as a thin line in the plot, with the density estimate of the LOO #' PITs overlaid as a thicker dark line. #' @param compare For `ppc_loo_pit_qq()`, a string that can be either #' `"uniform"` or `"normal"`. If `"uniform"` (the default) the Q-Q plot #' compares computed PIT values to the standard uniform distribution. If #' `compare="normal"`, the Q-Q plot compares standard normal quantiles #' calculated from the PIT values to the theoretical standard normal #' quantiles. #' @param trim Passed to [ggplot2::stat_density()]. #' @template args-density-controls #' @param boundary_correction For `ppc_loo_pit_overlay()`, when set to `TRUE` #' (the default) the function will compute boundary corrected density values #' via convolution and a Gaussian filter, also known as the reflection method #' (Boneva et al., 1971). As a result, parameters controlling the standard #' kernel density estimation such as `adjust`, `kernel` and `n_dens` are #' ignored. NOTE: The current implementation only works well for continuous #' observations. #' @param grid_len For `ppc_loo_pit_overlay()`, when `boundary_correction` is #' set to `TRUE` this parameter specifies the number of points used to #' generate the estimations. This is set to 512 by default. #' #' @references Boneva, L. I., Kendall, D., & Stefanov, I. (1971). Spline #' transformations: Three new diagnostic aids for the statistical #' data-analyst. *J. R. Stat. Soc. B* (Methodological), 33(1), 1-71. #' https://www.jstor.org/stable/2986005. #' ppc_loo_pit_overlay <- function(y, yrep, lw = NULL, ..., psis_object = NULL, pit = NULL, samples = 100, size = 0.25, alpha = 0.7, boundary_correction = TRUE, grid_len = 512, bw = "nrd0", trim = FALSE, adjust = 1, kernel = "gaussian", n_dens = 1024) { check_ignored_arguments(...) data <- ppc_loo_pit_data( y = y, yrep = yrep, lw = lw, psis_object = psis_object, pit = pit, samples = samples, bw = bw, boundary_correction = boundary_correction, grid_len = grid_len ) if (!missing(y) && all(y %in% 0:1)) { warning( "This plot is not recommended for binary data. ", "For plots that are more suitable see ", "\nhttps://avehtari.github.io/modelselection/diabetes.html#44_calibration_of_predictions", call. = FALSE ) } message(paste("NOTE: The kernel density estimate assumes continuous observations", "and is not optimal for discrete observations.")) if (boundary_correction) { p <- ggplot(data) + aes(x = .data$x, y = .data$value) + geom_line( aes(group = .data$rep_id, color = "yrep"), data = function(x) dplyr::filter(x, !.data$is_y), alpha = alpha, linewidth = size, na.rm = TRUE) + geom_line( aes(color = "y"), data = function(x) dplyr::filter(x, .data$is_y), linewidth = 1, lineend = "round", na.rm = TRUE) + scale_x_continuous( limits = c(0, 1), expand = expansion(0, 0.01), breaks = seq(0, 1, by = 0.25), labels = c("0", "0.25", "0.5", "0.75", "1") ) } else { p <- ggplot(data) + aes(x = .data$value) + stat_density( aes(group = .data$rep_id, color = "yrep"), data = function(x) dplyr::filter(x, !.data$is_y), geom = "line", position = "identity", linewidth = size, alpha = alpha, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n = n_dens, na.rm = TRUE) + stat_density( aes(color = "y"), data = function(x) dplyr::filter(x, .data$is_y), geom = "line", position = "identity", lineend = "round", linewidth = 1, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n = n_dens, na.rm = TRUE) + scale_x_continuous( limits = c(0.05, 0.95), expand = expansion(0, 0), breaks = seq(from = .1, to = .9, by = .2) ) } p + scale_color_ppc(labels = c("PIT", "Unif")) + scale_y_continuous( limits = c(0, NA), expand = expansion(mult = c(0, .25)) ) + bayesplot_theme_get() + yaxis_title(FALSE) + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) } #' @rdname PPC-loo #' @export ppc_loo_pit_data <- function(y, yrep, lw = NULL, ..., psis_object = NULL, pit = NULL, samples = 100, bw = "nrd0", boundary_correction = TRUE, grid_len = 512) { if (!is.null(pit)) { stopifnot(is.numeric(pit), is_vector_or_1Darray(pit)) inform("'pit' specified so ignoring 'y','yrep','lw' if specified.") } else { suggested_package("rstantools") y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) lw <- .get_lw(lw, psis_object) stopifnot(identical(dim(yrep), dim(lw))) pit <- rstantools::loo_pit(object = yrep, y = y, lw = lw) } if (!boundary_correction) { unifs <- matrix(runif(length(pit) * samples), nrow = samples) data <- ppc_data(pit, unifs) } else { unifs <- matrix(runif(grid_len * samples), nrow = samples) ref_list <- .ref_kde_correction(unifs, bw = bw, grid_len = grid_len) pit_list <- .kde_correction(pit, bw = bw, grid_len = grid_len) pit <- pit_list$bc_pvals unifs <- ref_list$unifs xs <- c(pit_list$xs, ref_list$xs) data <- ppc_data(pit, unifs) %>% dplyr::arrange(.data$rep_id) %>% mutate(x = xs) } data } #' @rdname PPC-loo #' @export ppc_loo_pit_qq <- function(y, yrep, lw = NULL, ..., psis_object = NULL, pit = NULL, compare = c("uniform", "normal"), size = 2, alpha = 1) { check_ignored_arguments(...) compare <- match.arg(compare) if (!is.null(pit)) { stopifnot(is.numeric(pit), is_vector_or_1Darray(pit)) inform("'pit' specified so ignoring 'y','yrep','lw' if specified.") } else { suggested_package("rstantools") y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) lw <- .get_lw(lw, psis_object) stopifnot(identical(dim(yrep), dim(lw))) pit <- rstantools::loo_pit(object = yrep, y = y, lw = lw) } if (compare == "uniform") { theoretical <- stats::qunif x_lab <- "Uniform" y_lab <- "LOO-PIT" } else { pit <- as.vector(stats::qnorm(pit)) theoretical <- stats::qnorm x_lab <- "Normal" y_lab <- "LOO-PIT (standard normal quantiles)" } qq <- ggplot(data.frame(p = pit)) + geom_qq( aes(sample = .data$p), distribution = theoretical, color = get_color("m"), size = size, alpha = alpha) + geom_abline( linetype = 2, color = "black") + bayesplot_theme_get() + labs(x = x_lab, y = y_lab) if (compare == "uniform") { qq + lims(x=c(0,1), y=c(0,1)) } else { qq } } #' @rdname PPC-loo #' @export ppc_loo_pit <- function(y, yrep, lw, pit = NULL, compare = c("uniform", "normal"), ..., size = 2, alpha = 1) { .Deprecated("ppc_loo_pit_qq or ppc_loo_pit_overlay") ppc_loo_pit_qq( y = y, yrep = yrep, lw = lw, pit = pit, compare = compare, size = size, alpha = alpha, ... ) } #' @rdname PPC-loo #' @export #' @template args-prob-prob_outer #' @param intervals For `ppc_loo_intervals()` and `ppc_loo_ribbon()`, optionally #' a matrix of pre-computed LOO predictive intervals that can be specified #' instead of `yrep` (ignored if `intervals` is specified). If not specified #' the intervals are computed internally before plotting. If specified, #' `intervals` must be a matrix with number of rows equal to the number of #' data points and five columns in the following order: lower outer interval, #' lower inner interval, median (50%), upper inner interval and upper outer #' interval (column names are ignored). #' @param order For `ppc_loo_intervals()`, a string indicating how to arrange #' the plotted intervals. The default (`"index"`) is to plot them in the #' order of the observations. The alternative (`"median"`) arranges them #' by median value from smallest (left) to largest (right). #' @param subset For `ppc_loo_intervals()` and `ppc_loo_ribbon()`, an optional #' integer vector indicating which observations in `y` (and `yrep`) to #' include. Dropping observations from `y` and `yrep` manually before passing #' them to the plotting function will not work because the dimensions will not #' match up with the dimensions of `psis_object`, but if all of `y` and `yrep` #' are passed along with `subset` then **bayesplot** can do the subsetting #' internally for `y`, `yrep` *and* `psis_object`. See the **Examples** #' section for a demonstration. #' ppc_loo_intervals <- function(y, yrep, psis_object, ..., subset = NULL, intervals = NULL, prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1, order = c("index", "median")) { check_ignored_arguments(...) y <- validate_y(y) order_by_median <- match.arg(order) == "median" if (!is.null(intervals)) { stopifnot(is.matrix(intervals), ncol(intervals) %in% c(3, 5)) inform(paste( "'intervals' specified so ignoring", "'yrep', 'psis_object', 'subset', if specified." )) if (ncol(intervals) == 3) { intervals <- cbind(intervals[, 1], intervals, intervals[, 3]) } } else { suggested_package("loo", min_version = "2.0.0") yrep <- validate_predictions(yrep, length(y)) if (!is.null(subset)) { stopifnot(length(y) >= length(subset)) y <- y[subset] yrep <- yrep[, subset, drop=FALSE] psis_object <- .psis_subset(psis_object, subset) } probs <- sort(c(prob, prob_outer)) a <- (1 - probs) / 2 stopifnot(identical(dim(psis_object), dim(yrep))) intervals <- suppressWarnings(t(loo::E_loo( x = yrep, psis_object = psis_object, type = "quantile", probs = sort(c(a, 0.5, 1 - a)) )$value)) } x <- seq_along(y) if (order_by_median) { x <- reorder(x, intervals[, 2]) } xy_labs <- if (order_by_median) { labs(x = "Ordered by median", y = NULL) } else { intervals_axis_labels(has_x = FALSE) } data <- .loo_intervals_data(y, x, intervals) ggplot(data) + intervals_inner_aes( needs_y = TRUE, color = "yrep", fill = "yrep" ) + geom_linerange( mapping = intervals_outer_aes(color = "yrep"), alpha = alpha, size = size ) + geom_pointrange( shape = 21, stroke = 0.5, linewidth = linewidth, size = size, fatten = fatten ) + geom_point( mapping = aes( y = .data$y_obs, color = "y", fill = "y" ), shape = 21, stroke = 0.5, size = 1 ) + scale_color_ppc() + scale_fill_ppc() + bayesplot_theme_get() + xy_labs + xaxis_text(!order_by_median) + xaxis_ticks(!order_by_median) } #' @rdname PPC-loo #' @export ppc_loo_ribbon <- function(y, yrep, psis_object, ..., subset = NULL, intervals = NULL, prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25) { check_ignored_arguments(...) y <- validate_y(y) if (!is.null(intervals)) { stopifnot(is.matrix(intervals), ncol(intervals) %in% c(3, 5)) inform(paste( "'intervals' specified so ignoring", "'yrep', 'psis_object', 'subset', if specified." )) if (ncol(intervals) == 3) { intervals <- cbind(intervals[, 1], intervals, intervals[, 3]) } } else { suggested_package("loo", min_version = "2.0.0") yrep <- validate_predictions(yrep, length(y)) if (!is.null(subset)) { stopifnot(length(y) >= length(subset)) y <- y[subset] yrep <- yrep[, subset, drop=FALSE] psis_object <- .psis_subset(psis_object, subset) } probs <- sort(c(prob, prob_outer)) a <- (1 - probs) / 2 stopifnot(identical(dim(psis_object), dim(yrep))) intervals <- suppressWarnings(t(loo::E_loo( x = yrep, psis_object = psis_object, type = "quantile", probs = sort(c(a, 0.5, 1 - a)) )$value)) } data <- .loo_intervals_data(y, x = seq_along(y), intervals) ggplot(data) + intervals_inner_aes(fill = "yrep", color = "yrep") + geom_ribbon( mapping = intervals_outer_aes(fill = "yrep", color = "yrep"), alpha = alpha, linewidth = 0.05 ) + geom_ribbon( mapping = intervals_outer_aes(), alpha = 1, linewidth = 0.05, fill = NA, color = get_color("m") ) + geom_ribbon(linewidth = 0.05) + geom_line( mapping = aes(y = .data$m), color = get_color("m"), linewidth = size ) + geom_blank(aes(fill = "y")) + geom_line( aes(y = .data$y_obs, color = "y"), linewidth = 0.5, alpha = 2/3 ) + scale_color_ppc() + scale_fill_ppc(values = c(NA, get_color("l"))) + intervals_axis_labels(has_x = FALSE) + bayesplot_theme_get() } # internal ---------------------------------------------------------------- .loo_intervals_data <- function(y, x, intervals) { stopifnot(length(y) == nrow(intervals), length(x) == length(y)) tibble::tibble( y_id = seq_along(y), y_obs = y, x = x, ll = intervals[, 1], l = intervals[, 2], m = intervals[, 3], h = intervals[, 4], hh = intervals[, 5]) } # subset a psis_object without breaking it # (FIXME: use function from loo package when subset.psis() method becomes available) .psis_subset <- function(psis_object, subset) { stopifnot(all(subset == as.integer(subset))) if (length(subset) > dim(psis_object)[2]) { abort("'subset' has too many elements.") } psis_object$log_weights <- psis_object$log_weights[, subset, drop=FALSE] psis_object$diagnostics$pareto_k <- psis_object$diagnostics$pareto_k[subset] psis_object$diagnostics$n_eff <- psis_object$diagnostics$n_eff[subset] attr(psis_object, "dims") <- c(dim(psis_object)[1], length(subset)) attr(psis_object, "norm_const_log") <- attr(psis_object, "norm_const_log")[subset] attr(psis_object, "tail_len") <- attr(psis_object, "tail_len")[subset] attr(psis_object, "r_eff") <- attr(psis_object, "r_eff")[subset] psis_object } ## Boundary correction based on code by ArViz development team # The main method is a 1-D density estimation for linear data with # convolution with a Gaussian filter. # Based on scipy.signal.gaussian formula .gaussian <- function(N, bw){ n <- seq(0, N -1) - (N - 1)/2 sigma = 2 * bw * bw w = exp(-n^2 / sigma) return(w) } .linear_convolution <- function(x, bw, grid_counts, grid_breaks, grid_len){ # 1-D Gaussian estimation via # convolution of a Gaussian filter and the binned relative freqs bin_width <- grid_breaks[2] - grid_breaks[1] f <- grid_counts / bin_width / length(x) bw <- bw / bin_width # number of data points to generate for gaussian filter gauss_n <- as.integer(bw * 2 *pi) if (gauss_n == 0){ gauss_n = 1 } # Generate Gaussian filter vector kernel <- .gaussian(gauss_n, bw) npad <- as.integer(grid_len / 5) # Reflection method (i.e. get first N and last N points to pad vector) f <- c(rev(f[1:(npad)]), f, rev(f)[(grid_len - npad):(grid_len - 1)]) # Convolution: Gaussian filter + reflection method (pading) works as an # averaging moving window based on a Gaussian density which takes care # of the density boundary values near 0 and 1. bc_pvals <- stats::filter(f, kernel, method = 'convolution', sides = 2)[(npad + 1):(npad + grid_len)] bc_pvals / (bw * (2 * pi)^0.5) } .kde_correction <- function(x, bw, grid_len){ # Generate boundary corrected values via a linear convolution using a # 1-D Gaussian window filter. This method uses the "reflection method" # to estimate these pvalues and helps speed up the code if (any(is.infinite(x))){ warning(paste("Ignored", sum(is.infinite(x)), "Non-finite PIT values are invalid for KDE boundary correction method")) x <- x[is.finite(x)] } if (grid_len < 100){ grid_len <- 100 } # Get relative frequency boundaries and counts for input vector bins <- seq(from= min(x), to = max(x), length.out = grid_len + 1) hist_obj <- graphics::hist(x, breaks = bins, plot = FALSE) grid_breaks <- hist_obj$breaks grid_counts <- hist_obj$counts # Compute bandwidth based on use specification bw <- stats::density(x, bw = bw)$bw # 1-D Convolution bc_pvals <- .linear_convolution(x, bw, grid_counts, grid_breaks, grid_len) # Generate vector of x-axis values for plotting based on binned relative freqs n_breaks <- length(grid_breaks) xs <- (grid_breaks[2:n_breaks] + grid_breaks[1:(n_breaks - 1)]) / 2 first_nonNA <- utils::head(which(!is.na(bc_pvals)),1) last_nonNA <- utils::tail(which(!is.na(bc_pvals)),1) bc_pvals[1:first_nonNA] <- bc_pvals[first_nonNA] bc_pvals[last_nonNA:length(bc_pvals)] <- bc_pvals[last_nonNA] list(xs = xs, bc_pvals = bc_pvals) } # Wrapper function to generate runif reference lines based on # .kde_correction() .ref_kde_correction <- function(unifs, bw, grid_len){ # Allocate memory idx <- seq(from = 1, to = ncol(unifs)*nrow(unifs) + ncol(unifs), by = ncol(unifs)) idx <- c(idx, ncol(unifs)*nrow(unifs)) xs <- rep(0, ncol(unifs)*nrow(unifs)) bc_mat <- matrix(0, nrow(unifs), ncol(unifs)) # Generate boundary corrected reference values for (i in 1:nrow(unifs)){ bc_list <- .kde_correction(unifs[i,], bw = bw, grid_len = grid_len) bc_mat[i,] <- bc_list$bc_pvals xs[idx[i]:(idx[i+1]-1)] <- bc_list$xs } list(xs = xs, unifs = bc_mat) } # Extract log weights from psis_object if provided .get_lw <- function(lw = NULL, psis_object = NULL) { if (is.null(lw) && is.null(psis_object)) { abort("One of 'lw' and 'psis_object' must be specified.") } else if (is.null(lw)) { suggested_package("loo", min_version = "2.0.0") if (!loo::is.psis(psis_object)) { abort("If specified, 'psis_object' must be a PSIS object from the loo package.") } lw <- loo::weights.importance_sampling(psis_object) } lw } bayesplot/R/helpers-mcmc.R0000644000176200001440000003066014556270424015175 0ustar liggesusers#' Prepare 3-D array for MCMC plots #' #' @noRd #' @param x,pars,regex_pars,transformations Users's arguments to one of the #' mcmc_* functions. #' @return A 3-D (Iterations x Chains x Parameters) array. #' prepare_mcmc_array <- function(x, pars = character(), regex_pars = character(), transformations = list()) { if (posterior::is_draws(x)) { x <- posterior::as_draws_array(x) } else if (is_df_with_chain(x)) { x <- df_with_chain2array(x) } else if (is_chain_list(x)) { # this will apply to mcmc.list and similar objects x <- chain_list2array(x) } else if (is.data.frame(x)) { # data frame without Chain column x <- as.matrix(x) } else { # try object's as.array method x <- as.array(x) } stopifnot(is.matrix(x) || is.array(x)) if (is.array(x) && !(length(dim(x)) %in% c(2,3))) { abort("Arrays should have 2 or 3 dimensions. See help('MCMC-overview').") } if (anyNA(x)) { abort("NAs not allowed in 'x'.") } if (rlang::is_quosures(pars)) { pars <- tidyselect_parameters(complete_pars = parameter_names(x), pars_list = pars) } else { pars <- select_parameters(complete_pars = parameter_names(x), explicit = pars, patterns = regex_pars) } # possibly recycle transformations (apply same to all pars) if (is.function(transformations) || (is.character(transformations) && length(transformations) == 1)) { transformations <- rep(list(transformations), length(pars)) transformations <- set_names(transformations, pars) } if (is.matrix(x)) { x <- x[, pars, drop=FALSE] if (length(transformations)) { x <- apply_transformations(x, transformations = transformations) } x <- array(x, dim = c(nrow(x), 1, ncol(x))) } else { x <- x[, , pars, drop = FALSE] if (length(transformations)) { x <- apply_transformations(x, transformations = transformations) } } pars <- rename_transformed_pars(pars, transformations) set_mcmc_dimnames(x, pars) } #' Explicit and/or regex parameter selection #' #' @noRd #' @param explicit Character vector of selected parameter names. #' @param patterns Character vector of regular expressions. #' @param complete_pars Character vector of all possible parameter names. #' @return Character vector of combined explicit and matched (via regex) #' parameter names, unless an error is thrown. #' select_parameters <- function(explicit = character(), patterns = character(), complete_pars = character()) { stopifnot(is.character(explicit), is.character(patterns), is.character(complete_pars)) if (!length(explicit) && !length(patterns)) { return(complete_pars) } if (length(explicit)) { if (!all(explicit %in% complete_pars)) { not_found <- which(!explicit %in% complete_pars) abort(paste( "Some 'pars' don't match parameter names:", paste(explicit[not_found], collapse = ", "), call. = FALSE )) } } if (!length(patterns)) { return(unique(explicit)) } else { regex_pars <- unlist(lapply(seq_along(patterns), function(j) { grep(patterns[j], complete_pars, value = TRUE) })) if (!length(regex_pars)) { abort("No matches for 'regex_pars'.") } } unique(c(explicit, regex_pars)) } #' Melt a 3-D array or matrix of MCMC draws #' #' @noRd #' @param x An mcmc_array (from prepare_mcmc_array). #' @param varnames,value.name,... Passed to reshape2::melt (array method). #' @return A molten data frame. #' melt_mcmc <- function(x, ...) UseMethod("melt_mcmc") #' @export melt_mcmc.mcmc_array <- function(x, varnames = c("Iteration", "Chain", "Parameter"), value.name = "Value", as.is = TRUE, ...) { stopifnot(is_mcmc_array(x)) long <- reshape2::melt( data = x, varnames = varnames, value.name = value.name, as.is = FALSE, ...) long$Parameter <- factor(long$Parameter) long } # If all chains are already merged #' @export melt_mcmc.matrix <- function(x, varnames = c("Draw", "Parameter"), value.name = "Value", ...) { long <- reshape2::melt( data = x, varnames = varnames, value.name = value.name, as.is = FALSE, ...) long$Parameter <- factor(long$Parameter) long } #' Set dimnames of 3-D array #' @noRd #' @param x 3-D array #' @param parnames Character vector of parameter names #' @return x with a modified dimnames. set_mcmc_dimnames <- function(x, parnames) { stopifnot(is_3d_array(x)) dimnames(x) <- list( Iteration = seq_len(nrow(x)), Chain = seq_len(ncol(x)), Parameter = parnames ) structure(x, class = c(class(x), "mcmc_array")) } #' Convert 3-D array to matrix with chains merged #' #' @noRd #' @param x A 3-D array (iter x chain x param) #' @return A matrix with one column per parameter #' merge_chains <- function(x) { xdim <- dim(x) mat <- array(x, dim = c(prod(xdim[1:2]), xdim[3])) colnames(mat) <- parameter_names(x) mat } #' Check if an object is a data.frame with a chain index column #' #' @noRd #' @param x object to check #' @return TRUE or FALSE is_df_with_chain <- function(x) { is.data.frame(x) && any(tolower(colnames(x)) %in% "chain") } validate_df_with_chain <- function(x) { stopifnot(is_df_with_chain(x)) x <- as.data.frame(x) if (!is.null(x$chain)) { if (is.null(x$Chain)) { x$Chain <- x$chain } x$chain <- NULL } x$Chain <- as.integer(x$Chain) x } # Convert data.frame with Chain variable to a 3-D array df_with_chain2array <- function(x) { x <- validate_df_with_chain(x) chain <- x$Chain n_chain <- length(unique(chain)) a <- x[, !colnames(x) %in% "Chain", drop = FALSE] parnames <- colnames(a) a <- as.matrix(a) x <- array(NA, dim = c(ceiling(nrow(a) / n_chain), n_chain, ncol(a))) for (j in seq_len(n_chain)) { x[, j, ] <- a[chain == j,, drop=FALSE] } set_mcmc_dimnames(x, parnames) } #' Check if an object is a list (but not a data.frame) that contains #' all 2-D objects #' @noRd #' @param x object to check #' @return TRUE or FALSE is_chain_list <- function(x) { check1 <- !is.data.frame(x) && is.list(x) dims <- try(sapply(x, function(chain) length(dim(chain))), silent=TRUE) if (inherits(dims, "try-error")) { return(FALSE) } check2 <- isTRUE(all(dims == 2)) # all elements of list should be matrices/2-D arrays check1 && check2 } validate_chain_list <- function(x) { n_chain <- length(x) for (i in seq_len(n_chain)) { nms <- colnames(as.matrix(x[[i]])) if (is.null(nms) || !all(nzchar(nms))) { abort(paste( "Some parameters are missing names.", "Check the column names for the matrices in your list of chains." )) } } if (n_chain > 1) { n_iter <- sapply(x, nrow) same_iters <- length(unique(n_iter)) == 1 if (!same_iters) { abort("Each chain should have the same number of iterations.") } cnames <- sapply(x, colnames) if (is.array(cnames)) { same_params <- identical(cnames[, 1], cnames[, 2]) } else { same_params <- length(unique(cnames)) == 1 } if (!same_params) { abort(paste( "The parameters for each chain should be in the same order", "and have the same names." )) } } x } # Convert list of matrices to 3-D array chain_list2array <- function(x) { x <- validate_chain_list(x) n_chain <- length(x) if (n_chain == 1) { n_iter <- nrow(x[[1]]) param_names <- colnames(x[[1]]) } else { n_iter <- sapply(x, nrow) cnames <- sapply(x, colnames) param_names <- if (is.array(cnames)) cnames[, 1] else cnames n_iter <- n_iter[1] } param_names <- unique(param_names) n_param <- length(param_names) out <- array(NA, dim = c(n_iter, n_chain, n_param)) for (i in seq_len(n_chain)) { out[, i,] <- x[[i]] } set_mcmc_dimnames(out, param_names) } # Get parameter names from a 3-D array parameter_names <- function(x) UseMethod("parameter_names") #' @export parameter_names.array <- function(x) { stopifnot(is_3d_array(x)) dimnames(x)[[3]] %||% abort("No parameter names found.") } #' @export parameter_names.default <- function(x) { colnames(x) %||% abort("No parameter names found.") } #' @export parameter_names.matrix <- function(x) { colnames(x) %||% abort("No parameter names found.") } # Check if an object is a 3-D array is_3d_array <- function(x) { if (!is.array(x)) { return(FALSE) } if (length(dim(x)) != 3) { return(FALSE) } TRUE } # Check if an object is a 3-D array AND has correct dimension names is_mcmc_array <- function(x) { if (!is_3d_array(x)) { return(FALSE) } if (!identical(names(dimnames(x)), c("Iteration", "Chain", "Parameter"))) { return(FALSE) } TRUE } # Check if 3-D array has multiple chains has_multiple_chains <- function(x) { stopifnot(is_3d_array(x)) isTRUE(dim(x)[2] > 1) } # Check if 3-D array has multiple parameters has_multiple_params <- function(x) { stopifnot(is_3d_array(x)) isTRUE(dim(x)[3] > 1) } STOP_need_multiple_chains <- function(call. = FALSE) { abort("This function requires multiple chains.") } # Validate that transformations match parameter names validate_transformations <- function(transformations = list(), pars = character()) { if (is.null(names(transformations))) { abort("'transformations' must be a _named_ list.") } else if (any(!nzchar(names(transformations)))) { abort("Each element of 'transformations' must have a name.") } transformations <- lapply(transformations, match.fun) if (!all(names(transformations) %in% pars)) { not_found <- which(!names(transformations) %in% pars) abort(paste( "Some names(transformations) don't match parameter names:", paste(names(transformations)[not_found], collapse = ", ") )) } transformations } #' Apply transformations to matrix or 3-D array of parameter draws #' #' @noRd #' @param x A matrix or 3-D array of draws #' @param transformation User's 'transformations' argument to one of the mcmc_* #' functions. #' @return x, with tranformations having been applied to some parameters. #' apply_transformations <- function(x, ...) { UseMethod("apply_transformations") } #' @export apply_transformations.matrix <- function(x, ..., transformations = list()) { pars <- colnames(x) x_transforms <- validate_transformations(transformations, pars) for (p in names(x_transforms)) { x[, p] <- x_transforms[[p]](x[, p]) } x } #' @export apply_transformations.array <- function(x, ..., transformations = list()) { stopifnot(length(dim(x)) == 3) pars <- dimnames(x)[[3]] x_transforms <- validate_transformations(transformations, pars) for (p in names(x_transforms)) { x[, , p] <- x_transforms[[p]](x[, , p]) } x } rename_transformed_pars <- function(pars, transformations) { stopifnot(is.character(pars), is.list(transformations)) has_names <- sapply(transformations, is.character) if (any(has_names)) { nms <- names(which(has_names)) for (nm in nms) { pars[which(pars == nm)] <- paste0( transformations[[nm]], "(", pars[which(pars == nm)], ")" ) } } if (any(!has_names)) { nms <- names(which(!has_names)) pars[pars %in% nms] <- paste0("t(", pars[pars %in% nms], ")") } pars } num_chains <- function(x, ...) UseMethod("num_chains") num_iters <- function(x, ...) UseMethod("num_iters") num_params <- function(x, ...) UseMethod("num_params") #' @export num_params.mcmc_array <- function(x, ...) dim(x)[3] #' @export num_chains.mcmc_array <- function(x, ...) dim(x)[2] #' @export num_iters.mcmc_array <- function(x, ...) dim(x)[1] #' @export num_params.data.frame <- function(x, ...) { stopifnot("Parameter" %in% colnames(x)) length(unique(x$Parameter)) } #' @export num_chains.data.frame <- function(x, ...) { stopifnot("Chain" %in% colnames(x)) length(unique(x$Chain)) } #' @export num_iters.data.frame <- function(x, ...) { cols <- colnames(x) stopifnot("Iteration" %in% cols || "Draws" %in% cols) if ("Iteration" %in% cols) { n <- length(unique(x$Iteration)) } else { n <- length(unique(x$Draw)) } n } bayesplot/R/mcmc-overview.R0000644000176200001440000000600414212422623015361 0ustar liggesusers#' Plots for Markov chain Monte Carlo simulations #' #' @name MCMC-overview #' @aliases MCMC #' @family MCMC #' #' @description #' The **bayesplot** MCMC module provides various plotting functions for #' creating graphical displays of Markov chain Monte Carlo (MCMC) simulations. #' The **MCMC plotting functions** section, below, provides links to the #' documentation for various categories of MCMC plots. Currently the MCMC #' plotting functions accept posterior draws provided in one of the following #' formats: #' * __3-D array__: An array with dimensions `Iteration, Chain, Parameter` in #' that order. #' * __list__: A list of matrices, where each matrix corresponds to a Markov #' chain. All of the matrices should have the same number of iterations (rows) #' and parameters (columns), and parameters should have the same names and be in #' the same order. #' * __matrix (2-D array)__: A matrix with one column per parameter. If using #' matrix there should only be a single Markov chain or all chains should #' already be merged (stacked). #' * __data frame__: There are two types of data frames allowed. Either a data #' frame with one column per parameter (if only a single chain or all chains #' have already been merged), or a data frame with one column per parameter plus #' an additional column `"Chain"` that contains the chain number (an integer) #' corresponding to each row in the data frame. #' * __draws__: Any of the `draws` formats supported by the #' \pkg{\link{posterior}} package. #' #' __Note__: typically the user should *not* include warmup iterations #' in the object passed to **bayesplot** plotting functions, although for #' certain plots (e.g. trace plots) it can occasionally be useful to include the #' warmup iterations for diagnostic purposes. #' #' @section MCMC plotting functions: #' #' * [Posterior distributions][MCMC-distributions]: #' Histograms and kernel density plots of parameter draws, optionally #' showing each Markov chain separately. #' * [Uncertainty intervals][MCMC-intervals]: Uncertainty intervals computed #' from parameter draws. #' * [Trace plots][MCMC-traces]: Times series of parameter draws, optionally #' including HMC/NUTS diagnostic information. #' * [Scatterplots][MCMC-scatterplots]: Scatterplots, heatmaps, and pairs #' plots of parameter draws, optionally including HMC/NUTS diagnostic #' information. #' * [Parallel coordinates plots][MCMC-parcoord]: Parallel coordinates plot #' of MCMC draws (one dimension per parameter), optionally including #' HMC/NUTS diagnostic information. #' * [Combos][MCMC-combos]: Combination plots (e.g. trace plot + histogram). #' * [General MCMC diagnostics][MCMC-diagnostics]: MCMC diagnostic plots #' including R-hat, effective sample size, autocorrelation. #' [NUTS diagnostics][MCMC-nuts]: Special diagnostic plots for #' the No-U-Turn Sampler. #' * [Comparisons to "true" values][MCMC-recover]: Plots comparing MCMC #' estimates to "true" parameter values (e.g., values used to simulate data). #' #' @template reference-vis-paper #' NULL bayesplot/R/ppd-distributions.R0000644000176200001440000002070714556270424016302 0ustar liggesusers#' PPD distributions #' #' Plot posterior or prior predictive distributions. Each of these functions #' makes the same plot as the corresponding [`ppc_`][PPC-distributions] function #' but without plotting any observed data `y`. The **Plot Descriptions** section #' at [PPC-distributions] has details on the individual plots. #' #' @name PPD-distributions #' @family PPDs #' #' @template args-ypred #' @inheritParams PPC-distributions #' #' @template details-binomial #' @template return-ggplot-or-data #' #' @examples #' # difference between ppd_dens_overlay() and ppc_dens_overlay() #' color_scheme_set("brightblue") #' preds <- example_yrep_draws() #' ppd_dens_overlay(ypred = preds[1:50, ]) #' ppc_dens_overlay(y = example_y_data(), yrep = preds[1:50, ]) #' NULL #' @rdname PPD-distributions #' @export ppd_data <- function(ypred, group = NULL) { ypred <- validate_predictions(ypred) if (!is.null(group)) { group <- validate_group(group, n_obs = ncol(ypred)) } .ppd_data(predictions = ypred, y = NULL, group = group) } #' @rdname PPD-distributions #' @export ppd_dens_overlay <- function(ypred, ..., size = 0.25, alpha = 0.7, trim = FALSE, bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024) { check_ignored_arguments(...) data <- ppd_data(ypred) ggplot(data, mapping = aes(x = .data$value)) + overlay_ppd_densities( mapping = aes(group = .data$rep_id, color = "ypred"), linewidth = size, alpha = alpha, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n = n_dens ) + scale_color_ppd( values = get_color("m"), guide = guide_legend( # in case user turns legend back on override.aes = list(size = 2 * size, alpha = 1)) ) + bayesplot_theme_get() + dont_expand_axes() + yaxis_title(FALSE) + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + legend_none() } #' @rdname PPD-distributions #' @export ppd_ecdf_overlay <- function(ypred, ..., discrete = FALSE, pad = TRUE, size = 0.25, alpha = 0.7) { check_ignored_arguments(...) data <- ppd_data(ypred) ggplot(data, mapping = aes(x = .data$value)) + hline_at( c(0, 0.5, 1), linewidth = c(0.2, 0.1, 0.2), linetype = 2, color = get_color("dh") ) + stat_ecdf( mapping = aes(group = .data$rep_id, color = "ypred"), geom = if (discrete) "step" else "line", linewidth = size, alpha = alpha, pad = pad ) + scale_color_ppd( values = get_color("m"), guide = guide_legend( # in case user turns legend back on override.aes = list(linewidth = 2 * size, alpha = 1)) ) + scale_y_continuous(breaks = c(0, 0.5, 1)) + bayesplot_theme_get() + yaxis_title(FALSE) + xaxis_title(FALSE) + legend_none() } #' @rdname PPD-distributions #' @export ppd_dens <- function(ypred, ..., trim = FALSE, size = 0.5, alpha = 1) { check_ignored_arguments(...) data <- ppd_data(ypred) ggplot(data, mapping = aes( x = .data$value, color = "ypred", fill = "ypred" )) + geom_density( linewidth = size, alpha = alpha, trim = trim ) + scale_color_ppd() + scale_fill_ppd() + bayesplot_theme_get() + facet_wrap_parsed("rep_label") + force_axes_in_facets() + dont_expand_y_axis() + legend_none() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) + facet_text(FALSE) } #' @rdname PPD-distributions #' @export ppd_hist <- function(ypred, ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { check_ignored_arguments(...) data <- ppd_data(ypred) ggplot(data, mapping = set_hist_aes( freq, color = "ypred", fill = "ypred" )) + geom_histogram( size = 0.25, binwidth = binwidth, bins = bins, breaks = breaks ) + scale_color_ppd() + scale_fill_ppd() + bayesplot_theme_get() + facet_wrap_parsed("rep_label") + force_axes_in_facets() + dont_expand_y_axis() + legend_none() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) + facet_text(FALSE) } #' @rdname PPD-distributions #' @export ppd_freqpoly <- function(ypred, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppd_data(ypred, group = dots$group) ggplot(data, mapping = set_hist_aes( freq, color = "ypred", fill = "ypred" )) + geom_area( stat = "bin", binwidth = binwidth, bins = bins, size = size, alpha = alpha ) + facet_wrap_parsed("rep_label") + scale_color_ppd() + scale_fill_ppd() + bayesplot_theme_get() + force_axes_in_facets() + dont_expand_y_axis() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) + facet_text(FALSE) + legend_none() } #' @rdname PPD-distributions #' @export ppd_freqpoly_grouped <- function(ypred, group, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppd_freqpoly", call), parent.frame()) g + facet_grid( rep_label ~ group, scales = "free", labeller = label_parsed ) + force_axes_in_facets() + facet_text() + theme(strip.text.y = element_blank()) } #' @rdname PPD-distributions #' @export ppd_boxplot <- function(ypred, ..., notch = TRUE, size = 0.5, alpha = 1) { check_ignored_arguments(...) data <- ppd_data(ypred) ggplot(data, mapping = aes( x = .data$rep_label, y = .data$value, color = "ypred", fill = "ypred" )) + geom_boxplot( notch = notch, linewidth = size, alpha = alpha, outlier.color = get_color("lh"), outlier.alpha = 2/3, outlier.size = 1 ) + scale_color_ppd() + scale_fill_ppd() + scale_x_discrete(labels = function(x) parse(text=x)) + bayesplot_theme_get() + yaxis_title(FALSE) + xaxis_ticks(FALSE) + xaxis_text(FALSE) + xaxis_title(FALSE) + legend_none() } # internal ---------------------------------------------------------------- #' Back end for both `ppd_data()` and `ppc_data()` #' #' @noRd #' @param predictions SxN matrix of predictions (`ypred` or `yrep`) already validated. #' @param y User's `y` argument (if applicable), already validated. #' @param group User's `group` argument, already validated. #' @return A molten data frame of predictions, possible including `y`. #' @importFrom dplyr left_join select .ppd_data <- function(predictions, y = NULL, group = NULL) { if (!is.null(y)) { data <- melt_and_stack(y, predictions) } else { data <- melt_predictions(predictions) levels(data$rep_label) <- gsub("rep", "pred", levels(data$rep_label)) } if (!is.null(group)) { group_indices <- tibble::tibble(group, y_id = seq_along(group)) data <- data %>% left_join(group_indices, by = "y_id") %>% select("group", tidyselect::everything()) } data } #' Wrapper for stat_density with some argument defaults changed #' #' This function is called internally by `ppd_dens_overlay()` and #' `ppc_dens_overlay()`. #' #' @param geom,position Arguments passed to [ggplot2::stat_density()] but #' with different defaults. #' @param ... All arguments other than `geom` and `position` to pass to #' `stat_density()`. The defaults will be the same as for `stat_density()`. #' @return Object returned by `stat_density()`. #' @noRd overlay_ppd_densities <- function(..., geom = "line", position = "identity") { stat_density(..., geom = geom, position = position) } bayesplot/R/ppc-discrete.R0000644000176200001440000003143214556270424015176 0ustar liggesusers#' PPCs for discrete outcomes #' #' Many of the [PPC][PPC-overview] functions in **bayesplot** can #' be used with discrete data. The small subset of these functions that can #' *only* be used if `y` and `yrep` are discrete are documented #' on this page. Currently these include rootograms for count outcomes and bar #' plots for ordinal, categorical, and multinomial outcomes. See the #' **Plot Descriptions** section below. #' #' @name PPC-discrete #' @family PPCs #' #' @template args-y-yrep #' @param ... Currently unused. #' @param prob A value between `0` and `1` indicating the desired probability #' mass to include in the `yrep` intervals. Set `prob=0` to remove the #' intervals. (Note: for rootograms these are intervals of the *square roots* #' of the expected counts.) #' @param width For bar plots only, passed to [ggplot2::geom_bar()] to control #' the bar width. #' @param size,fatten,linewidth For bar plots, `size`, `fatten`, and `linewidth` #' are passed to [ggplot2::geom_pointrange()] to control the appearance of the #' `yrep` points and intervals. For rootograms `size` is passed to #' [ggplot2::geom_line()]. #' @param freq For bar plots only, if `TRUE` (the default) the y-axis will #' display counts. Setting `freq=FALSE` will put proportions on the y-axis. #' #' @template return-ggplot-or-data #' #' @details For all of these plots `y` and `yrep` must be integers, although #' they need not be integers in the strict sense of \R's #' [integer][base::integer] type. For rootogram plots `y` and `yrep` must also #' be non-negative. #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_bars()`}{ #' Bar plot of `y` with `yrep` medians and uncertainty intervals #' superimposed on the bars. #' } #' \item{`ppc_bars_grouped()`}{ #' Same as `ppc_bars()` but a separate plot (facet) is generated for each #' level of a grouping variable. #' } #' \item{`ppc_rootogram()`}{ #' Rootograms allow for diagnosing problems in count data models such as #' overdispersion or excess zeros. They consist of a histogram of `y` with the #' expected counts based on `yrep` overlaid as a line along with uncertainty #' intervals. The y-axis represents the square roots of the counts to #' approximately adjust for scale differences and thus ease comparison between #' observed and expected counts. Using the `style` argument, the histogram #' style can be adjusted to focus on different aspects of the data: #' * _Standing_: basic histogram of observed counts with curve #' showing expected counts. #' * _Hanging_: observed counts counts hanging from the curve #' representing expected counts. #' * _Suspended_: histogram of the differences between expected and #' observed counts. #' #' **All of the rootograms are plotted on the square root scale**. See Kleiber #' and Zeileis (2016) for advice on interpreting rootograms and selecting #' among the different styles. #' } #' } #' #' @examples #' set.seed(9222017) #' #' # bar plots #' f <- function(N) { #' sample(1:4, size = N, replace = TRUE, prob = c(0.25, 0.4, 0.1, 0.25)) #' } #' y <- f(100) #' yrep <- t(replicate(500, f(100))) #' dim(yrep) #' group <- gl(2, 50, length = 100, labels = c("GroupA", "GroupB")) #' #' color_scheme_set("mix-pink-blue") #' ppc_bars(y, yrep) #' #' # split by group, change interval width, and display proportion #' # instead of count on y-axis #' color_scheme_set("mix-blue-pink") #' ppc_bars_grouped(y, yrep, group, prob = 0.5, freq = FALSE) #' #' \dontrun{ #' # example for ordinal regression using rstanarm #' library(rstanarm) #' fit <- stan_polr( #' tobgp ~ agegp, #' data = esoph, #' method = "probit", #' prior = R2(0.2, "mean"), #' init_r = 0.1, #' seed = 12345, #' # cores = 4, #' refresh = 0 #' ) #' #' # coded as character, so convert to integer #' yrep_char <- posterior_predict(fit) #' print(yrep_char[1, 1:4]) #' #' yrep_int <- sapply(data.frame(yrep_char, stringsAsFactors = TRUE), as.integer) #' y_int <- as.integer(esoph$tobgp) #' #' ppc_bars(y_int, yrep_int) #' #' ppc_bars_grouped( #' y = y_int, #' yrep = yrep_int, #' group = esoph$agegp, #' freq=FALSE, #' prob = 0.5, #' fatten = 1, #' size = 1.5 #' ) #' } #' NULL #' @rdname PPC-discrete #' @export ppc_bars <- function(y, yrep, ..., prob = 0.9, width = 0.9, size = 1, fatten = 2.5, linewidth = 1, freq = TRUE) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_bars_data( y = y, yrep = yrep, group = dots$group, prob = prob, freq = freq ) if (!is.null(dots$group)) { limits <- geom_ignore() } else { limits <- expand_limits(y = 1.05 * max(data[["h"]], na.rm = TRUE)) } ggplot(data) + geom_col( data = dplyr::filter(data, !is.na(.data$y_obs)), mapping = aes(x = .data$x, y = .data$y_obs, fill = "y"), color = get_color("lh"), width = width ) + geom_pointrange( mapping = intervals_inner_aes(needs_y = TRUE, color = "yrep"), size = size, fatten = fatten, linewidth = linewidth, na.rm = TRUE ) + scale_color_ppc( values = get_color("d"), labels = yrep_label(), guide = guide_legend(order = 1, override.aes = list(size = .75 * size)) ) + scale_fill_ppc(values = get_color("l"), labels = y_label()) + scale_x_continuous(breaks = pretty) + labs(x = NULL, y = if (freq) "Count" else "Proportion") + dont_expand_y_axis() + bayesplot_theme_get() + limits + reduce_legend_spacing(0.25) } #' @rdname PPC-discrete #' @export #' @template args-group #' @param facet_args An optional list of arguments (other than `facets`) #' passed to [ggplot2::facet_wrap()] to control faceting. ppc_bars_grouped <- function(y, yrep, group, ..., facet_args = list(), prob = 0.9, width = 0.9, size = 1, fatten = 2.5, linewidth = 1, freq = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_bars", call), parent.frame()) if (fixed_y(facet_args)) { g <- g + expand_limits(y = 1.05 * max(g$data[["h"]], na.rm = TRUE)) } g + bars_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPC-discrete #' @export #' @param style For `ppc_rootogram`, a string specifying the rootogram #' style. The options are `"standing"`, `"hanging"`, and #' `"suspended"`. See the **Plot Descriptions** section, below, for #' details on the different styles. #' #' @references #' Kleiber, C. and Zeileis, A. (2016). #' Visualizing count data regressions using rootograms. #' *The American Statistician*. 70(3): 296--303. #' . #' #' @examples #' # rootograms for counts #' y <- rpois(100, 20) #' yrep <- matrix(rpois(10000, 20), ncol = 100) #' #' color_scheme_set("brightblue") #' ppc_rootogram(y, yrep) #' ppc_rootogram(y, yrep, prob = 0) #' #' ppc_rootogram(y, yrep, style = "hanging", prob = 0.8) #' ppc_rootogram(y, yrep, style = "suspended") #' ppc_rootogram <- function(y, yrep, style = c("standing", "hanging", "suspended"), ..., prob = 0.9, size = 1) { check_ignored_arguments(...) style <- match.arg(style) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) if (!all_counts(y)) { abort("ppc_rootogram expects counts as inputs to 'y'.") } if (!all_counts(yrep)) { abort("ppc_rootogram expects counts as inputs to 'yrep'.") } alpha <- (1 - prob) / 2 probs <- c(alpha, 1 - alpha) ymax <- max(y, yrep) xpos <- 0L:ymax # prepare a table for yrep tyrep <- as.list(rep(NA, nrow(yrep))) for (i in seq_along(tyrep)) { tyrep[[i]] <- table(yrep[i,]) matches <- match(xpos, rownames(tyrep[[i]])) tyrep[[i]] <- as.numeric(tyrep[[i]][matches]) } tyrep <- do.call(rbind, tyrep) tyrep[is.na(tyrep)] <- 0 tyexp <- sqrt(colMeans(tyrep)) tyquantile <- sqrt(t(apply(tyrep, 2, quantile, probs = probs))) colnames(tyquantile) <- c("tylower", "tyupper") # prepare a table for y ty <- table(y) ty <- sqrt(as.numeric(ty[match(xpos, rownames(ty))])) if (style == "suspended") { ty <- tyexp - ty } ty[is.na(ty)] <- 0 ypos <- ty / 2 if (style == "hanging") { ypos <- tyexp - ypos } data <- data.frame(xpos, ypos, ty, tyexp, tyquantile) graph <- ggplot(data) + aes( ymin = .data$tylower, ymax = .data$tyupper, height = .data$ty ) + geom_tile( aes( x = .data$xpos, y = .data$ypos, fill = "Observed" ), color = get_color("lh"), linewidth = 0.25, width = 1 ) + bayesplot_theme_get() if (style != "standing") { graph <- graph + hline_0(size = 0.4) } graph <- graph + geom_smooth( aes( x = .data$xpos, y = .data$tyexp, color = "Expected" ), fill = get_color("d"), linewidth = size, stat = "identity" ) + scale_fill_manual("", values = get_color("l")) + scale_color_manual("", values = get_color("dh")) + labs(x = expression(italic(y)), y = expression(sqrt(Count))) if (style == "standing") { graph <- graph + dont_expand_y_axis() } graph + reduce_legend_spacing(0.25) } #' @rdname PPC-discrete #' @export ppc_bars_data <- function(y, yrep, group = NULL, prob = 0.9, freq = TRUE) { stopifnot(0 <= prob && prob <= 1, is.logical(freq)) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) if (!all_whole_number(y)) { abort("ppc_bars expects 'y' to be discrete.") } if (!all_whole_number(yrep)) { abort("ppc_bars expects 'yrep' to be discrete.") } if (!is.null(group)) { group <- validate_group(group, length(y)) } .ppc_bars_data( y = y, yrep = yrep, group = group, prob = prob, freq = freq ) } # internal ---------------------------------------------------------------- #' Internal function for `ppc_bars_data()` #' #' @noRd #' @param y,yrep,group User's already validated `y`, `yrep`, and (if applicable) #' `group` arguments. #' @param prob,freq User's `prob` and `freq` arguments. #' @importFrom dplyr %>% ungroup count arrange mutate summarise across full_join rename all_of .ppc_bars_data <- function(y, yrep, group = NULL, prob = 0.9, freq = TRUE) { alpha <- (1 - prob) / 2 probs <- sort(c(alpha, 0.5, 1 - alpha)) # Prepare for final summary lo <- function(x) quantile(x, probs[1]) mid <- function(x) quantile(x, probs[2]) hi <- function(x) quantile(x, probs[3]) summary_var <- ifelse(freq, "n", "proportion") summary_funs <- list(l = lo, m = mid, h = hi) # use l,m,h like in our intervals data # Set a dummy group for ungrouped data if (is.null(group)) { was_null_group <- TRUE group <- 1 } else{ was_null_group <- FALSE } tmp_data <- data.frame( group = factor(group), y = y, yrep = t(yrep) ) data <- reshape2::melt(tmp_data, id.vars = "group") %>% count(.data$group, .data$value, .data$variable) %>% group_by(.data$variable, .data$group) %>% mutate(proportion = .data$n / sum(.data$n)) %>% ungroup() %>% group_by(.data$group, .data$value) yrep_summary <- data %>% dplyr::filter(!.data$variable == "y") %>% summarise(across(all_of(summary_var), summary_funs, .names = "{.fn}")) %>% ungroup() %>% arrange(.data$group, .data$value) y_summary <- data %>% dplyr::filter(.data$variable == "y") %>% ungroup() %>% rename(y_obs = all_of(summary_var)) %>% arrange(.data$group, .data$value) cols <- syms(c(if (!was_null_group) "group", "x", "y_obs", "l", "m", "h")) # full join to keep empty cells full_join(yrep_summary, y_summary, by = c("group", "value")) %>% rename(x = "value") %>% arrange(.data$x) %>% select(!!!cols) } #' Create the facet layer for grouped bar plots #' @param facet_args User's `facet_args` argument. #' @param scales_default String to use for `scales` argument to `facet_wrap()` #' if not specified by user. The default is `"fixed"` for bar plots. This is #' the same as `ggplot2::facet_wrap()` but different than #' `bayesplot::intervals_group_facets()`, which has a default of `"free"`. #' @return Object returned by `facet_wrap()`. #' @noRd bars_group_facets <- function(facet_args, scales_default = "fixed") { facet_args[["facets"]] <- "group" facet_args[["scales"]] <- facet_args[["scales"]] %||% scales_default do.call("facet_wrap", facet_args) } fixed_y <- function(facet_args) { !isTRUE(facet_args[["scales"]] %in% c("free", "free_y")) } bayesplot/R/mcmc-traces.R0000644000176200001440000006025214556270424015014 0ustar liggesusers#' Trace and rank plots of MCMC draws #' #' Trace and rank plots of MCMC draws. See the **Plot Descriptions** #' section, below, for details. #' #' @name MCMC-traces #' @family MCMC #' @template args-mcmc-x #' @template args-pars #' @template args-regex_pars #' @template args-transformations #' @template args-facet_args #' @template args-pit-ecdf #' @param ... Currently ignored. #' @param size An optional value to override the default line size #' for `mcmc_trace()` or the default point size for `mcmc_trace_highlight()`. #' @param alpha For `mcmc_trace_highlight()`, passed to #' [ggplot2::geom_point()] to control the transparency of the points #' for the chains not highlighted. #' @param n_warmup An integer; the number of warmup iterations included in #' `x`. The default is `n_warmup = 0`, i.e. to assume no warmup #' iterations are included. If `n_warmup > 0` then the background for #' iterations `1:n_warmup` is shaded gray. #' @param iter1 An integer; the iteration number of the first included draw #' (default is `0`). This can be used to make it more obvious that the warmup #' iterations have been discarded from the traceplot. It cannot be specified #' if `n_warmup` is also set to a positive value. #' @param window An integer vector of length two specifying the limits of a #' range of iterations to display. #' @param np For models fit using [NUTS] (more generally, any #' [symplectic integrator](https://en.wikipedia.org/wiki/Symplectic_integrator)), #' an optional data frame providing NUTS diagnostic information. The data #' frame should be the object returned by [nuts_params()] or one with the same #' structure. If `np` is specified then tick marks are added to the bottom of #' the trace plot indicating within which iterations there was a divergence #' (if there were any). See the end of the **Examples** section, below. #' @param np_style A call to the `trace_style_np()` helper function to #' specify arguments controlling the appearance of tick marks representing #' divergences (if the `np` argument is specified). #' @param divergences Deprecated. Use the `np` argument instead. #' #' @template return-ggplot-or-data #' @return `mcmc_trace_data()` returns the data for the trace *and* rank plots #' in the same data frame. #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_trace()`}{ #' Standard trace plots of MCMC draws. For models fit using [NUTS], #' the `np` argument can be used to also show divergences on the trace plot. #' } #' \item{`mcmc_trace_highlight()`}{ #' Traces are plotted using points rather than lines and the opacity of all #' chains but one (specified by the `highlight` argument) is reduced. #' } #' \item{`mcmc_rank_hist()`}{ #' Whereas traditional trace plots visualize how the chains mix over the #' course of sampling, rank histograms visualize how the values #' from the chains mix together in terms of ranking. An ideal plot would #' show the rankings mixing or overlapping in a uniform distribution. #' See Vehtari et al. (2019) for details. #' } #' \item{`mcmc_rank_overlay()`}{ #' Ranks from `mcmc_rank_hist()` are plotted using overlaid lines in a #' single panel. #' } #' \item{`mcmc_rank_ecdf()`}{ #' The ECDFs of the ranks from `mcmc_rank_hist()` are plotted with the #' simultaneous confidence bands with a coverage determined by `prob`, that #' is, bands that completely cover all of the rank ECDFs with the probability #' `prob`. If `plot_diff = TRUE`, the difference between the observed rank #' ECDFs and the theoretical expectation for samples originating from the #' same distribution is drawn. See Säilynoja et al. (2021) for details. #' } #' } #' #' @template reference-improved-rhat #' @template reference-uniformity-test #' @examples #' # some parameter draws to use for demonstration #' x <- example_mcmc_draws(chains = 4, params = 6) #' dim(x) #' dimnames(x) #' #' # trace plots of the betas #' color_scheme_set("viridis") #' mcmc_trace(x, regex_pars = "beta") #' \donttest{ #' color_scheme_set("viridisA") #' mcmc_trace(x, regex_pars = "beta") #' #' color_scheme_set("viridisC") #' mcmc_trace(x, regex_pars = "beta") #' } #' #' # mix color schemes #' color_scheme_set("mix-blue-red") #' mcmc_trace(x, regex_pars = "beta") #' #' # use traditional ggplot discrete color scale #' mcmc_trace(x, pars = c("alpha", "sigma")) + #' ggplot2::scale_color_discrete() #' #' # zoom in on a window of iterations, increase line size, #' # add tick marks, move legend to the top, add gray background #' color_scheme_set("viridisA") #' mcmc_trace(x[,, 1:4], window = c(100, 130), size = 1) + #' panel_bg(fill = "gray90", color = NA) + #' legend_move("top") #' #' # Rank-normalized histogram plots. Instead of showing how chains mix over #' # time, look at how the ranking of MCMC samples mixed between chains. #' color_scheme_set("viridisE") #' mcmc_rank_hist(x, "alpha") #' mcmc_rank_hist(x, pars = c("alpha", "sigma"), ref_line = TRUE) #' mcmc_rank_overlay(x, "alpha") #' #' # ECDF and ECDF difference plots of the ranking of MCMC samples between chains. #' # Provide 99% simultaneous confidence intervals for the chains sampling from #' # the same distribution. #' mcmc_rank_ecdf(x, prob = 0.99) #' mcmc_rank_ecdf(x, prob = 0.99, plot_diff = TRUE) #' #' \dontrun{ #' # parse facet label text #' color_scheme_set("purple") #' p <- mcmc_trace( #' x, #' regex_pars = "beta\\[[1,3]\\]", #' facet_args = list(labeller = ggplot2::label_parsed) #' ) #' p + facet_text(size = 15) #' #' # mark first 100 draws as warmup #' mcmc_trace(x, n_warmup = 100) #' #' # plot as points, highlighting chain 2 #' color_scheme_set("brightblue") #' mcmc_trace_highlight(x, pars = "sigma", highlight = 2, size = 2) #' #' # for models fit using HMC/NUTS divergences can be displayed in the trace plot #' library("rstanarm") #' fit <- stan_glm(mpg ~ ., data = mtcars, refresh = 0, #' # next line to keep example fast and also ensure we get some divergences #' prior = hs(), iter = 400, adapt_delta = 0.8) #' #' # extract draws using as.array (instead of as.matrix) to keep #' # chains separate for trace plot #' posterior <- as.array(fit) #' #' # for stanfit and stanreg objects use nuts_params() to get the divergences #' mcmc_trace(posterior, pars = "sigma", np = nuts_params(fit)) #' #' color_scheme_set("viridis") #' mcmc_trace( #' posterior, #' pars = c("wt", "sigma"), #' size = 0.5, #' facet_args = list(nrow = 2), #' np = nuts_params(fit), #' np_style = trace_style_np(div_color = "black", div_size = 0.5) #' ) #' } #' NULL #' @rdname MCMC-traces #' @export mcmc_trace <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), n_warmup = 0, iter1 = 0, window = NULL, size = NULL, np = NULL, np_style = trace_style_np(), divergences = NULL) { # deprecate 'divergences' arg in favor of 'np' # (for consistency across functions) if (!is.null(np) && !is.null(divergences)) { abort(paste0( "'np' and 'divergences' can't both be specified. ", "Use only 'np' (the 'divergences' argument is deprecated)." )) } else if (!is.null(divergences)) { warn(paste0( "The 'divergences' argument is deprecated ", "and will be removed in a future release. ", "Use the 'np' argument instead." )) np <- divergences } check_ignored_arguments(...) .mcmc_trace( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, n_warmup = n_warmup, window = window, size = size, style = "line", np = np, np_style = np_style, iter1 = iter1, ... ) } #' @rdname MCMC-traces #' @export #' @param highlight For `mcmc_trace_highlight()`, an integer specifying one #' of the chains that will be more visible than the others in the plot. mcmc_trace_highlight <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), n_warmup = 0, window = NULL, size = NULL, alpha = 0.2, highlight = 1) { check_ignored_arguments(...) .mcmc_trace( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, n_warmup = n_warmup, window = window, size = size, alpha = alpha, highlight = highlight, style = "point", ... ) } #' @rdname MCMC-traces #' @export #' @param div_color,div_size,div_alpha Optional arguments to the #' `trace_style_np()` helper function that are eventually passed to #' [ggplot2::geom_rug()] if the `np` argument is also specified. They control #' the color, size, and transparency specifications for showing divergences in #' the plot. The default values are displayed in the **Usage** section above. #' trace_style_np <- function(div_color = "red", div_size = 0.25, div_alpha = 1) { stopifnot( is.character(div_color), is.numeric(div_size), is.numeric(div_alpha) && div_alpha >= 0 && div_alpha <= 1 ) style <- list( color = c(div = div_color), size = c(div = div_size), alpha = c(div = div_alpha) ) structure(style, class = c(class(style), "nuts_style")) } #' @rdname MCMC-traces #' @param n_bins For the rank plots, the number of bins to use for the histogram #' of rank-normalized MCMC samples. Defaults to `20`. #' @param ref_line For the rank plots, whether to draw a horizontal line at the #' average number of ranks per bin. Defaults to `FALSE`. #' @export mcmc_rank_overlay <- function(x, pars = character(), regex_pars = character(), transformations = list(), facet_args = list(), ..., n_bins = 20, ref_line = FALSE) { check_ignored_arguments(...) data <- mcmc_trace_data( x, pars = pars, regex_pars = regex_pars, transformations = transformations ) n_chains <- unique(data$n_chains) n_param <- unique(data$n_parameters) # We have to bin and count the data ourselves because # ggplot2::stat_bin(geom = "step") does not draw the final bin. histobins <- data %>% dplyr::distinct(.data$value_rank) %>% mutate(cut = cut(.data$value_rank, n_bins)) %>% group_by(.data$cut) %>% mutate(bin_start = min(.data$value_rank)) %>% ungroup() %>% select(-c("cut")) d_bin_counts <- data %>% left_join(histobins, by = "value_rank") %>% count(.data$parameter, .data$chain, .data$bin_start) # Duplicate the final bin, setting the left edge to the greatest x value, so # that the entire x-axis is used, right_edge <- max(data$value_rank) d_bin_counts <- d_bin_counts %>% dplyr::filter(.data$bin_start == max(.data$bin_start)) %>% mutate(bin_start = right_edge) %>% dplyr::bind_rows(d_bin_counts) scale_color <- scale_color_manual("Chain", values = chain_colors(n_chains)) layer_ref_line <- if (ref_line) { geom_hline( yintercept = (right_edge / n_bins) / n_chains, color = get_color("dark_highlight"), size = 1, linetype = "dashed" ) } else { NULL } facet_call <- NULL if (n_param > 1) { facet_args$facets <- vars(.data$parameter) facet_args$scales <- facet_args$scales %||% "fixed" facet_call <- do.call("facet_wrap", facet_args) } ggplot(d_bin_counts) + aes(x = .data$bin_start, y = .data$n, color = .data$chain) + geom_step() + layer_ref_line + facet_call + scale_color + ylim(c(0, NA)) + bayesplot_theme_get() + force_x_axis_in_facets() + labs(x = "Rank", y = NULL) } #' @rdname MCMC-traces #' @export mcmc_rank_hist <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), n_bins = 20, ref_line = FALSE) { check_ignored_arguments(...) data <- mcmc_trace_data( x, pars = pars, regex_pars = regex_pars, transformations = transformations ) n_iter <- unique(data$n_iterations) n_chains <- unique(data$n_chains) n_param <- unique(data$n_parameters) # Create a dataframe with chain x parameter x min(rank) x max(rank) to set # x axis range in each facet data_boundaries <- data %>% dplyr::distinct(.data$chain, .data$parameter) data_boundaries <- dplyr::bind_rows( mutate(data_boundaries, value_rank = min(data$value_rank)), mutate(data_boundaries, value_rank = max(data$value_rank)) ) right_edge <- max(data_boundaries$value_rank) facet_args[["scales"]] <- facet_args[["scales"]] %||% "fixed" # If there is one parameter, put the chains in one row. # Otherwise, use a grid. if (n_param > 1) { facet_f <- facet_grid facet_args[["rows"]] <- facet_args[["rows"]] %||% vars(.data$parameter) facet_args[["cols"]] <- facet_args[["cols"]] %||% vars(.data$chain) } else { facet_f <- facet_wrap facet_args[["facets"]] <- vars(.data$parameter, .data$chain) facet_args[["nrow"]] <- facet_args[["nrow"]] %||% 1 labeller <- function(x) label_value(x, multi_line = FALSE) facet_args[["labeller"]] <- facet_args[["labeller"]] %||% labeller } layer_ref_line <- if (ref_line) { geom_hline( yintercept = (right_edge / n_bins) / n_chains, color = get_color("dark_highlight"), size = .5, linetype = "dashed" ) } else { NULL } facet_call <- do.call(facet_f, facet_args) ggplot(data) + aes(x = .data$value_rank) + geom_histogram( color = get_color("mid_highlight"), fill = get_color("mid"), binwidth = right_edge / n_bins, boundary = right_edge, linewidth = 0.25 ) + layer_ref_line + geom_blank(data = data_boundaries) + facet_call + force_x_axis_in_facets() + dont_expand_y_axis(c(0.005, 0)) + bayesplot_theme_get() + theme( axis.line.y = element_blank(), axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank() ) + labs(x = "Rank") } #' @rdname MCMC-traces #' @param prob For `mcmc_rank_ecdf()`, a value between 0 and 1 #' specifying the desired simultaneous confidence of the confidence bands to be #' drawn for the rank ECDF plots. #' @param plot_diff For `mcmc_rank_ecdf()`, a boolean specifying if the #' difference between the observed rank ECDFs and the theoretical expectation #' should be drawn instead of the unmodified rank ECDF plots. #' @export mcmc_rank_ecdf <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., K = NULL, facet_args = list(), prob = 0.99, plot_diff = FALSE, interpolate_adj = NULL) { check_ignored_arguments(..., ok_args = c("K", "pit", "prob", "plot_diff", "interpolate_adj", "M") ) data <- mcmc_trace_data( x, pars = pars, regex_pars = regex_pars, transformations = transformations, highlight = 1 ) n_iter <- unique(data$n_iterations) n_chain <- unique(data$n_chains) n_param <- unique(data$n_parameters) x <- if (is.null(K)) { 0:n_iter / n_iter } else { 0:K / K } gamma <- adjust_gamma( N = n_iter, L = n_chain, K = if (is.null(K)) { n_iter } else { K }, prob = prob, interpolate_adj = interpolate_adj, ... ) lims <- ecdf_intervals( gamma = gamma, N = n_iter, K = if (is.null(K)) { n_iter } else { K }, L <- n_chain ) data_lim <- data.frame( upper = lims$upper / n_iter - (plot_diff == TRUE) * x, lower = lims$lower / n_iter - (plot_diff == TRUE) * x, x = x ) data <- data %>% group_by(.data$parameter, .data$chain) %>% dplyr::group_map(~ data.frame( parameter = .y[1], chain = .y[2], ecdf_value = ecdf(.x$value_rank / (n_iter * n_chain))(x) - (plot_diff == TRUE) * x, x = x )) %>% dplyr::bind_rows() mapping <- aes( x = .data$x, y = .data$ecdf_value, color = .data$chain, group = .data$chain ) scale_color <- scale_color_manual("Chain", values = chain_colors(n_chain)) facet_call <- NULL if (n_param == 1) { facet_call <- ylab(levels(data$parameter)) } else { facet_args$facets <- vars(.data$parameter) facet_args$scales <- facet_args$scales %||% "free" facet_call <- do.call("facet_wrap", facet_args) } ggplot() + geom_step(data = data_lim, aes(x = .data$x, y = .data$upper), show.legend = FALSE) + geom_step(data = data_lim, aes(x = .data$x, y = .data$lower), show.legend = FALSE) + geom_step(mapping, data) + bayesplot_theme_get() + scale_color + facet_call + scale_x_continuous(breaks = pretty) + legend_move(ifelse(n_chain > 1, "right", "none")) + xaxis_title(FALSE) + yaxis_title(on = n_param == 1) } #' @rdname MCMC-traces #' @export mcmc_trace_data <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., highlight = NULL, n_warmup = 0, iter1 = 0) { check_ignored_arguments(...) x <- prepare_mcmc_array(x, pars, regex_pars, transformations) if (iter1 < 0) { abort("'iter1' cannot be negative.") } if (n_warmup > 0 && iter1 > 0) { abort("'n_warmup' and 'iter1' can't both be specified.") } if (!is.null(highlight)) { stopifnot(length(highlight) == 1) if (!has_multiple_chains(x)){ STOP_need_multiple_chains() } if (!highlight %in% seq_len(ncol(x))) { abort(paste0( "'highlight' is ", highlight, ", but 'x' contains ", ncol(x), " chains." )) } } data <- melt_mcmc(x) data$Chain <- factor(data$Chain) data$n_chains <- num_chains(data) data$n_iterations <- num_iters(data) data$n_parameters <- num_params(data) data <- rlang::set_names(data, tolower) first_cols <- syms(c("parameter", "value", "value_rank")) data <- data %>% group_by(.data$parameter) %>% mutate( value_rank = rank(.data$value, ties.method = "average") ) %>% ungroup() %>% select(!!! first_cols, tidyselect::everything()) data$highlight <- if (!is.null(highlight)) { data$chain == highlight } else { FALSE } data$warmup <- data$iteration <= n_warmup data$iteration <- data$iteration + as.integer(iter1) tibble::as_tibble(data) } # internal ----------------------------------------------------------------- .mcmc_trace <- function(x, pars = character(), regex_pars = character(), transformations = list(), n_warmup = 0, window = NULL, size = NULL, facet_args = list(), highlight = NULL, style = c("line", "point"), alpha = 0.2, np = NULL, np_style = trace_style_np(), iter1 = 0, ...) { style <- match.arg(style) data <- mcmc_trace_data( x, pars = pars, regex_pars = regex_pars, transformations = transformations, highlight = highlight, n_warmup = n_warmup, iter1 = iter1 ) n_iter <- unique(data$n_iterations) n_chain <- unique(data$n_chains) n_param <- unique(data$n_parameters) mapping <- aes( x = .data$iteration, y = .data$value, color = .data$chain ) if (!is.null(highlight)) { mapping <- modify_aes( mapping, alpha = .data$highlight, color = .data$highlight ) } layer_warmup <- if (n_warmup > 0) { layer_warmup <- annotate( "rect", xmin = -Inf, xmax = n_warmup, ymin = -Inf, ymax = Inf, linewidth = 1, color = "gray88", fill = "gray88", alpha = 0.5 ) } else { NULL } geom_args <- list() if (style == "line") { geom_args$linewidth = size %||% 1 / 3 } else { geom_args$size = size %||% 1 } layer_draws <- do.call(paste0("geom_", style), geom_args) coord_window <- if (!is.null(window)) { stopifnot(length(window) == 2) coord_cartesian(xlim = window) } else { NULL } scale_alpha <- NULL scale_color <- NULL div_rug <- NULL div_guides <- NULL if (!is.null(highlight)) { ## scale_alpha_discrete() warns on default scale_alpha <- scale_alpha_ordinal(range = c(alpha, 1), guide = "none") scale_color <- scale_color_manual( "", values = get_color(c("lh", "d")), labels = c("Other chains", paste("Chain", highlight))) } else { scale_color <- scale_color_manual("Chain", values = chain_colors(n_chain)) if (!is.null(np)) { div_rug <- divergence_rug(np, np_style, n_iter, n_chain) if (!is.null(div_rug)) { div_guides <- guides( color = guide_legend(order = 1), linetype = guide_legend( order = 2, title = NULL, keywidth = rel(1/2), override.aes = list(size = rel(1/2))) ) } } } facet_call <- NULL if (n_param == 1) { facet_call <- ylab(levels(data$parameter)) } else { facet_args$facets <- vars(.data$parameter) facet_args$scales <- facet_args$scales %||% "free" facet_call <- do.call("facet_wrap", facet_args) } ggplot(data, mapping) + bayesplot_theme_get() + layer_warmup + layer_draws + coord_window + scale_alpha + scale_color + div_rug + div_guides + facet_call + scale_x_continuous(breaks = pretty) + legend_move(ifelse(n_chain > 1, "right", "none")) + xaxis_title(FALSE) + yaxis_title(on = n_param == 1) } chain_colors <- function(n) { all_clrs <- unlist(color_scheme_get()) clrs <- switch( as.character(n), "1" = get_color("m"), "2" = get_color(c("l", "d")), "3" = get_color(c("l", "m", "d")), "4" = all_clrs[-c(2, 4)], "5" = all_clrs[-3], "6" = all_clrs, rep_len(all_clrs, n) ) unname(rev(clrs)) } #' Add divergences to trace plot using geom_rug #' #' @noRd #' @param np User's `np` argument, if specified. #' @param np_style User's `np_style` argument, if specified. #' @param n_iter Number of iterations in the trace plot (to check against number #' of iterations provided in `np`). #' @param n_chain Number of chains in the trace plot (to check against number of #' chains provided in `np`). #' @return Object returned by `ggplot2::geom_rug()`. #' #' @importFrom dplyr summarise group_by select divergence_rug <- function(np, np_style, n_iter, n_chain) { if (is.data.frame(np)) { np <- validate_nuts_data_frame(np) stopifnot(num_iters(np) == n_iter, num_chains(np) == n_chain) iter <- sym("Iteration") val <- sym("Value") param <- sym("Parameter") divg <- sym("Divergent") div_info <- np %>% dplyr::filter(UQ(param) == "divergent__") %>% group_by(!! iter) %>% summarise( Divergent = ifelse(sum(!! val) > 0, !! iter, NA) ) %>% select(!! divg) } else { # not using a data frame is deprecated but maintain backwards # compatibility for now divergences <- np stopifnot( is_vector_or_1Darray(divergences), length(divergences) == n_iter, all(divergences %in% c(0, 1)) ) divergences <- ifelse(divergences == 1, seq_along(divergences), NA) div_info <- data.frame(Divergent = divergences) } if (all(is.na(div_info$Divergent))) { inform("No divergences to plot.") return(NULL) } geom_rug( aes(x = .data$Divergent, linetype = "Divergence"), data = div_info, na.rm = TRUE, inherit.aes = FALSE, sides = "b", color = np_style$color[["div"]], linewidth = np_style$size[["div"]], alpha = np_style$alpha[["div"]] ) } bayesplot/R/mcmc-distributions.R0000644000176200001440000003124114556270424016431 0ustar liggesusers#' Histograms and kernel density plots of MCMC draws #' #' Various types of histograms and kernel density plots of MCMC draws. See the #' **Plot Descriptions** section, below, for details. #' #' @name MCMC-distributions #' @family MCMC #' #' @template args-mcmc-x #' @template args-pars #' @template args-regex_pars #' @template args-transformations #' @template args-facet_args #' @template args-density-controls #' @param ... Currently ignored. #' @param alpha Passed to the geom to control the transparency. #' #' @template return-ggplot #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_hist()`}{ #' Histograms of posterior draws with all chains merged. #' } #' \item{`mcmc_dens()`}{ #' Kernel density plots of posterior draws with all chains merged. #' } #' \item{`mcmc_hist_by_chain()`}{ #' Histograms of posterior draws with chains separated via faceting. #' } #' \item{`mcmc_dens_overlay()`}{ #' Kernel density plots of posterior draws with chains separated but #' overlaid on a single plot. #' } #' \item{`mcmc_violin()`}{ #' The density estimate of each chain is plotted as a violin with #' horizontal lines at notable quantiles. #' } #' \item{`mcmc_dens_chains()`}{ #' Ridgeline kernel density plots of posterior draws with chains separated #' but overlaid on a single plot. In `mcmc_dens_overlay()` parameters #' appear in separate facets; in `mcmc_dens_chains()` they appear in the #' same panel and can overlap vertically. #' } #' } #' #' @examples #' set.seed(9262017) #' # some parameter draws to use for demonstration #' x <- example_mcmc_draws() #' dim(x) #' dimnames(x) #' #' ################## #' ### Histograms ### #' ################## #' #' # histograms of all parameters #' color_scheme_set("brightblue") #' mcmc_hist(x) #' #' # histograms of some parameters #' color_scheme_set("pink") #' mcmc_hist(x, pars = c("alpha", "beta[2]")) #' \donttest{ #' mcmc_hist(x, pars = "sigma", regex_pars = "beta") #' } #' # example of using 'transformations' argument to plot log(sigma), #' # and parsing facet labels (e.g. to get greek letters for parameters) #' mcmc_hist(x, transformations = list(sigma = "log"), #' facet_args = list(labeller = ggplot2::label_parsed)) + #' facet_text(size = 15) #' \donttest{ #' # instead of list(sigma = "log"), you could specify the transformation as #' # list(sigma = log) or list(sigma = function(x) log(x)), but then the #' # label for the transformed sigma is 't(sigma)' instead of 'log(sigma)' #' mcmc_hist(x, transformations = list(sigma = log)) #' #' # separate histograms by chain #' color_scheme_set("pink") #' mcmc_hist_by_chain(x, regex_pars = "beta") #' } #' #' ################# #' ### Densities ### #' ################# #' #' mcmc_dens(x, pars = c("sigma", "beta[2]"), #' facet_args = list(nrow = 2)) #' \donttest{ #' # separate and overlay chains #' color_scheme_set("mix-teal-pink") #' mcmc_dens_overlay(x, pars = c("sigma", "beta[2]"), #' facet_args = list(nrow = 2)) + #' facet_text(size = 14) #' x2 <- example_mcmc_draws(params = 6) #' mcmc_dens_chains(x2, pars = c("beta[1]", "beta[2]", "beta[3]")) #' } #' # separate chains as violin plots #' color_scheme_set("green") #' mcmc_violin(x) + panel_bg(color = "gray20", size = 2, fill = "gray30") #' NULL #' @rdname MCMC-distributions #' @export #' @template args-hist #' @template args-hist-freq #' mcmc_hist <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE, alpha = 1 ) { check_ignored_arguments(...) .mcmc_hist( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, binwidth = binwidth, bins = bins, breaks = breaks, by_chain = FALSE, freq = freq, alpha = alpha, ... ) } #' @rdname MCMC-distributions #' @export mcmc_dens <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), trim = FALSE, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL, alpha = 1 ) { check_ignored_arguments(...) .mcmc_dens( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, by_chain = FALSE, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens, alpha = alpha, ... ) } #' @rdname MCMC-distributions #' @export #' mcmc_hist_by_chain <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE, alpha = 1 ) { check_ignored_arguments(...) .mcmc_hist( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, binwidth = binwidth, bins = bins, by_chain = TRUE, freq = freq, alpha = alpha, ... ) } #' @rdname MCMC-distributions #' @export mcmc_dens_overlay <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), color_chains = TRUE, trim = FALSE, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) { check_ignored_arguments(...) .mcmc_dens( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, by_chain = TRUE, color_chains = color_chains, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens, ... ) } #' @rdname MCMC-distributions #' @template args-density-controls #' @param color_chains Option for whether to separately color chains. #' @export mcmc_dens_chains <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., color_chains = TRUE, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) { check_ignored_arguments(...) data <- mcmc_dens_chains_data( x, pars = pars, regex_pars = regex_pars, transformations = transformations, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens ) n_chains <- length(unique(data$chain)) if (n_chains == 1) STOP_need_multiple_chains() # An empty data-frame to train legend colors line_training <- dplyr::slice(data, 0) if (color_chains) { scale_color <- scale_color_manual(values = chain_colors(n_chains)) } else { scale_color <- scale_color_manual( values = rep(get_color("m"), n_chains), guide = "none") } ggplot(data) + aes( x = .data$x, y = .data$parameter, color = .data$chain, group = interaction(.data$chain, .data$parameter) ) + geom_line(data = line_training) + ggridges::geom_density_ridges( aes(height = .data$density), stat = "identity", fill = NA, show.legend = FALSE ) + labs(color = "Chain") + scale_y_discrete( limits = unique(rev(data$parameter)), expand = c(0.05, .6) ) + scale_color + bayesplot_theme_get() + yaxis_title(FALSE) + xaxis_title(FALSE) + grid_lines_y(color = "gray90") + theme(axis.text.y = element_text(hjust = 1, vjust = 0)) } #' @rdname MCMC-distributions #' @export mcmc_dens_chains_data <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL ) { check_ignored_arguments(...) x %>% prepare_mcmc_array( pars = pars, regex_pars = regex_pars, transformations = transformations ) %>% melt_mcmc() %>% compute_column_density( group_vars = c("Parameter", "Chain"), value_var = "Value", interval_width = 1, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens ) %>% mutate(Chain = factor(.data$Chain)) %>% rlang::set_names(tolower) %>% dplyr::as_tibble() } #' @rdname MCMC-distributions #' @inheritParams ppc_violin_grouped #' @export mcmc_violin <- function( x, pars = character(), regex_pars = character(), transformations = list(), ..., facet_args = list(), probs = c(0.1, 0.5, 0.9) ) { check_ignored_arguments(...) .mcmc_dens( x, pars = pars, regex_pars = regex_pars, transformations = transformations, facet_args = facet_args, geom = "violin", probs = probs, ... ) } # internal ----------------------------------------------------------------- .mcmc_hist <- function( x, pars = character(), regex_pars = character(), transformations = list(), facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, by_chain = FALSE, freq = TRUE, alpha = 1, ... ) { x <- prepare_mcmc_array(x, pars, regex_pars, transformations) if (by_chain && !has_multiple_chains(x)) { STOP_need_multiple_chains() } data <- melt_mcmc(x, value.name = "value") n_param <- num_params(data) graph <- ggplot(data, aes(x = ~ value)) + geom_histogram( set_hist_aes(freq), fill = get_color("mid"), color = get_color("mid_highlight"), size = .25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks, alpha = alpha ) facet_args[["scales"]] <- facet_args[["scales"]] %||% "free" if (!by_chain) { if (n_param > 1) { facet_args[["facets"]] <- vars(.data$Parameter) graph <- graph + do.call("facet_wrap", facet_args) } } else { facet_args[["rows"]] <- vars(.data$Chain) if (n_param > 1) { facet_args[["cols"]] <- vars(.data$Parameter) } graph <- graph + do.call("facet_grid", facet_args) + force_axes_in_facets() } if (n_param == 1) { graph <- graph + xlab(levels(data$Parameter)) } graph + dont_expand_y_axis(c(0.005, 0)) + bayesplot_theme_get() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(on = n_param == 1) } .mcmc_dens <- function( x, pars = character(), regex_pars = character(), transformations = list(), facet_args = list(), by_chain = FALSE, color_chains = FALSE, geom = c("density", "violin"), probs = c(0.1, 0.5, 0.9), trim = FALSE, alpha = 1, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL, ... ) { bw <- bw %||% "nrd0" adjust <- adjust %||% 1 kernel <- kernel %||% "gaussian" n_dens <- n_dens %||% 1024 x <- prepare_mcmc_array(x, pars, regex_pars, transformations) data <- melt_mcmc.mcmc_array(x) data$Chain <- factor(data$Chain) n_param <- num_params(data) geom <- match.arg(geom) violin <- geom == "violin" geom_fun <- if (!violin) "stat_density" else "geom_violin" if (by_chain || violin) { if (!has_multiple_chains(x)) { STOP_need_multiple_chains() } else { n_chains <- num_chains(data) } } geom_args <- list(size = 0.5, na.rm = TRUE, alpha = alpha) if (violin) { geom_args[["draw_quantiles"]] <- probs } else { geom_args[["trim"]] <- trim geom_args[["bw"]] <- bw geom_args[["adjust"]] <- adjust geom_args[["kernel"]] <- kernel geom_args[["n"]] <- n_dens } if (by_chain) { # aes_mapping[["color"]] <- ~ Chain # aes_mapping[["group"]] <- ~ Chain if (violin) { aes_mapping <- aes(x = .data$Chain, y = .data$Value, color = .data$Chain, group = .data$Chain) } else { aes_mapping <- aes(x = .data$Value, color = .data$Chain, group = .data$Chain) } geom_args[["geom"]] <- "line" geom_args[["position"]] <- "identity" } else { if (violin) { aes_mapping <- aes(x = .data$Chain, y = .data$Value) } else { aes_mapping <- aes(x = .data$Value) } geom_args[["fill"]] <- get_color("mid") geom_args[["color"]] <- get_color("mid_highlight") } graph <- ggplot(data, mapping = aes_mapping) + do.call(geom_fun, geom_args) if (!violin) { graph <- graph + dont_expand_x_axis() } if (by_chain) { if (color_chains) { scale_color <- scale_color_manual(values = chain_colors(n_chains)) } else { scale_color <- scale_color_manual( values = rep(get_color("m"), n_chains), guide = "none") } graph <- graph + scale_color } if (n_param == 1) { graph <- graph + labs(x = if (violin) "Chain" else levels(data$Parameter), y = if (violin) levels(data$Parameter) else NULL) } else { facet_args[["facets"]] <- vars(.data$Parameter) facet_args[["scales"]] <- facet_args[["scales"]] %||% "free" graph <- graph + do.call("facet_wrap", facet_args) } graph + dont_expand_y_axis(c(0.005, 0)) + bayesplot_theme_get() + yaxis_text(FALSE) + yaxis_ticks(FALSE) + yaxis_title(on = n_param == 1 && violin) + xaxis_title(on = n_param == 1) } bayesplot/R/bayesplot-package.R0000644000176200001440000001002414335264324016176 0ustar liggesusers#' **bayesplot**: Plotting for Bayesian Models #' #' @docType package #' @name bayesplot-package #' @aliases bayesplot #' #' @import ggplot2 stats rlang #' @importFrom dplyr %>% summarise group_by select #' #' @description #' \if{html}{ #' \figure{stanlogo.png}{options: width="50" alt="mc-stan.org"} #' } #' *Stan Development Team* #' #' The **bayesplot** package provides a variety of **ggplot2**-based #' plotting functions for use after fitting Bayesian models (typically, though #' not exclusively, via Markov chain Monte Carlo). The package is designed not #' only to provide convenient functionality for users, but also a common set of #' functions that can be easily used by developers working on a variety of #' packages for Bayesian modeling, particularly (but not necessarily) packages #' powered by RStan (the \R interface to Stan). #' Examples of packages that will soon (or already are) using **bayesplot** are #' **rstan** itself, as well as the **rstan**-dependent **rstanarm** and #' **brms** packages for applied regression modeling. #' #' @section Plotting functionality: #' \if{html}{ #' \figure{bayesplot1.png}{options: width="30\%" alt="mcmc_areas"} #' \figure{bayesplot2.png}{options: width="30\%" alt="ppc_hist"} #' \figure{bayesplot3.png}{options: width="30\%" alt="ppc_dens_overlay"} #' } #' #' The plotting functions in **bayesplot** are organized into several modules: #' * [MCMC][MCMC-overview]: Visualizations of Markov chain #' Monte Carlo (MCMC) simulations generated by *any* MCMC algorithm #' as well as diagnostics. There are also additional functions specifically #' for use with models fit using the [No-U-Turn Sampler (NUTS)][NUTS]. #' * [PPC][PPC-overview]: Graphical (posterior or prior) predictive #' checks (PPCs). #' * [PPD][PPD-overview]: Plots of (posterior or prior) predictive #' distributions without comparisons to observed data. #' #' @section Resources: #' * __Online documentation and vignettes__: Visit the __bayesplot__ website at #' #' * __Bug reports and feature requests__: If you would like to request a new #' feature or if you have noticed a bug that needs to be fixed please let us #' know at the **bayesplot** issue tracker at #' #' * __General questions and help__: #' To ask a question about **bayesplot** on the Stan Forums forum please visit #' . #' #' @template seealso-theme #' @template seealso-colors #' @seealso [ggplot2::ggsave()] for saving plots. #' #' @template reference-vis-paper #' #' @examples #' # A few quick examples (all of the functions have many examples #' # on their individual help pages) #' #' # MCMC plots #' x <- example_mcmc_draws(params = 5) #' mcmc_intervals(x, prob = 0.5) #' mcmc_intervals(x, regex_pars = "beta") #' #' color_scheme_set("purple") #' mcmc_areas(x, regex_pars = "beta", prob = 0.8) #' #' color_scheme_set("mix-blue-red") #' mcmc_trace(x, pars = c("alpha", "sigma"), #' facet_args = list(nrow = 2)) #' #' color_scheme_set("brightblue") #' mcmc_scatter(x, pars = c("beta[1]", "sigma"), #' transformations = list(sigma = "log")) #' #' #' # Graphical PPCs #' y <- example_y_data() #' yrep <- example_yrep_draws() #' ppc_dens_overlay(y, yrep[1:50, ]) #' \donttest{ #' color_scheme_set("pink") #' ppc_stat(y, yrep, stat = "median") + grid_lines() #' ppc_hist(y, yrep[1:8, ]) #' #' # Same plots but without y (using ppd_ instead of ppc_) #' bayesplot_theme_set(ggplot2::theme_gray()) #' ypred <- yrep #' ppd_dens_overlay(ypred[1:50, ]) #' ppd_stat(ypred, stat = "median") + grid_lines() #' ppd_hist(ypred[1:8, ]) #' } #' NULL # internal ---------------------------------------------------------------- # nocov start # release reminders (for devtools) release_questions <- function() { c( "Have you reduced the size of the vignettes for CRAN?", "Have you tested with '^tests/testthat/_snaps$' removed from .Rbuildignore?", "Have you put '^tests/testthat/_snaps$' back in .Rbuildignore? before submitting?" ) } # nocov end bayesplot/R/ppc-intervals.R0000644000176200001440000002200114335264324015370 0ustar liggesusers #' PPC intervals #' #' Medians and central interval estimates of `yrep` with `y` overlaid. #' See the **Plot Descriptions** section, below. #' #' @name PPC-intervals #' @family PPCs #' #' @template args-y-yrep #' @template args-group #' @template args-facet_args #' @template args-prob-prob_outer #' @param x A numeric vector to use as the x-axis #' variable. For example, `x` could be a predictor variable from a #' regression model, a time variable for time-series models, etc. If `x` #' is missing or `NULL` then the observation index is used for the x-axis. #' @param alpha,size,fatten,linewidth Arguments passed to geoms. For ribbon #' plots `alpha` is passed to [ggplot2::geom_ribbon()] to control the opacity #' of the outer ribbon and `size` is passed to [ggplot2::geom_line()] to #' control the size of the line representing the median prediction (`size=0` #' will remove the line). For interval plots `alpha`, `size`, `fatten`, and #' `linewidth` are passed to [ggplot2::geom_pointrange()] (`fatten=0` will #' remove the point estimates). #' @param ... Currently unused. #' #' @template return-ggplot-or-data #' #' @template reference-vis-paper #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_intervals(), ppc_ribbon()`}{ #' `100*prob`% central intervals for `yrep` at each `x` #' value. `ppc_intervals()` plots intervals as vertical bars with points #' indicating `yrep` medians and darker points indicating observed #' `y` values. `ppc_ribbon()` plots a ribbon of connected intervals #' with a line through the median of `yrep` and a darker line connecting #' observed `y` values. In both cases an optional `x` variable can #' also be specified for the x-axis variable. #' #' Depending on the number of observations and the variability in the #' predictions at different values of `x`, one of these plots may be easier #' to read than the other. #' } #' \item{`ppc_intervals_grouped(), ppc_ribbon_grouped()`}{ #' Same as `ppc_intervals()` and `ppc_ribbon()`, respectively, but a #' separate plot (facet) is generated for each level of a grouping variable. #' } #' } #' #' @examples #' y <- rnorm(50) #' yrep <- matrix(rnorm(5000, 0, 2), ncol = 50) #' #' color_scheme_set("brightblue") #' ppc_intervals(y, yrep) #' ppc_ribbon(y, yrep) #' ppc_ribbon(y, yrep, y_draw = "points") #' \dontrun{ #' ppc_ribbon(y, yrep, y_draw = "both") #' } #' #' ppc_intervals(y, yrep, size = 1.5, fatten = 0) # remove the yrep point estimates #' #' color_scheme_set("teal") #' year <- 1950:1999 #' ppc_intervals(y, yrep, x = year, fatten = 1) + ggplot2::xlab("Year") #' ppc_ribbon(y, yrep, x = year) + ggplot2::xlab("Year") #' #' color_scheme_set("pink") #' year <- rep(2000:2009, each = 5) #' group <- gl(5, 1, length = 50, labels = LETTERS[1:5]) #' ppc_ribbon_grouped(y, yrep, x = year, group, y_draw = "both") + #' ggplot2::scale_x_continuous(breaks = pretty) #' #' ppc_ribbon_grouped(y, yrep, x = year, group, #' facet_args = list(scales = "fixed")) + #' xaxis_text(FALSE) + #' xaxis_ticks(FALSE) + #' panel_bg(fill = "gray20") #' #' # get the data frames used to make the ggplots #' ppc_dat <- ppc_intervals_data(y, yrep, x = year, prob = 0.5) #' ppc_group_dat <- ppc_intervals_data(y, yrep, x = year, group = group, prob = 0.5) #' #' \dontrun{ #' library("rstanarm") #' fit <- stan_glmer(mpg ~ wt + (1|cyl), data = mtcars, refresh = 0) #' yrep <- posterior_predict(fit) #' #' color_scheme_set("purple") #' ppc_intervals(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, prob = 0.8) + #' panel_bg(fill="gray90", color = NA) + #' grid_lines(color = "white") #' #' ppc_ribbon(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, #' prob = 0.6, prob_outer = 0.8) #' #' ppc_ribbon_grouped(y = mtcars$mpg, yrep = yrep, x = mtcars$wt, #' group = mtcars$cyl) #' #' #' color_scheme_set("gray") #' ppc_intervals(mtcars$mpg, yrep, prob = 0.5) + #' ggplot2::scale_x_continuous( #' labels = rownames(mtcars), #' breaks = 1:nrow(mtcars) #' ) + #' xaxis_text(angle = -70, vjust = 1, hjust = 0) + #' xaxis_title(FALSE) #' #' } #' #' NULL #' @rdname PPC-intervals #' @export ppc_intervals <- function(y, yrep, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_intervals_data( y = y, yrep = yrep, x = x, group = dots$group, prob = prob, prob_outer = prob_outer ) ggplot(data) + intervals_inner_aes( needs_y = TRUE, color = "yrep", fill = "yrep" ) + geom_linerange( mapping = intervals_outer_aes(color = "yrep"), alpha = alpha, size = size, linewidth = linewidth ) + geom_pointrange( shape = 21, stroke = 0.5, size = size, fatten = fatten, linewidth = linewidth ) + geom_point( mapping = aes( y = .data$y_obs, color = "y", fill = "y" ), shape = 21, stroke = 0.5, size = 1 ) + scale_color_ppc() + scale_fill_ppc() + intervals_axis_labels(has_x = !is.null(x)) + bayesplot_theme_get() } #' @rdname PPC-intervals #' @export ppc_intervals_grouped <- function(y, yrep, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 1, fatten = 2.5, linewidth = 1) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_intervals", call), parent.frame()) g + intervals_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPC-intervals #' @export #' @param y_draw For ribbon plots only, a string specifying how to draw `y`. Can #' be `"line"` (the default), `"points"`, or `"both"`. ppc_ribbon <- function(y, yrep, x = NULL, ..., prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25, y_draw = c("line", "points", "both")) { y_draw <- match.arg(y_draw) dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_intervals_data( y = y, yrep = yrep, x = x, group = dots$group, prob = prob, prob_outer = prob_outer ) g <- ggplot(data) + intervals_inner_aes(fill = "yrep", color = "yrep") + geom_ribbon( mapping = intervals_outer_aes(fill = "yrep", color = "yrep"), color = NA, linewidth = 0.2 * size, alpha = alpha ) + geom_ribbon( mapping = intervals_outer_aes(), fill = NA, color = get_color("m"), linewidth = 0.2 * size, alpha = 1 ) + geom_ribbon(linewidth = 0.5 * size) + geom_line( mapping = aes(y = .data$m), color = get_color("m"), linewidth = size ) + geom_blank(aes(fill = "y")) if (y_draw == "line" || y_draw == "both") { g <- g + geom_line( aes(y = .data$y_obs, color = "y"), linewidth = 0.5 ) } if (y_draw == "points" || y_draw == "both") { g <- g + geom_point( mapping = aes(y = .data$y_obs, color = "y", fill = "y"), shape = 21, size = 1.5 ) } g + scale_color_ppc() + scale_fill_ppc(values = c(NA, get_color("l")), na.value = NA) + intervals_axis_labels(has_x = !is.null(x)) + bayesplot_theme_get() } #' @export #' @rdname PPC-intervals ppc_ribbon_grouped <- function(y, yrep, x = NULL, group, ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, alpha = 0.33, size = 0.25, y_draw = c("line", "points", "both")) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_ribbon", call), parent.frame()) g + intervals_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPC-intervals #' @export ppc_intervals_data <- function(y, yrep, x = NULL, group = NULL, ..., prob = 0.5, prob_outer = 0.9) { check_ignored_arguments(...) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) x <- validate_x(x, y) if (!is.null(group)) { group <- validate_group(group, length(y)) } .ppd_intervals_data( predictions = yrep, y = y, x = x, group = group, prob = prob, prob_outer = prob_outer ) } #' @rdname PPC-intervals #' @export ppc_ribbon_data <- ppc_intervals_data bayesplot/R/ppc-distributions.R0000644000176200001440000005176614560773547016323 0ustar liggesusers#' PPC distributions #' #' Compare the empirical distribution of the data `y` to the distributions of #' simulated/replicated data `yrep` from the posterior predictive distribution. #' See the **Plot Descriptions** section, below, for details. #' #' @name PPC-distributions #' @family PPCs #' #' @template args-y-yrep #' @template args-group #' @template args-hist #' @template args-hist-freq #' @template args-dens #' @template args-pit-ecdf #' @param size,alpha Passed to the appropriate geom to control the appearance of #' the predictive distributions. #' @param ... Currently unused. #' #' @template details-binomial #' @template return-ggplot-or-data #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_hist(), ppc_freqpoly(), ppc_dens(), ppc_boxplot()`}{ #' A separate histogram, shaded frequency polygon, smoothed kernel density #' estimate, or box and whiskers plot is displayed for `y` and each #' dataset (row) in `yrep`. For these plots `yrep` should therefore #' contain only a small number of rows. See the **Examples** section. #' } #' \item{`ppc_freqpoly_grouped()`}{ #' A separate frequency polygon is plotted for each level of a grouping #' variable for `y` and each dataset (row) in `yrep`. For this plot #' `yrep` should therefore contain only a small number of rows. See the #' **Examples** section. #' } #' \item{`ppc_ecdf_overlay(), ppc_dens_overlay(), #' ppc_ecdf_overlay_grouped(), ppc_dens_overlay_grouped()`}{ #' Kernel density or empirical CDF estimates of each dataset (row) in #' `yrep` are overlaid, with the distribution of `y` itself on top #' (and in a darker shade). When using `ppc_ecdf_overlay()` with discrete #' data, set the `discrete` argument to `TRUE` for better results. #' For an example of `ppc_dens_overlay()` also see Gabry et al. (2019). #' } #' \item{`ppc_violin_grouped()`}{ #' The density estimate of `yrep` within each level of a grouping #' variable is plotted as a violin with horizontal lines at notable #' quantiles. `y` is overlaid on the plot either as a violin, points, or #' both, depending on the `y_draw` argument. #' } #' \item{`ppc_pit_ecdf()`, `ppc_pit_ecdf_grouped()`}{ #' The PIT-ECDF of the empirical PIT values of `y` computed with respect to #' the corresponding `yrep` values. `100 * prob`% central simultaneous #' confidence intervals are provided to asses if `y` and `yrep` originate #' from the same distribution. The PIT values can also be provided directly #' as `pit`. #' See Säilynoja et al. (2021) for more details.} #' } #' #' @template reference-vis-paper #' @template reference-uniformity-test #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' #' @examples #' color_scheme_set("brightblue") #' y <- example_y_data() #' yrep <- example_yrep_draws() #' group <- example_group_data() #' dim(yrep) #' #' ppc_dens_overlay(y, yrep[1:25, ]) #' \donttest{ #' # ppc_ecdf_overlay with continuous data (set discrete=TRUE if discrete data) #' ppc_ecdf_overlay(y, yrep[sample(nrow(yrep), 25), ]) #' #' # PIT-ECDF and PIT-ECDF difference plot of the PIT values of y compared to #' # yrep with 99% simultaneous confidence bands. #' ppc_pit_ecdf(y, yrep, prob = 0.99, plot_diff = FALSE) #' ppc_pit_ecdf(y, yrep, prob = 0.99, plot_diff = TRUE) #' } #' #' # for ppc_hist,dens,freqpoly,boxplot definitely use a subset yrep rows so #' # only a few (instead of nrow(yrep)) histograms are plotted #' ppc_hist(y, yrep[1:8, ]) #' \donttest{ #' color_scheme_set("red") #' ppc_boxplot(y, yrep[1:8, ]) #' #' # wizard hat plot #' color_scheme_set("blue") #' ppc_dens(y, yrep[200:202, ]) #' } #' #' \donttest{ #' # frequency polygons #' ppc_freqpoly(y, yrep[1:3, ], alpha = 0.1, size = 1, binwidth = 5) #' #' ppc_freqpoly_grouped(y, yrep[1:3, ], group) + yaxis_text() #' #' # if groups are different sizes then the 'freq' argument can be useful #' ppc_freqpoly_grouped(y, yrep[1:3, ], group, freq = FALSE) + yaxis_text() #' } #' #' # density and distribution overlays by group #' ppc_dens_overlay_grouped(y, yrep[1:25, ], group = group) #' #' ppc_ecdf_overlay_grouped(y, yrep[1:25, ], group = group) #' #' \donttest{ #' # PIT-ECDF plots of the PIT values by group #' # with 99% simultaneous confidence bands. #' ppc_pit_ecdf_grouped(y, yrep, group=group, prob=0.99) #' } #' #' \donttest{ #' # don't need to only use small number of rows for ppc_violin_grouped #' # (as it pools yrep draws within groups) #' color_scheme_set("gray") #' ppc_violin_grouped(y, yrep, group, size = 1.5) #' ppc_violin_grouped(y, yrep, group, alpha = 0) #' #' # change how y is drawn #' ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "points", y_size = 1.5) #' ppc_violin_grouped(y, yrep, group, #' alpha = 0, y_draw = "both", #' y_size = 1.5, y_alpha = 0.5, y_jitter = 0.33 #' ) #' } NULL #' @rdname PPC-distributions #' @export ppc_data <- function(y, yrep, group = NULL) { y <- validate_y(y) N <- length(y) yrep <- validate_predictions(yrep, N) if (!is.null(group)) { group <- validate_group(group, N) } # see R/ppd-distributions.R .ppd_data(predictions = yrep, y = y, group = group) } #' @rdname PPC-distributions #' @export #' @template args-density-controls ppc_dens_overlay <- function(y, yrep, ..., size = 0.25, alpha = 0.7, trim = FALSE, bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024) { check_ignored_arguments(...) data <- ppc_data(y, yrep) ggplot(data, mapping = aes(x = .data$value)) + overlay_ppd_densities( mapping = aes(group = .data$rep_id, color = "yrep"), data = function(x) dplyr::filter(x, !.data$is_y), linewidth = size, alpha = alpha, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n = n_dens ) + overlay_ppd_densities( mapping = aes(color = "y"), data = function(x) dplyr::filter(x, .data$is_y), lineend = "round", linewidth = 1, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n = n_dens ) + scale_color_ppc() + bayesplot_theme_get() + dont_expand_axes() + yaxis_title(FALSE) + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) } #' @rdname PPC-distributions #' @export #' @template args-density-controls ppc_dens_overlay_grouped <- function(y, yrep, group, ..., size = 0.25, alpha = 0.7, trim = FALSE, bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024) { check_ignored_arguments(...) p_overlay <- ppc_dens_overlay( y = y, yrep = yrep, ..., size = size, alpha = alpha, trim = trim, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens ) # Use + list(data) trick to replace the data in the plot. The layer-specific # data in the y and yrep layers should be safe because they are # specified using a function on the main plot data. data <- ppc_data(y, yrep, group = group) p_overlay <- p_overlay + list(data) p_overlay + facet_wrap("group") + force_axes_in_facets() } #' @export #' @rdname PPC-distributions #' @param discrete For `ppc_ecdf_overlay()`, should the data be treated as #' discrete? The default is `FALSE`, in which case `geom="line"` is #' passed to [ggplot2::stat_ecdf()]. If `discrete` is set to #' `TRUE` then `geom="step"` is used. #' @param pad A logical scalar passed to [ggplot2::stat_ecdf()]. #' ppc_ecdf_overlay <- function(y, yrep, ..., discrete = FALSE, pad = TRUE, size = 0.25, alpha = 0.7) { check_ignored_arguments(...) data <- ppc_data(y, yrep) ggplot(data) + aes(x = .data$value) + hline_at( 0.5, linewidth = 0.1, linetype = 2, color = get_color("dh") ) + hline_at( c(0, 1), linewidth = 0.2, linetype = 2, color = get_color("dh") ) + stat_ecdf( data = function(x) dplyr::filter(x, !.data$is_y), mapping = aes(group = .data$rep_id, color = "yrep"), geom = if (discrete) "step" else "line", linewidth = size, alpha = alpha, pad = pad ) + stat_ecdf( data = function(x) dplyr::filter(x, .data$is_y), mapping = aes(color = "y"), geom = if (discrete) "step" else "line", linewidth = 1, pad = pad ) + scale_color_ppc() + scale_y_continuous(breaks = c(0, 0.5, 1)) + bayesplot_theme_get() + yaxis_title(FALSE) + xaxis_title(FALSE) } #' @export #' @rdname PPC-distributions ppc_ecdf_overlay_grouped <- function(y, yrep, group, ..., discrete = FALSE, pad = TRUE, size = 0.25, alpha = 0.7) { check_ignored_arguments(...) p_overlay <- ppc_ecdf_overlay( y = y, yrep = yrep, ..., discrete = discrete, pad = pad, size = size, alpha = alpha ) # Use + list(data) trick to replace the data in the plot data <- ppc_data(y, yrep, group = group) p_overlay <- p_overlay + list(data) p_overlay + facet_wrap("group") + force_axes_in_facets() } #' @rdname PPC-distributions #' @export ppc_dens <- function(y, yrep, ..., trim = FALSE, size = 0.5, alpha = 1) { check_ignored_arguments(...) data <- ppc_data(y, yrep) ggplot(data, mapping = aes( x = .data$value, fill = .data$is_y_label, color = .data$is_y_label )) + geom_density( linewidth = size, alpha = alpha, trim = trim ) + scale_fill_ppc() + scale_color_ppc() + bayesplot_theme_get() + facet_wrap_parsed("rep_label") + force_axes_in_facets() + dont_expand_y_axis() + space_legend_keys() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) + facet_text(FALSE) + facet_bg(FALSE) } #' @rdname PPC-distributions #' @export ppc_hist <- function(y, yrep, ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { check_ignored_arguments(...) data <- ppc_data(y, yrep) ggplot(data, mapping = set_hist_aes( freq = freq, fill = !!quote(is_y_label), color = !!quote(is_y_label) )) + geom_histogram( linewidth = 0.25, binwidth = binwidth, bins = bins, breaks = breaks ) + scale_fill_ppc() + scale_color_ppc() + facet_wrap_parsed("rep_label") + force_axes_in_facets() + dont_expand_y_axis() + bayesplot_theme_get() + space_legend_keys() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) + facet_text(FALSE) + facet_bg(FALSE) } #' @rdname PPC-distributions #' @export ppc_freqpoly <- function(y, yrep, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_data(y, yrep, group = dots$group) ggplot(data, mapping = set_hist_aes( freq = freq, fill = !!quote(is_y_label), color = !!quote(is_y_label) )) + geom_area( stat = "bin", binwidth = binwidth, bins = bins, linewidth = size, alpha = alpha ) + scale_fill_ppc() + scale_color_ppc() + facet_wrap_parsed("rep_label") + bayesplot_theme_get() + force_axes_in_facets() + dont_expand_y_axis() + space_legend_keys() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) + facet_text(FALSE) } #' @rdname PPC-distributions #' @export ppc_freqpoly_grouped <- function(y, yrep, group, ..., binwidth = NULL, bins = NULL, freq = TRUE, size = 0.5, alpha = 1) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_freqpoly", call), parent.frame()) g + facet_grid( rep_label ~ group, scales = "free", labeller = label_parsed ) + force_axes_in_facets() + facet_text() + theme(strip.text.y = element_blank()) } #' @rdname PPC-distributions #' @export #' @param notch For the box plot, a logical scalar passed to #' [ggplot2::geom_boxplot()]. Note: unlike `geom_boxplot()`, the default is #' `notch=TRUE`. #' ppc_boxplot <- function(y, yrep, ..., notch = TRUE, size = 0.5, alpha = 1) { check_ignored_arguments(...) data <- ppc_data(y, yrep) ggplot(data, mapping = aes( x = .data$rep_label, y = .data$value, fill = .data$is_y_label, color = .data$is_y_label )) + geom_boxplot( notch = notch, linewidth = size, alpha = alpha, outlier.alpha = 2 / 3, outlier.size = 1 ) + scale_x_discrete(labels = function(x) parse(text = x)) + scale_fill_ppc() + scale_color_ppc() + bayesplot_theme_get() + yaxis_title(FALSE) + xaxis_ticks(FALSE) + xaxis_text(FALSE) + xaxis_title(FALSE) } #' @rdname PPC-distributions #' @export #' @param probs A numeric vector passed to [ggplot2::geom_violin()]'s #' `draw_quantiles` argument to specify at which quantiles to draw #' horizontal lines. Set to `NULL` to remove the lines. #' @param y_draw For `ppc_violin_grouped()`, a string specifying how to draw #' `y`: `"violin"` (default), `"points"` (jittered points), or `"both"`. #' @param y_jitter,y_size,y_alpha For `ppc_violin_grouped()`, if `y_draw` is #' `"points"` or `"both"` then `y_size`, `y_alpha`, and `y_jitter` are passed #' to to the `size`, `alpha`, and `width` arguments of [ggplot2::geom_jitter()] #' to control the appearance of `y` points. The default of `y_jitter=NULL` #' will let **ggplot2** determine the amount of jitter. #' ppc_violin_grouped <- function(y, yrep, group, ..., probs = c(0.1, 0.5, 0.9), size = 1, alpha = 1, y_draw = c("violin", "points", "both"), y_size = 1, y_alpha = 1, y_jitter = 0.1) { check_ignored_arguments(...) y_draw <- match.arg(y_draw) y_violin <- y_draw %in% c("violin", "both") y_points <- y_draw %in% c("points", "both") args_violin_yrep <- list( data = function(x) dplyr::filter(x, !.data$is_y), aes(fill = "yrep", color = "yrep"), draw_quantiles = probs, alpha = alpha, linewidth = size ) args_violin_y <- list( data = function(x) dplyr::filter(x, .data$is_y), aes(fill = "y", color = "y"), show.legend = FALSE, alpha = 0 ) args_jitter_y <- list( data = function(x) dplyr::filter(x, .data$is_y), aes(fill = "y", color = "y"), shape = 21, alpha = y_alpha, size = y_size, width = y_jitter, height = 0, show.legend = FALSE ) violin_y_func <- if (y_violin) geom_violin else geom_ignore jitter_y_func <- if (y_points) geom_jitter else geom_ignore layer_violin_yrep <- do.call(geom_violin, args_violin_yrep) layer_violin_y <- do.call(violin_y_func, args_violin_y) layer_jitter_y <- do.call(jitter_y_func, args_jitter_y) data <- ppc_data(y, yrep, group) ggplot(data, mapping = aes(x = .data$group, y = .data$value)) + layer_violin_yrep + layer_violin_y + layer_jitter_y + scale_fill_ppc(values = c(NA, get_color("l"))) + scale_color_ppc() + yaxis_title(FALSE) + xaxis_title(FALSE) + bayesplot_theme_get() } #' @export #' @param pit An optional vector of probability integral transformed values for #' which the ECDF is to be drawn. If NULL, PIT values are computed to `y` with #' respect to the corresponding values in `yrep`. #' @rdname PPC-distributions #' ppc_pit_ecdf <- function(y, yrep, ..., pit = NULL, K = NULL, prob = .99, plot_diff = FALSE, interpolate_adj = NULL) { check_ignored_arguments(..., ok_args = c("K", "pit", "prob", "plot_diff", "interpolate_adj") ) if (is.null(pit)) { pit <- ppc_data(y, yrep) %>% group_by(.data$y_id) %>% dplyr::group_map( ~ mean(.x$value[.x$is_y] > .x$value[!.x$is_y]) + runif(1, max = mean(.x$value[.x$is_y] == .x$value[!.x$is_y])) ) %>% unlist() if (is.null(K)) { K <- min(nrow(yrep) + 1, 1000) } } else { inform("'pit' specified so ignoring 'y', and 'yrep' if specified.") pit <- validate_pit(pit) if (is.null(K)) { K <- length(pit) } } N <- length(pit) gamma <- adjust_gamma( N = N, K = K, prob = prob, interpolate_adj = interpolate_adj ) lims <- ecdf_intervals(gamma = gamma, N = N, K = K) ggplot() + aes( x = seq(0,1,length.out = K), y = ecdf(pit)(seq(0, 1, length.out = K)) - (plot_diff == TRUE) * seq(0, 1, length.out = K), color = "y" ) + geom_step(show.legend = FALSE) + geom_step(aes( y = lims$upper[-1] / N - (plot_diff == TRUE) * seq(0, 1, length.out = K), color = "yrep" ), linetype = 2, show.legend = FALSE) + geom_step(aes( y = lims$lower[-1] / N - (plot_diff == TRUE) * seq(0, 1, length.out = K), color = "yrep" ), linetype = 2, show.legend = FALSE) + labs(y = ifelse(plot_diff,"ECDF - difference","ECDF"), x = "PIT") + yaxis_ticks(FALSE) + scale_color_ppc() + bayesplot_theme_get() } #' @export #' @rdname PPC-distributions #' ppc_pit_ecdf_grouped <- function(y, yrep, group, ..., K = NULL, pit = NULL, prob = .99, plot_diff = FALSE, interpolate_adj = NULL) { check_ignored_arguments(..., ok_args = c("K", "pit", "prob", "plot_diff", "interpolate_adj") ) if (is.null(pit)) { pit <- ppc_data(y, yrep, group) %>% group_by(.data$y_id) %>% dplyr::group_map( ~ mean(.x$value[.x$is_y] > .x$value[!.x$is_y]) + runif(1, max = mean(.x$value[.x$is_y] == .x$value[!.x$is_y])) ) %>% unlist() if (is.null(K)) { K <- min(nrow(yrep) + 1, 1000) } } else { inform("'pit' specified so ignoring 'y' and 'yrep' if specified.") pit <- validate_pit(pit) } N <- length(pit) gammas <- lapply(unique(group), function(g) { N_g <- sum(group == g) adjust_gamma( N = N_g, K = ifelse(is.null(K), N_g, K), prob = prob, interpolate_adj = interpolate_adj ) }) names(gammas) <- unique(group) data <- data.frame(pit = pit, group = group) %>% group_by(group) %>% dplyr::group_map( ~ data.frame( ecdf_value = ecdf(.x$pit)(seq(0, 1, length.out = ifelse(is.null(K), nrow(.x), K))), group = .y[1], lims_upper = ecdf_intervals( gamma = gammas[[unlist(.y[1])]], N = nrow(.x), K = ifelse(is.null(K), nrow(.x), K) )$upper[-1] / nrow(.x), lims_lower = ecdf_intervals( gamma = gammas[[unlist(.y[1])]], N = nrow(.x), K = ifelse(is.null(K), nrow(.x), K) )$lower[-1] / nrow(.x), x = seq(0, 1, length.out = ifelse(is.null(K), nrow(.x), K)) ) ) %>% dplyr::bind_rows() ggplot(data) + aes( x = .data$x, y = .data$ecdf_value - (plot_diff == TRUE) * .data$x, group = .data$group, color = "y" ) + geom_step(show.legend = FALSE) + geom_step(aes( y = .data$lims_upper - (plot_diff == TRUE) * .data$x, color = "yrep" ), linetype = 2, show.legend = FALSE) + geom_step(aes( y = .data$lims_lower - (plot_diff == TRUE) * .data$x, color = "yrep" ), linetype = 2, show.legend = FALSE) + labs(y = ifelse(plot_diff,"ECDF - difference","ECDF"), x = "PIT") + yaxis_ticks(FALSE) + bayesplot_theme_get() + facet_wrap("group") + scale_color_ppc() + force_axes_in_facets() } bayesplot/R/ppd-test-statistics.R0000644000176200001440000002000514556270424016536 0ustar liggesusers#' PPD test statistics #' #' The distribution of a (test) statistic `T(ypred)`, or a pair of (test) #' statistics, over the simulations from the posterior or prior predictive #' distribution. Each of these functions makes the same plot as the #' corresponding [`ppc_`][PPC-test-statistics] function but without comparing to #' any observed data `y`. The **Plot Descriptions** section at #' [PPC-test-statistics] has details on the individual plots. #' #' @name PPD-test-statistics #' @aliases PPD-statistics #' @family PPDs #' #' @template args-ypred #' @inheritParams PPC-test-statistics #' #' @template details-binomial #' @template return-ggplot-or-data #' #' @template reference-vis-paper #' @examples #' yrep <- example_yrep_draws() #' ppd_stat(yrep) #' ppd_stat(yrep, stat = "sd") + legend_none() #' #' # use your own function for the 'stat' argument #' color_scheme_set("brightblue") #' q25 <- function(y) quantile(y, 0.25) #' ppd_stat(yrep, stat = "q25") # legend includes function name NULL #' @rdname PPD-test-statistics #' @export ppd_stat <- function(ypred, stat = "mean", ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { stopifnot(length(stat) == 1) dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppd_stat_data( ypred = ypred, group = dots$group, stat = match.fun(stat) ) ggplot(data, mapping = set_hist_aes( freq, color = "ypred", fill = "ypred" )) + geom_histogram( linewidth = 0.25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks ) + scale_color_ppd(guide = "none") + scale_fill_ppd(labels = Typred_label(), guide = guide_legend( title = stat_legend_title(stat, deparse(substitute(stat))) )) + bayesplot_theme_get() + dont_expand_y_axis() + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + yaxis_title(FALSE) } #' @rdname PPD-test-statistics #' @export ppd_stat_grouped <- function(ypred, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppd_stat", call), parent.frame()) g + stat_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPD-test-statistics #' @export ppd_stat_freqpoly <- function(ypred, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE) { stopifnot(length(stat) == 1) dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppd_stat_data( ypred = ypred, group = dots$group, stat = match.fun(stat) ) ggplot(data, mapping = set_hist_aes(freq)) + geom_freqpoly( aes(color = "ypred"), linewidth = 0.5, na.rm = TRUE, binwidth = binwidth, bins = bins ) + scale_color_ppd( name = stat_legend_title(stat, deparse(substitute(stat))), labels = Typred_label() ) + dont_expand_y_axis(c(0.005, 0)) + bayesplot_theme_get() + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + yaxis_title(FALSE) } #' @rdname PPD-test-statistics #' @export ppd_stat_freqpoly_grouped <- function(ypred, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppd_stat_freqpoly", call), parent.frame()) g + stat_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPD-test-statistics #' @export ppd_stat_2d <- function(ypred, stat = c("mean", "sd"), ..., size = 2.5, alpha = 0.7) { check_ignored_arguments(...) if (length(stat) != 2) { abort("For ppd_stat_2d the 'stat' argument must have length 2.") } if (is.character(stat)) { lgnd_title <- bquote(italic(T) == (list(.(stat[1]), .(stat[2])))) stat_labs <- stat } else { lgnd_title <- expression(italic(T) == (list(italic(T)[1], italic(T)[2]))) stat_labs <- expression(italic(T)[1], italic(T)[2]) } data <- ppd_stat_data( ypred = ypred, group = NULL, stat = c(match.fun(stat[[1]]), match.fun(stat[[2]])) ) ggplot(data) + geom_point( mapping = aes( x = .data$value, y = .data$value2, fill = "ypred", color = "ypred" ), shape = 21, size = size, alpha = alpha ) + scale_fill_ppd(lgnd_title, labels = Typred_label()) + scale_color_ppd(lgnd_title, labels = Typred_label()) + labs(x = stat_labs[1], y = stat_labs[2]) + bayesplot_theme_get() } #' @rdname PPD-test-statistics #' @export ppd_stat_data <- function(ypred, group = NULL, stat) { if (!(length(stat) %in% 1:2)) { abort("'stat' must have length 1 or 2.") } ypred <- validate_predictions(ypred) if (!is.null(group)) { group <- validate_group(group, ncol(ypred)) } if (length(stat) == 1) { stat <- match.fun(stat) } else { stat <- list(match.fun(stat[[1]]), match.fun(stat[[2]])) } .ppd_stat_data( predictions = ypred, y = NULL, group = group, stat = stat ) } # internal ---------------------------------------------------------------- #' Back end for both `ppd_stat_data()` and `ppc_stat_data()`. #' #' @noRd #' @param predictions,y,group Already validated `y`, `yrep` or `ypred`, and #' `group` objects. #' @param stat A function already validated and returned by `match.fun()`, or a #' list of two such functions. #' @return A data frame with columns `group` (if not `NULL`), `variable`, #' `value`, and `value2` (if `stat` contains two functions). #' #' @examples #' y <- example_y_data() #' yrep <- example_yrep_draws() #' group <- example_group_data() #' ppd_stat_data(yrep, group, stat = "median") #' ppc_stat_data(y, yrep, group, stat = "median") #' #' @importFrom dplyr group_by ungroup summarise rename .ppd_stat_data <- function(predictions, y = NULL, group = NULL, stat) { stopifnot(length(stat) %in% c(1,2)) if (length(stat) == 1) { stopifnot(is.function(stat)) # sanity check, should already be validated stat1 <- stat stat2 <- NULL } else { # two stats stopifnot(is.function(stat[[1]]), is.function(stat[[2]])) stat1 <- stat[[1]] stat2 <- stat[[2]] } has_group <- !is.null(group) has_y <- !is.null(y) if (!has_group) { group <- 1 } if (!has_y) { y <- 1 } d <- data.frame( y = y, group = factor(group), ypred = t(predictions) ) colnames(d) <- gsub(".", "_", colnames(d), fixed = TRUE) molten_d <- reshape2::melt(d, id.vars = "group") molten_d <- group_by(molten_d, .data$group, .data$variable) data <- molten_d %>% summarise( value1 = stat1(.data$value), value2 = if (!is.null(stat2)) stat2(.data$value) else NA ) %>% rename(value = "value1") %>% ungroup() if (is.null(stat2)) { data$value2 <- NULL } if (!has_group) { data$group <- NULL } if (!has_y) { data <- dplyr::filter(data, .data$variable != "y") data$variable <- droplevels(data$variable) } else { levels(data$variable) <- gsub("ypred", "yrep", levels(data$variable)) } data } # Create the facet layer for grouped stat plots stat_group_facets <- function(facet_args, scales_default = "free") { facet_args[["facets"]] <- "group" facet_args[["scales"]] <- facet_args[["scales"]] %||% scales_default do.call("facet_wrap", facet_args) } Typred_label <- function() expression(italic(T)(italic(y)[pred])) bayesplot/R/mcmc-recover.R0000644000176200001440000002524214556270424015200 0ustar liggesusers#' Compare MCMC estimates to "true" parameter values #' #' Plots comparing MCMC estimates to "true" parameter values. Before fitting a #' model to real data it is useful to simulate data according to the model using #' known (fixed) parameter values and to check that these "true" parameter #' values are (approximately) recovered by fitting the model to the simulated #' data. See the **Plot Descriptions** section, below, for details on the #' available plots. #' #' @name MCMC-recover #' @family MCMC #' #' @template args-mcmc-x #' @template args-facet_args #' @param true A numeric vector of "true" values of the parameters in `x`. #' There should be one value in `true` for each parameter included in #' `x` and the order of the parameters in `true` should be the same #' as the order of the parameters in `x`. #' @param batch Optionally, a vector-like object (numeric, character, integer, #' factor) used to split the parameters into batches. If `batch` is #' specified, it must have the same length as `true` and be in the same #' order as `true`. Parameters in the same batch will be grouped together #' in the same facet in the plot (see the **Examples** section, below). #' The default is to group all parameters together into a single batch. #' Changing the default is most useful when parameters are on very different #' scales, in which case `batch` can be used to group them into batches #' within which it makes sense to use the same y-axis. #' @param ... Currently unused. #' @param prob The probability mass to include in the inner interval. The #' default is `0.5` (50% interval). #' @param prob_outer The probability mass to include in the outer interval. The #' default is `0.9` (90% interval). #' @param point_est The point estimate to show. Either `"median"` (the #' default), `"mean"`, or `"none"`. #' @param size,alpha Passed to [ggplot2::geom_point()] to control the #' appearance of plotted points. #' #' @template return-ggplot #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_recover_intervals()`}{ #' Central intervals and point estimates computed from MCMC draws, with #' "true" values plotted using a different shape. #' } #' \item{`mcmc_recover_scatter()`}{ #' Scatterplot of posterior means (or medians) against "true" values. #' } #' \item{`mcmc_recover_hist()`}{ #' Histograms of the draws for each parameter with the "true" value overlaid #' as a vertical line. #' } #' } #' #' @examples #' \dontrun{ #' library(rstanarm) #' alpha <- 1; beta <- rnorm(10, 0, 3); sigma <- 2 #' X <- matrix(rnorm(1000), 100, 10) #' y <- rnorm(100, mean = c(alpha + X %*% beta), sd = sigma) #' fit <- stan_glm(y ~ ., data = data.frame(y, X), refresh = 0) #' draws <- as.matrix(fit) #' print(colnames(draws)) #' true <- c(alpha, beta, sigma) #' #' mcmc_recover_intervals(draws, true) #' #' # put the coefficients on X into the same batch #' mcmc_recover_intervals(draws, true, batch = c(1, rep(2, 10), 1)) #' # equivalent #' mcmc_recover_intervals(draws, true, batch = grepl("X", colnames(draws))) #' # same but facets stacked vertically #' mcmc_recover_intervals(draws, true, #' batch = grepl("X", colnames(draws)), #' facet_args = list(ncol = 1), #' size = 3) #' #' # each parameter in its own facet #' mcmc_recover_intervals(draws, true, batch = 1:ncol(draws)) #' # same but in a different order #' mcmc_recover_intervals(draws, true, batch = c(1, 3, 4, 2, 5:12)) #' # present as bias by centering with true values #' mcmc_recover_intervals(sweep(draws, 2, true), rep(0, ncol(draws))) + hline_0() #' #' #' # scatterplot of posterior means vs true values #' mcmc_recover_scatter(draws, true, point_est = "mean") #' #' #' # histograms of parameter draws with true value added as vertical line #' color_scheme_set("brightblue") #' mcmc_recover_hist(draws[, 1:4], true[1:4]) #' } #' NULL #' @rdname MCMC-recover #' @export mcmc_recover_intervals <- function(x, true, batch = rep(1, length(true)), ..., facet_args = list(), prob = 0.5, prob_outer = 0.9, point_est = c("median", "mean", "none"), size = 4, alpha = 1) { check_ignored_arguments(...) x <- merge_chains(prepare_mcmc_array(x)) stopifnot( is.numeric(true), ncol(x) == length(true), length(batch) == length(true), prob_outer >= prob, prob > 0, prob_outer <= 1 ) all_separate <- length(unique(batch)) == length(true) point_est <- match.arg(point_est) if (point_est == "none") { point_est <- NULL } alpha1 <- (1 - prob) / 2 alpha2 <- (1 - prob_outer) / 2 probs <- sort(c(alpha1, 1 - alpha1, alpha2, 1 - alpha2)) intervals <- t(apply(x, 2, quantile, probs = probs)) colnames(intervals) <- c("ll", "l", "u", "uu") plot_data <- data.frame( Parameter = rownames(intervals), True = true, Point = apply(x, 2, point_est %||% function(x) NA), intervals ) if (!all_separate) { plot_data$Batch <- factor(batch, levels = unique(batch)) } else { plot_data$Batch <- factor(rownames(intervals), levels = rownames(intervals)[as.integer(as.factor(batch))]) } facet_args[["facets"]] <- "Batch" facet_args[["strip.position"]] <- facet_args[["strip.position"]] %||% "top" facet_args[["scales"]] <- facet_args[["scales"]] %||% "free" plot_caption <- paste0("Showing ", round(prob * 100, 1), "% and ", round(prob_outer * 100, 1), "% intervals") graph <- ggplot(plot_data, aes(x = .data$Parameter, xend = .data$Parameter)) + geom_segment( aes(y = .data$ll, yend = .data$uu, color = "Estimated"), lineend = "round", show.legend = FALSE ) + geom_segment( aes(y = .data$l, yend = .data$u, color = "Estimated"), linewidth = 2, lineend = "round", show.legend = FALSE ) + bayesplot_theme_get() if (!is.null(point_est)) { graph <- graph + geom_point( aes(y = .data$Point, shape = "Estimated", color = "Estimated", fill = "Estimated"), size = size ) } graph <- graph + geom_point( aes(y = .data$True, shape = "True", color = "True", fill = "True"), size = size, alpha = alpha ) + scale_color_manual( name = "", values = c(Estimated = get_color("d"), True = get_color("dh")), guide = if (is.null(point_est)) "none" else "legend" ) + scale_fill_manual( name = "", values = c(Estimated = get_color("d"), True = get_color("l")) ) + scale_shape_manual( name = "", values = c(Estimated = 21, True = 24) ) + do.call("facet_wrap", facet_args) + labs(y = "Value", x = "Parameter", subtitle = plot_caption) + theme(plot.caption = element_text(hjust = 0)) + xaxis_title(FALSE) + yaxis_title(FALSE) if (all_separate) { return( graph + theme(axis.line.x = element_blank()) + xaxis_ticks(FALSE) + xaxis_text(FALSE) ) } graph + xaxis_text(face = "bold") + facet_text(FALSE) } #' @rdname MCMC-recover #' @export mcmc_recover_scatter <- function(x, true, batch = rep(1, length(true)), ..., facet_args = list(), point_est = c("median", "mean"), size = 3, alpha = 1) { check_ignored_arguments(...) x <- merge_chains(prepare_mcmc_array(x)) stopifnot( is.numeric(true), ncol(x) == length(true), length(batch) == length(true) ) one_true_per_batch <- length(unique(batch)) == length(true) one_batch <- length(unique(batch)) == 1 point_est <- match.arg(point_est) plot_data <- data.frame( Parameter = colnames(x), Point = apply(x, 2, point_est), True = true ) if (!one_true_per_batch) { plot_data$Batch <- factor(batch, levels = unique(batch)) } else { plot_data$Batch <- factor(colnames(x), levels = colnames(x)[as.integer(as.factor(batch))]) } facet_args[["facets"]] <- "Batch" facet_args[["strip.position"]] <- facet_args[["strip.position"]] %||% "top" facet_args[["scales"]] <- facet_args[["scales"]] %||% "free" # To ensure that the x and y scales have the same range, find the min and max # value on each coordinate. plot them invisibly with geom_blank() later on. corners <- plot_data %>% group_by(.data$Batch) %>% summarise( min = min(pmin(.data$Point, .data$True)), max = max(pmax(.data$Point, .data$True)) ) graph <- ggplot(plot_data, aes(x = .data$True, y = .data$Point)) + geom_abline( slope = 1, intercept = 0, linetype = 2, color = "black" ) + geom_point( shape = 21, color = get_color("mh"), fill = get_color("m"), size = size, alpha = alpha ) + geom_blank(aes(x = min, y = min), data = corners) + geom_blank(aes(x = max, y = max), data = corners) + do.call("facet_wrap", facet_args) + labs(x = "True", y = "Estimated") + bayesplot_theme_get() if (one_batch) { graph <- graph + facet_text(FALSE) } graph } #' @rdname MCMC-recover #' @export #' @template args-hist mcmc_recover_hist <- function(x, true, ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL) { check_ignored_arguments(...) x <- merge_chains(prepare_mcmc_array(x)) stopifnot( is.numeric(true), ncol(x) == length(true) ) vline_data <- data.frame(Parameter = colnames(x), True = true) hist_data <- melt_mcmc(x)[, -1] vline_data$Parameter <- factor(vline_data$Parameter, levels = levels(hist_data$Parameter)) facet_args[["facets"]] <- "Parameter" facet_args[["scales"]] <- facet_args[["scales"]] %||% "free" ggplot() + geom_histogram( aes(x = .data$Value, fill = "Estimated"), data = hist_data, color = get_color("lh"), linewidth = 0.25, binwidth = binwidth, bins = bins, breaks = breaks ) + geom_vline( aes(xintercept = .data$True, color = "True"), data = vline_data, linewidth = 1.5 ) + do.call("facet_wrap", facet_args) + scale_fill_manual("", values = get_color("l")) + scale_color_manual("", values = get_color("dh")) + guides(color = guide_legend(), fill = guide_legend(order = 1)) + dont_expand_y_axis() + bayesplot_theme_get() + reduce_legend_spacing(0.25) + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + yaxis_title(FALSE) } bayesplot/R/ppc-scatterplots.R0000644000176200001440000001404514335264324016121 0ustar liggesusers#' PPC scatterplots #' #' Scatterplots of the observed data `y` vs. simulated/replicated data #' `yrep` from the posterior predictive distribution. See the #' **Plot Descriptions** and **Details** sections, below. #' #' @name PPC-scatterplots #' @family PPCs #' #' @template args-y-yrep #' @template args-group #' @template args-facet_args #' @param ... Currently unused. #' @param size,alpha Arguments passed to [ggplot2::geom_point()] to control the #' appearance of the points. #' @param ref_line If `TRUE` (the default) a dashed line with intercept 0 and #' slope 1 is drawn behind the scatter plot. #' #' @template details-binomial #' @template return-ggplot-or-data #' #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_scatter()`}{ #' For each dataset (row) in `yrep` a scatterplot is generated showing `y` #' against that row of `yrep`. For this plot `yrep` should only contain a #' small number of rows. #' } #' \item{`ppc_scatter_avg()`}{ #' A single scatterplot of `y` against the average values of `yrep`, i.e., #' the points `(x,y) = (mean(yrep[, n]), y[n])`, where each `yrep[, n]` is #' a vector of length equal to the number of posterior draws. Unlike #' for `ppc_scatter()`, for `ppc_scatter_avg()` `yrep` should contain many #' draws (rows). #' } #' \item{`ppc_scatter_avg_grouped()`}{ #' The same as `ppc_scatter_avg()`, but a separate plot is generated for #' each level of a grouping variable. #' } #' } #' #' @examples #' y <- example_y_data() #' yrep <- example_yrep_draws() #' p1 <- ppc_scatter_avg(y, yrep) #' p1 #' #' # don't draw line x=y #' ppc_scatter_avg(y, yrep, ref_line = FALSE) #' #' p2 <- ppc_scatter(y, yrep[20:23, ], alpha = 0.5, size = 1.5) #' p2 #' #' # give x and y axes the same limits #' lims <- ggplot2::lims(x = c(0, 160), y = c(0, 160)) #' p1 + lims #' p2 + lims #' #' # for ppc_scatter_avg_grouped the default is to allow the facets #' # to have different x and y axes #' group <- example_group_data() #' ppc_scatter_avg_grouped(y, yrep, group) #' #' # let x-axis vary but force y-axis to be the same #' ppc_scatter_avg_grouped(y, yrep, group, facet_args = list(scales = "free_x")) #' NULL #' @rdname PPC-scatterplots #' @export ppc_scatter <- function(y, yrep, ..., facet_args = list(), size = 2.5, alpha = 0.8, ref_line = TRUE) { check_ignored_arguments(...) data <- ppc_scatter_data(y, yrep) if (nrow(yrep) == 1) { facet_layer <- geom_ignore() } else { facet_args[["facets"]] <- "rep_label" facet_layer <- do.call("facet_wrap_parsed", facet_args) } ggplot(data, scatter_aes(color = "yrep", fill = "yrep")) + scatter_ref_line(ref_line) + geom_point( size = size, alpha = alpha, shape = 21, show.legend = FALSE ) + # use ppd color scale since only need one color # (and legend is off so no label modification needed) scale_color_ppd() + scale_fill_ppd() + bayesplot_theme_get() + facet_layer + labs(x = yrep_label(), y = y_label()) + force_axes_in_facets() + facet_text(FALSE) + legend_none() } #' @rdname PPC-scatterplots #' @export ppc_scatter_avg <- function(y, yrep, ..., size = 2.5, alpha = 0.8, ref_line = TRUE) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_scatter_avg_data(y, yrep, group = dots$group) if (is.null(dots$group) && nrow(yrep) == 1) { inform( "With only 1 row in 'yrep' ppc_scatter_avg is the same as ppc_scatter." ) } ggplot(data, scatter_aes(color = "yrep", fill = "yrep")) + scatter_ref_line(ref_line) + geom_point( alpha = alpha, size = size, shape = 21, show.legend = FALSE ) + # ppd instead of ppc (see comment in ppc_scatter) scale_color_ppd() + scale_fill_ppd() + labs(x = yrep_avg_label(), y = y_label()) + bayesplot_theme_get() } #' @rdname PPC-scatterplots #' @export ppc_scatter_avg_grouped <- function(y, yrep, group, ..., facet_args = list(), size = 2.5, alpha = 0.8, ref_line = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_scatter_avg", call), parent.frame()) g + scatter_avg_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPC-scatterplots #' @export ppc_scatter_data <- function(y, yrep) { y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) melt_predictions(yrep) %>% dplyr::arrange(.data$y_id) %>% tibble::add_column( y_obs = rep(y, each = nrow(yrep)), .before = "rep_id" ) } #' @rdname PPC-scatterplots #' @export ppc_scatter_avg_data <- function(y, yrep, group = NULL) { y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) if (!is.null(group)) { group <- validate_group(group, length(y)) } data <- ppc_scatter_data(y = y, yrep = t(colMeans(yrep))) data$rep_id <- NA_integer_ levels(data$rep_label) <- "mean(italic(y)[rep]))" if (!is.null(group)) { data <- tibble::add_column(data, group = group[data$y_id], .before = "y_id" ) } data } # internal ---------------------------------------------------------------- yrep_avg_label <- function() expression(paste("Average ", italic(y)[rep])) scatter_aes <- function(...) { aes(x = .data$value, y = .data$y_obs, ...) } scatter_avg_group_facets <- function(facet_args) { facet_args[["facets"]] <- "group" facet_args[["scales"]] <- facet_args[["scales"]] %||% "free" do.call("facet_wrap", facet_args) } scatter_ref_line <- function(ref_line, linetype = 2, color = get_color("dh"), ...) { if (!ref_line) { return(geom_ignore()) } abline_01(linetype = 2, color = get_color("dh"), ...) } bayesplot/R/bayesplot_grid.R0000644000176200001440000001105014127610043015602 0ustar liggesusers#' Arrange plots in a grid #' #' The `bayesplot_grid` function makes it simple to juxtapose plots using #' common \eqn{x} and/or \eqn{y} axes. #' #' @export #' @param ... One or more ggplot objects. #' @param plots A list of ggplot objects. Can be used as an alternative to #' specifying plot objects via `...`. #' @param grid_args An optional named list of arguments to pass to #' [gridExtra::arrangeGrob()] (`nrow`, `ncol`, #' `widths`, etc.). #' @param titles,subtitles Optional character vectors of plot titles and #' subtitles. If specified, `titles` and `subtitles` must must have #' length equal to the number of plots specified. #' @param xlim,ylim Optionally, numeric vectors of length 2 specifying lower and #' upper limits for the axes that will be shared across all plots. #' @param legends If any of the plots have legends should they be displayed? #' Defaults to `TRUE`. #' @param save_gg_objects If `TRUE`, the default, then the ggplot objects #' specified in `...` or via the `plots` argument are saved in a #' list in the `"bayesplots"` component of the returned object. #' Setting this to `FALSE` will make the returned object smaller but #' these individual plot objects will not be available. #' #' @return An object of class `"bayesplot_grid"` (essentially a gtable object #' from [gridExtra::arrangeGrob()]), which has a `plot` method. #' #' @examples #' y <- example_y_data() #' yrep <- example_yrep_draws() #' stats <- c("sd", "median", "max", "min") #' #' color_scheme_set("pink") #' bayesplot_grid( #' plots = lapply(stats, function(s) ppc_stat(y, yrep, stat = s)), #' titles = stats, #' legends = FALSE, #' grid_args = list(ncol = 1) #' ) #' #' \dontrun{ #' library(rstanarm) #' mtcars$log_mpg <- log(mtcars$mpg) #' fit1 <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0) #' fit2 <- stan_glm(log_mpg ~ wt, data = mtcars, refresh = 0) #' #' y <- mtcars$mpg #' yrep1 <- posterior_predict(fit1, draws = 50) #' yrep2 <- posterior_predict(fit2, fun = exp, draws = 50) #' #' color_scheme_set("blue") #' ppc1 <- ppc_dens_overlay(y, yrep1) #' ppc1 #' ppc1 + yaxis_text() #' #' color_scheme_set("red") #' ppc2 <- ppc_dens_overlay(y, yrep2) #' bayesplot_grid(ppc1, ppc2) #' #' # make sure the plots use the same limits for the axes #' bayesplot_grid(ppc1, ppc2, xlim = c(-5, 60), ylim = c(0, 0.2)) #' #' # remove the legends and add text #' bayesplot_grid(ppc1, ppc2, xlim = c(-5, 60), ylim = c(0, 0.2), #' legends = FALSE, subtitles = rep("Predicted MPG", 2)) #' } #' bayesplot_grid <- function(..., plots = list(), xlim = NULL, ylim = NULL, grid_args = list(), titles = character(), subtitles = character(), legends = TRUE, save_gg_objects = TRUE) { suggested_package("gridExtra") dots <- list(...) if (length(dots) && length(plots)) { abort("Arguments '...' and 'plots' can't both be specified.") } else if (length(plots)) { if (!is.list(plots) || !all_ggplot(plots)) abort("'plots' must be a list of ggplot objects.") } else if (length(dots)) { if (!all_ggplot(dots)) abort("All objects in '...' must be ggplot objects.") plots <- dots } else { abort("No plots specified.") } if (length(titles)) { stopifnot(is.character(titles), length(titles) == length(plots)) plots <- lapply(seq_along(plots), function(j) plots[[j]] + ggtitle(titles[j])) } if (length(subtitles)) { stopifnot(is.character(subtitles), length(subtitles) == length(plots)) plots <- lapply(seq_along(plots), function(j) plots[[j]] + labs(subtitle = subtitles[j])) } if (!legends) plots <- lapply(plots, function(p) p + legend_none()) if (!is.null(xlim)) plots <- lapply(plots, function(p) p + ggplot2::xlim(xlim)) if (!is.null(ylim)) plots <- lapply(plots, function(p) p + ggplot2::ylim(ylim)) grid_args$grobs <- plots g <- do.call(gridExtra::arrangeGrob, args = grid_args) if (save_gg_objects) { g$bayesplots <- plots } as_bayesplot_grid(g) } # internal ---------------------------------------------------------------- as_bayesplot_grid <- function(x) { structure(x, class = unique(c("bayesplot_grid", class(x)))) } is_bayesplot_grid <- function(x) { inherits(x, "bayesplot_grid") } all_ggplot <- function(x) { all(sapply(x, "inherits", what = "ggplot")) } #' @export print.bayesplot_grid <- function(x, ...) { gridExtra::grid.arrange(x, ...) } #' @export plot.bayesplot_grid <- print.bayesplot_grid bayesplot/R/ppc-censoring.R0000644000176200001440000001321714335264324015361 0ustar liggesusers#' PPC censoring #' #' @description Compare the empirical distribution of censored data `y` to the #' distributions of simulated/replicated data `yrep` from the posterior #' predictive distribution. See the **Plot Descriptions** section, below, for #' details. #' #' Although some of the other \pkg{bayesplot} plots can be used with censored #' data, `ppc_km_overlay()` is currently the only plotting function designed #' *specifically* for censored data. We encourage you to suggest or contribute #' additional plots at #' [github.com/stan-dev/bayesplot](https://github.com/stan-dev/bayesplot). #' #' @name PPC-censoring #' @family PPCs #' #' @template args-y-yrep #' @param size,alpha Passed to the appropriate geom to control the appearance of #' the `yrep` distributions. #' @param ... Currently only used internally. #' #' @template return-ggplot #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_km_overlay()`}{ #' Empirical CCDF estimates of each dataset (row) in `yrep` are overlaid, #' with the Kaplan-Meier estimate (Kaplan and Meier, 1958) for `y` itself on #' top (and in a darker shade). This is a PPC suitable for right-censored #' `y`. Note that the replicated data from `yrep` is assumed to be #' uncensored. #' } #' \item{`ppc_km_overlay_grouped()`}{ #' The same as `ppc_km_overlay()`, but with separate facets by `group`. #' } #' } #' #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' @template reference-km #' #' @examples #' color_scheme_set("brightblue") #' y <- example_y_data() #' # For illustrative purposes, (right-)censor values y > 110: #' status_y <- as.numeric(y <= 110) #' y <- pmin(y, 110) #' # In reality, the replicated data (yrep) would be obtained from a #' # model which takes the censoring of y properly into account. Here, #' # for illustrative purposes, we simply use example_yrep_draws(): #' yrep <- example_yrep_draws() #' dim(yrep) #' \donttest{ #' ppc_km_overlay(y, yrep[1:25, ], status_y = status_y) #' } #' # With separate facets by group: #' group <- example_group_data() #' \donttest{ #' ppc_km_overlay_grouped(y, yrep[1:25, ], group = group, status_y = status_y) #' } NULL #' @export #' @rdname PPC-censoring #' @param status_y The status indicator for the observations from `y`. This must #' be a numeric vector of the same length as `y` with values in \{0, 1\} (0 = #' right censored, 1 = event). ppc_km_overlay <- function( y, yrep, ..., status_y, size = 0.25, alpha = 0.7 ) { check_ignored_arguments(..., ok_args = "add_group") add_group <- list(...)$add_group suggested_package("survival") suggested_package("ggfortify") stopifnot(is.numeric(status_y)) stopifnot(all(status_y %in% c(0, 1))) data <- ppc_data(y, yrep, group = status_y) # Modify the status indicator: # * For the observed data ("y"), convert the status indicator back to # a numeric. # * For the replicated data ("yrep"), set the status indicator # to 1 ("event"). This way, the Kaplan-Meier estimator reduces # to "1 - ECDF" with ECDF denoting the ordinary empirical cumulative # distribution function. data <- data %>% dplyr::mutate(group = ifelse(.data$is_y, as.numeric(as.character(.data$group)), 1)) sf_form <- survival::Surv(value, group) ~ rep_label if (!is.null(add_group)) { data <- dplyr::inner_join(data, tibble::tibble(y_id = seq_along(y), add_group = add_group), by = "y_id") sf_form <- update(sf_form, . ~ . + add_group) } sf <- survival::survfit( sf_form, data = data ) names(sf$strata) <- sub("add_group=", "add_group:", names(sf$strata)) # Needed to split the strata names in ggfortify:::fortify.survfit() properly. fsf <- fortify(sf) if(any(grepl("add_group", levels(fsf$strata)))){ strata_split <- strsplit(as.character(fsf$strata), split = ", add_group:") fsf$strata <- as.factor(sapply(strata_split, "[[", 1)) fsf$group <- as.factor(sapply(strata_split, "[[", 2)) } fsf$is_y_color <- as.factor(sub("\\[rep\\] \\(.*$", "rep", sub("^italic\\(y\\)", "y", fsf$strata))) fsf$is_y_size <- ifelse(fsf$is_y_color == "yrep", size, 1) fsf$is_y_alpha <- ifelse(fsf$is_y_color == "yrep", alpha, 1) # Ensure that the observed data gets plotted last by reordering the # levels of the factor "strata" fsf$strata <- factor(fsf$strata, levels = rev(levels(fsf$strata))) ggplot(data = fsf, mapping = aes(x = .data$time, y = .data$surv, color = .data$is_y_color, group = .data$strata, size = .data$is_y_size, alpha = .data$is_y_alpha)) + geom_step() + hline_at( 0.5, linewidth = 0.1, linetype = 2, color = get_color("dh") ) + hline_at( c(0, 1), linewidth = 0.2, linetype = 2, color = get_color("dh") ) + scale_size_identity() + scale_alpha_identity() + scale_color_ppc() + scale_y_continuous(breaks = c(0, 0.5, 1)) + xlab(y_label()) + yaxis_title(FALSE) + xaxis_title(FALSE) + yaxis_ticks(FALSE) + bayesplot_theme_get() } #' @export #' @rdname PPC-censoring #' @template args-group ppc_km_overlay_grouped <- function( y, yrep, group, ..., status_y, size = 0.25, alpha = 0.7 ) { check_ignored_arguments(...) p_overlay <- ppc_km_overlay( y = y, yrep = yrep, add_group = group, ..., status_y = status_y, size = size, alpha = alpha ) p_overlay + facet_wrap("group") + force_axes_in_facets() } bayesplot/R/tidy-params.R0000644000176200001440000002132614127610043015034 0ustar liggesusers#' Tidy parameter selection #' #' Parameter selection in the style of **dplyr** and other tidyverse packages. #' #' @name tidy-params #' #' @details #' As of version `1.7.0`, **bayesplot** allows the `pars` argument for [MCMC #' plots][bayesplot::MCMC-overview] to use "tidy" variable selection (in the #' style of the **dplyr** package). The [`vars()`][dplyr::vars] function is #' re-exported from **dplyr** for this purpose. #' #' Features of tidy selection includes direct selection (`vars(alpha, sigma)`), #' everything-but selection (`vars(-alpha)`), ranged selection #' (``vars(`beta[1]`:`beta[3]`)``), support for selection functions #' (`vars(starts_with("beta"))`), and combinations of these features. See the #' **Examples** section, below. #' #' When using `pars` for tidy parameter selection, the `regex_pars` argument is #' ignored because **bayesplot** supports using [tidyselect helper #' functions][tidyselect::select_helpers] (`starts_with()`, `contains()`, #' `num_range()`, etc.) for the same purpose. **bayesplot** also exports some #' additional helper functions to help with parameter selection: #' #' * `param_range()`: like [`num_range()`][tidyselect::num_range] but used #' when parameter indexes are in brackets (e.g. `beta[2]`). #' #' * `param_glue()`: for more complicated parameter names with multiple #' indexes (including variable names) inside the brackets #' (e.g., `beta[(Intercept) age_group:3]`). #' #' These functions can be used inside of `vars()`, `dplyr::select()`, #' and similar functions, just like the #' [tidyselect helper functions][tidyselect::select_helpers]. #' #' @section Extra Advice: #' #' Parameter names in `vars()` are not quoted. When the names contain special #' characters like brackets, they should be wrapped in backticks, as in #' ``vars(`beta[1]`)``. #' #' To exclude a range of variables, wrap the sequence in parentheses and then #' negate it. For example, (``vars(-(`beta[1]`:`beta[3]`))``) would exclude #' `beta[1]`, `beta[2]`, and `beta[3]`. #' #' `vars()` is a helper function. It holds onto the names and expressions used #' to select columns. When selecting variables inside a **bayesplot** #' function, use `vars(...)`: `mcmc_hist(data, pars = vars(alpha))`. When #' using `select()` to prepare a dataframe for a **bayesplot** function, do #' not use `vars()`: `data %>% select(alpha) %>% mcmc_hist()`. #' #' Internally, tidy selection works by converting names and expressions #' into position numbers. As a result, integers will select parameters; #' `vars(1, 3)` selects the first and third ones. We do not endorse this #' approach because positions might change as variables are added and #' removed from models. To select a parameter that happens to be called `1`, #' use backticks to escape it ``vars(`1`)``. #' #' @seealso [glue::glue()] #' #' @examples #' x <- example_mcmc_draws(params = 6) #' dimnames(x) #' mcmc_hex(x, pars = vars(alpha, `beta[2]`)) #' mcmc_dens(x, pars = vars(sigma, contains("beta"))) #' mcmc_hist(x, pars = vars(-contains("beta"))) #' #' # using the param_range() helper #' mcmc_hist(x, pars = vars(param_range("beta", c(1, 3, 4)))) #' #' \donttest{ #' ############################# #' ## Examples using rstanarm ## #' ############################# #' if (requireNamespace("rstanarm", quietly = TRUE)) { #' # see ?rstanarm::example_model #' fit <- example("example_model", package = "rstanarm", local=TRUE)$value #' print(fit) #' posterior <- as.data.frame(fit) #' str(posterior) #' #' color_scheme_set("brightblue") #' mcmc_hist(posterior, pars = vars(size, contains("period"))) #' #' # same as previous but using dplyr::select() and piping #' library("dplyr") #' posterior %>% #' select(size, contains("period")) %>% #' mcmc_hist() #' #' mcmc_intervals(posterior, pars = vars(contains("herd"))) #' mcmc_intervals(posterior, pars = vars(contains("herd"), -contains("Sigma"))) #' #' bayesplot_theme_set(ggplot2::theme_dark()) #' color_scheme_set("viridisC") #' mcmc_areas_ridges(posterior, pars = vars(starts_with("b["))) #' #' bayesplot_theme_set() #' color_scheme_set("purple") #' not_789 <- vars(starts_with("b["), -matches("[7-9]")) #' mcmc_intervals(posterior, pars = not_789) #' #' # using the param_glue() helper #' just_149 <- vars(param_glue("b[(Intercept) herd:{level}]", level = c(1,4,9))) #' mcmc_intervals(posterior, pars = just_149) #' #' # same but using param_glue() with dplyr::select() #' # before passing to bayesplot #' posterior %>% #' select(param_glue("b[(Intercept) herd:{level}]", #' level = c(1, 4, 9))) %>% #' mcmc_intervals() #' } #'} NULL # re-export vars for tidy parameter selection #' @importFrom dplyr vars #' @export dplyr::vars #' @rdname tidy-params #' @export #' @param vars `NULL` or a character vector of parameter names to choose from. #' This is only needed for the atypical use case of calling the function as a #' standalone function outside of `vars()`, `select()`, etc. Typically this is #' left as `NULL` and will be set automatically for the user. #' @param prefix,range For `param_range()` only, `prefix` is a string naming a #' parameter and `range` is an integer vector providing the indices of a #' subset of elements to select. For example, using #' #' param_range("beta", c(1,2,8)) #' #' would select parameters named `beta[1]`, `beta[2]`, and `beta[8]`. #' `param_range()` is only designed for the case that the indices are integers #' surrounded by brackets. If there are no brackets use #' [num_range()][tidyselect::select_helpers]. #' param_range <- function(prefix, range, vars = NULL) { if (!is.null(vars) && !is.character(vars)) { abort("'vars' must be NULL or a character vector.") } nms <- paste0(prefix, "[", range, "]") param_matches <- match(nms, vars %||% tidyselect::peek_vars()) param_matches[!is.na(param_matches)] } #' @rdname tidy-params #' @export #' @param pattern,... For `param_glue()` only, `pattern` is a string containing #' expressions enclosed in braces and `...` should be named arguments #' providing one character vector per expression in braces in `pattern`. It is #' easiest to describe how to use these arguments with an example: #' #' param_glue("beta_{var}[{level}]", #' var = c("age", "income"), #' level = c(3,8)) #' #' would select parameters with names #' `"beta_age[3]"`, `"beta_income[3]"`, `"beta_age[8]"`, `"beta_income[8]"`. #' #' @examples #' \dontrun{ #' ################################### #' ## More examples of param_glue() ## #' ################################### #' library(dplyr) #' posterior <- tibble( #' b_Intercept = rnorm(1000), #' sd_condition__Intercept = rexp(1000), #' sigma = rexp(1000), #' `r_condition[A,Intercept]` = rnorm(1000), #' `r_condition[B,Intercept]` = rnorm(1000), #' `r_condition[C,Intercept]` = rnorm(1000), #' `r_condition[A,Slope]` = rnorm(1000), #' `r_condition[B,Slope]` = rnorm(1000) #' ) #' posterior #' #' # using one expression in braces #' posterior %>% #' select( #' param_glue("r_condition[{level},Intercept]", level = c("A", "B")) #' ) %>% #' mcmc_hist() #' #' # using multiple expressions in braces #' posterior %>% #' select( #' param_glue( #' "r_condition[{level},{type}]", #' level = c("A", "B"), #' type = c("Intercept", "Slope")) #' ) %>% #' mcmc_hist() #'} param_glue <- function(pattern, ..., vars = NULL) { if (!is.null(vars) && !is.character(vars)) { abort("'vars' must be NULL or a character vector.") } dots <- as.list(expand.grid(...)) nms <- as.character(glue::glue_data(dots, pattern)) param_matches <- match(nms, vars %||% tidyselect::peek_vars()) param_matches[!is.na(param_matches)] } # internal ---------------------------------------------------------------- #' Internal function for tidy parameter selection #' #' This function is called internally by `prepare_mcmc_array()` if the user's #' `pars` argument is a quosure. #' #' @noRd #' @param complete_pars A character vector of *all* parameter names. #' @param pars_list A list of columns generated by `vars()`. #' @return Character vector of selected parameter names. tidyselect_parameters <- function(complete_pars, pars_list) { # We use the list of helpers so that we don't have to keep track of any # changes to tidyselect. We use `env_bury()`` so that the definitions of # selection helpers are available. This pattern is taken from the example code # in `vars_select_helpers`. helpers <- tidyselect::vars_select_helpers pars_list <- lapply(pars_list, rlang::env_bury, !!! helpers) selected <- tidyselect::vars_select(.vars = complete_pars, !!! pars_list) if (!length(selected)) { abort("No parameters were found matching those names.") } unname(selected) } bayesplot/R/ppd-overview.R0000644000176200001440000000343514212422623015232 0ustar liggesusers#' Plots of posterior or prior predictive distributions #' #' @name PPD-overview #' @aliases PPD #' @family PPDs #' #' @description The **bayesplot** PPD module provides various plotting functions #' for creating graphical displays of simulated data from the posterior or #' prior predictive distribution. These plots are essentially the same as the #' corresponding [PPC] plots but without showing any observed data. Because #' these are not "checks" compared to data we use PPD (for prior/posterior #' predictive distribution) instead of PPC (for prior/posterior predictive #' check). #' #' @section PPD plotting functions: The functions for plotting prior and #' posterior predictive distributions without observed data each have the #' prefix `ppd_` and all have a required argument `ypred` (a matrix of #' predictions). The plots are organized into several categories, each with #' its own documentation: #' * [PPD-distributions]: Histograms, kernel density estimates, boxplots, and #' other plots of multiple simulated datasets (rows) in `ypred`. These are the #' same as the plots in [PPC-distributions] but without including any #' comparison to `y`. #' #' * [PPD-intervals]: Interval estimates for each predicted observations #' (columns) in `ypred`. The x-axis variable can be optionally specified by #' the user (e.g. to plot against against a predictor variable or over #' time).These are the same as the plots in [PPC-intervals] but without #' including any comparison to `y`. #' #' * [PPD-test-statistics]: The distribution of a statistic, or a pair of #' statistics, over the simulated datasets (rows) in `ypred`. These are the #' same as the plots in [PPC-test-statistics] but without including any #' comparison to `y`. #' #' @template reference-vis-paper #' NULL bayesplot/R/ppc-test-statistics.R0000644000176200001440000002634014556270424016545 0ustar liggesusers#' PPC test statistics #' #' The distribution of a (test) statistic `T(yrep)`, or a pair of (test) #' statistics, over the simulated datasets in `yrep`, compared to the #' observed value `T(y)` computed from the data `y`. See the #' **Plot Descriptions** and **Details** sections, below, as #' well as [Gabry et al. (2019)](https://github.com/jgabry/bayes-vis-paper#readme). #' #' @name PPC-test-statistics #' @aliases PPC-statistics #' @family PPCs #' #' @template args-y-yrep #' @template args-group #' @template args-facet_args #' @template args-hist #' @template args-hist-freq #' @param stat A single function or a string naming a function, except for the #' 2D plot which requires a vector of exactly two names or functions. In all #' cases the function(s) should take a vector input and return a scalar #' statistic. If specified as a string (or strings) then the legend will #' display the function name(s). If specified as a function (or functions) #' then generic naming is used in the legend. #' @param ... Currently unused. #' #' @template details-binomial #' @template return-ggplot-or-data #' #' @template reference-vis-paper #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' #' @section Plot Descriptions: #' \describe{ #' \item{`ppc_stat()`, `ppc_stat_freqpoly()`}{ #' A histogram or frequency polygon of the distribution of a statistic #' computed by applying `stat` to each dataset (row) in `yrep`. The value of #' the statistic in the observed data, `stat(y)`, is overlaid as a vertical #' line. More details and example usage of `ppc_stat()` can be found in Gabry #' et al. (2019). #' } #' \item{`ppc_stat_grouped()`,`ppc_stat_freqpoly_grouped()`}{ #' The same as `ppc_stat()` and `ppc_stat_freqpoly()`, but a separate plot is #' generated for each level of a grouping variable. More details and example #' usage of `ppc_stat_grouped()` can be found in Gabry et al. (2019). #' } #' \item{`ppc_stat_2d()`}{ #' A scatterplot showing the joint distribution of two statistics #' computed over the datasets (rows) in `yrep`. The value of the #' statistics in the observed data is overlaid as large point. #' } #' } #' #' @examples #' y <- example_y_data() #' yrep <- example_yrep_draws() #' ppc_stat(y, yrep) #' ppc_stat(y, yrep, stat = "sd") + legend_none() #' #' # use your own function for the 'stat' argument #' color_scheme_set("brightblue") #' q25 <- function(y) quantile(y, 0.25) #' ppc_stat(y, yrep, stat = "q25") # legend includes function name #' #' # can define the function in the 'stat' argument instead of #' # using its name but then the legend doesn't include the function name #' ppc_stat(y, yrep, stat = function(y) quantile(y, 0.25)) #' #' # plots by group #' color_scheme_set("teal") #' group <- example_group_data() #' ppc_stat_grouped(y, yrep, group) #' ppc_stat_grouped(y, yrep, group) + yaxis_text() #' #' # force y-axes to have same scales, allow x axis to vary #' ppc_stat_grouped(y, yrep, group, facet_args = list(scales = "free_x")) + yaxis_text() #' #' # the freqpoly plots use frequency polygons instead of histograms #' ppc_stat_freqpoly(y, yrep, stat = "median") #' ppc_stat_freqpoly_grouped(y, yrep, group, stat = "median", facet_args = list(nrow = 2)) #' #' # ppc_stat_2d allows 2 statistics and makes a scatterplot #' bayesplot_theme_set(ggplot2::theme_linedraw()) #' color_scheme_set("viridisE") #' ppc_stat_2d(y, yrep, stat = c("mean", "sd")) #' #' bayesplot_theme_set(ggplot2::theme_grey()) #' color_scheme_set("brewer-Paired") #' ppc_stat_2d(y, yrep, stat = c("median", "mad")) #' #' # reset aesthetics #' color_scheme_set() #' bayesplot_theme_set() #' NULL #' @rdname PPC-test-statistics #' @export ppc_stat <- function(y, yrep, stat = "mean", ..., binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { stopifnot(length(stat) == 1) dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_stat_data( y = y, yrep = yrep, group = dots$group, stat = match.fun(stat) ) ggplot( data = dplyr::filter(data, .data$variable != "y"), mapping = set_hist_aes(freq) ) + geom_histogram( aes(fill = "yrep"), color = get_color("lh"), linewidth = 0.25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks ) + geom_vline( data = dplyr::filter(data, .data$variable == "y"), mapping = aes(xintercept = .data$value, color = "y"), linewidth = 1.5 ) + scale_color_ppc(values = get_color("dh"), labels = Ty_label()) + scale_fill_ppc(values = get_color("l"), labels = Tyrep_label()) + guides( color = guide_legend(title = NULL), fill = guide_legend( order = 1, title = stat_legend_title(stat, deparse(substitute(stat))) ) ) + dont_expand_y_axis() + bayesplot_theme_get() + no_legend_spacing() + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + yaxis_title(FALSE) } #' @rdname PPC-test-statistics #' @export ppc_stat_grouped <- function(y, yrep, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_stat", call), parent.frame()) g + stat_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPC-test-statistics #' @export ppc_stat_freqpoly <- function(y, yrep, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE) { stopifnot(length(stat) == 1) dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_stat_data( y = y, yrep = yrep, group = dots$group, stat = match.fun(stat) ) ggplot( data = dplyr::filter(data, .data$variable != "y"), mapping = set_hist_aes(freq) ) + geom_freqpoly( aes(color = "yrep"), linewidth = 0.5, na.rm = TRUE, binwidth = binwidth, bins = bins ) + geom_vline( data = dplyr::filter(data, .data$variable == "y"), mapping = aes(xintercept = .data$value, color = "y"), show.legend = FALSE, linewidth = 1 ) + scale_color_ppc( name = stat_legend_title(stat, deparse(substitute(stat))), values = set_names(get_color(c("m", "dh")), c("yrep", "y")), labels = c(yrep = Tyrep_label(), y = Ty_label()) ) + dont_expand_y_axis(c(0.005, 0)) + bayesplot_theme_get() + xaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + yaxis_title(FALSE) } #' @rdname PPC-test-statistics #' @export ppc_stat_freqpoly_grouped <- function(y, yrep, group, stat = "mean", ..., facet_args = list(), binwidth = NULL, bins = NULL, freq = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_stat_freqpoly", call), parent.frame()) g + stat_group_facets(facet_args) + force_axes_in_facets() } #' @rdname PPC-test-statistics #' @export #' @param size,alpha For the 2D plot only, arguments passed to #' [ggplot2::geom_point()] to control the appearance of scatterplot points. ppc_stat_2d <- function(y, yrep, stat = c("mean", "sd"), ..., size = 2.5, alpha = 0.7) { check_ignored_arguments(...) if (length(stat) != 2) { abort("For ppc_stat_2d the 'stat' argument must have length 2.") } if (is.character(stat)) { lgnd_title <- bquote(italic(T) == (list(.(stat[1]), .(stat[2])))) stat_labs <- stat } else { lgnd_title <- expression(italic(T) == (list(italic(T)[1], italic(T)[2]))) stat_labs <- expression(italic(T)[1], italic(T)[2]) } data <- ppc_stat_data( y = y, yrep = yrep, group = NULL, stat = c(match.fun(stat[[1]]), match.fun(stat[[2]])) ) y_segment_data <- stat_2d_segment_data(data) y_point_data <- data.frame( x = y_segment_data[1, "x"], y = y_segment_data[2, "y"] ) ggplot(data) + geom_point( aes( x = .data$value, y = .data$value2, fill = "yrep", color = "yrep" ), shape = 21, size = size, alpha = alpha ) + geom_segment( data = y_segment_data, aes( x = .data$x, y = .data$y, xend = .data$xend, yend = .data$yend, color = "y" ), linetype = 2, linewidth = 0.4, show.legend = FALSE ) + geom_point( data = y_point_data, mapping = aes( x = .data$x, y = .data$y, fill = "y", color = "y" ), size = size * 1.5, shape = 21, stroke = 0.75 ) + scale_fill_ppc(lgnd_title, labels = c(Ty_label(), Tyrep_label())) + scale_color_ppc(lgnd_title, labels = c(Ty_label(), Tyrep_label())) + labs(x = stat_labs[1], y = stat_labs[2]) + bayesplot_theme_get() } #' @rdname PPC-test-statistics #' @export ppc_stat_data <- function(y, yrep, group = NULL, stat) { if (!(length(stat) %in% 1:2)) { abort("'stat' must have length 1 or 2.") } y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) if (!is.null(group)) { group <- validate_group(group, length(y)) } if (length(stat) == 1) { stat <- match.fun(stat) } else { stat <- list(match.fun(stat[[1]]), match.fun(stat[[2]])) } .ppd_stat_data( predictions = yrep, y = y, group = group, stat = stat ) } # internal ---------------------------------------------------------------- #' Make legend title for ppc_stat,ppc_stat_grouped,ppc_stat_freqpoly_grouped #' #' @param stat The user's `stat` argument. #' @param stat_txt `deparse(substitute())` applied to users `stat` argument. #' @return Either throws an error or returns a legend title (possibly `NULL`). #' @noRd stat_legend_title <- function(stat, stat_txt) { stopifnot(is.character(stat) || is.function(stat)) if (is.character(stat)) { lgnd_txt <- stat } else { lgnd_txt <- if (length(stat_txt) == 1 && !grepl("^function", stat_txt)) stat_txt else NA } if (is.na(lgnd_txt)) return(NULL) bquote(italic(T) == .(lgnd_txt)) } #' Make data frame for geom_segment() for ppc_stat_2d() #' @param data Data frame from `ppc_stat_data()`. #' @return Data frame with two rows and four columns (`x`,`xend`,`y`,`yend`). #' @noRd stat_2d_segment_data <- function(data) { y_data <- dplyr::filter(data, .data$variable == "y") stats <- c(y_data$value[1], y_data$value2[1]) data.frame( x = c(stats[1], -Inf), xend = c(stats[1], stats[1]), y = c(-Inf, stats[2]), yend = c(stats[2], stats[2]) ) } Ty_label <- function() expression(italic(T(italic(y)))) Tyrep_label <- function() expression(italic(T)(italic(y)[rep])) bayesplot/R/bayesplot-helpers.R0000644000176200001440000003447314556270424016266 0ustar liggesusers#' Convenience functions for adding or changing plot details #' #' Convenience functions for adding to (and changing details of) ggplot objects #' (many of the objects returned by **bayesplot** functions). See the #' **Examples** section, below. #' #' @name bayesplot-helpers #' #' @param ... For the various `vline_`, `hline_`, and `abline_` #' functions, `...` is passed to [ggplot2::geom_vline()], #' [ggplot2::geom_hline()], and [ggplot2::geom_abline()], #' respectively, to control the appearance of the line(s). #' #' For functions ending in `_bg`, `...` is passed to #' [ggplot2::element_rect()]. #' #' For functions ending in `_text` or `_title`, `...` is passed #' to [ggplot2::element_text()]. #' #' For `xaxis_ticks` and `yaxis_ticks`, `...` is passed to #' [ggplot2::element_line()]. #' #' For `overlay_function`, `...` is passed to #' [ggplot2::stat_function()]. #' #' @return #' A **ggplot2** layer or [ggplot2::theme()] object that can be #' added to existing ggplot objects, like those created by many of the #' **bayesplot** plotting functions. See the **Details** section. #' #' @details #' \subsection{Add vertical, horizontal, and diagonal lines to plots}{ #' * `vline_at()` and `hline_at()` return an object created by either #' [ggplot2::geom_vline()] or [ggplot2::geom_hline()] that can be added to a #' ggplot object to draw a vertical or horizontal line (at one or several #' values). If `fun` is missing then the lines are drawn at the values in `v`. #' If `fun` is specified then the lines are drawn at the values returned by `fun(v)`. #' #' * `vline_0()` and `hline_0()` are wrappers for `vline_at()` and `hline_at()` #' with `v = 0` and `fun` missing. #' #' * `abline_01()` is a wrapper for [ggplot2::geom_abline()] with the intercept #' set to `0` and the slope set to `1`. #' #' * `lbub()` returns a _function_ that takes a single argument `x` and returns #' the lower and upper bounds (`lb`, `ub`) of the `100*p`\% central interval #' of `x`, as well as the median (if `med=TRUE`). #' } #' #' \subsection{Control appearance of facet strips}{ #' * `facet_text()` returns ggplot2 theme objects that can be added to an #' existing plot (ggplot object) to format the text in facet strips. #' #' * `facet_bg()` can be added to a plot to change the background of the facet strips. #' } #' #' \subsection{Move legend, remove legend, or style the legend text}{ #' * `legend_move()` and `legend_none()` return a ggplot2 theme object that can #' be added to an existing plot (ggplot object) in order to change the #' position of the legend or remove it. #' #' * `legend_text()` works much like `facet_text()` but for the legend. #' } #' #' \subsection{Control appearance of \eqn{x}-axis and \eqn{y}-axis features}{ #' * `xaxis_title()` and `yaxis_title()` return a ggplot2 theme object #' that can be added to an existing plot (ggplot object) in order to toggle or #' format the titles displayed on the `x` or `y` axis. (To change #' the titles themselves use [ggplot2::labs()].) #' #' * `xaxis_text()` and `yaxis_text()` return a ggplot2 theme object #' that can be added to an existing plot (ggplot object) in order to toggle or #' format the text displayed on the `x` or `y` axis (e.g. tick #' labels). #' #' * `xaxis_ticks()` and `yaxis_ticks()` return a ggplot2 theme object #' that can be added to an existing plot (ggplot object) to change the #' appearance of the axis tick marks. #' } #' #' \subsection{Customize plot background}{ #' * `plot_bg()` returns a ggplot2 theme object that can be added to an #' existing plot (ggplot object) to format the background of the *entire* plot. #' #' * `panel_bg()` returns a ggplot2 theme object that can be added to an #' existing plot (ggplot object) to format the background of the just the #' plotting area. #' #' * `grid_lines()` returns a ggplot2 theme object that can be added to #' an existing plot (ggplot object) to add grid lines to the plot background. #' } #' #' \subsection{Superimpose a function on an existing plot}{ #' * `overlay_function()` is a simple wrapper for [ggplot2::stat_function()] but #' with the `inherit.aes` argument fixed to `FALSE`. Fixing `inherit.aes=FALSE` #' will avoid potential errors due to the [ggplot2::aes()]thetic mapping used by #' certain **bayesplot** plotting functions. #' } #' #' @seealso [theme_default()] for the default ggplot theme used by #' **bayesplot**. #' #' @examples #' color_scheme_set("gray") #' x <- example_mcmc_draws(chains = 1) #' dim(x) #' colnames(x) #' #' #' ################################### #' ### vertical & horizontal lines ### #' ################################### #' (p <- mcmc_intervals(x, regex_pars = "beta")) #' #' # vertical line at zero (with some optional styling) #' p + vline_0() #' p + vline_0(linewidth = 0.25, color = "darkgray", linetype = 2) #' #' # vertical line(s) at specified values #' v <- c(-0.5, 0, 0.5) #' p + vline_at(v, linetype = 3, linewidth = 0.25) #' #' my_lines <- vline_at(v, alpha = 0.25, linewidth = 0.75 * c(1, 2, 1), #' color = c("maroon", "skyblue", "violet")) #' p + my_lines #' #' \donttest{ #' # add vertical line(s) at computed values #' # (three ways of getting lines at column means) #' color_scheme_set("brightblue") #' p <- mcmc_intervals(x, regex_pars = "beta") #' p + vline_at(x[, 3:4], colMeans) #' p + vline_at(x[, 3:4], "colMeans", color = "darkgray", #' lty = 2, linewidth = 0.25) #' p + vline_at(x[, 3:4], function(a) apply(a, 2, mean), #' color = "orange", #' linewidth = 2, alpha = 0.1) #' } #' #' # using the lbub function to get interval lower and upper bounds (lb, ub) #' color_scheme_set("pink") #' parsed <- ggplot2::label_parsed #' p2 <- mcmc_hist(x, pars = "beta[1]", binwidth = 1/20, #' facet_args = list(labeller = parsed)) #' (p2 <- p2 + facet_text(size = 16)) #' #' b1 <- x[, "beta[1]"] #' p2 + vline_at(b1, fun = lbub(0.8), color = "gray20", #' linewidth = 2 * c(1,.5,1), alpha = 0.75) #' p2 + vline_at(b1, lbub(0.8, med = FALSE), color = "gray20", #' linewidth = 2, alpha = 0.75) #' #' #' ########################## #' ### format axis titles ### #' ########################## #' color_scheme_set("green") #' y <- example_y_data() #' yrep <- example_yrep_draws() #' (p3 <- ppc_stat(y, yrep, stat = "median", binwidth = 1/4)) #' #' # turn off the legend, turn on x-axis title #' p3 + #' legend_none() + #' xaxis_title(size = 13, family = "sans") + #' ggplot2::xlab(expression(italic(T(y)) == median(italic(y)))) #' #' #' ################################ #' ### format axis & facet text ### #' ################################ #' color_scheme_set("gray") #' p4 <- mcmc_trace(example_mcmc_draws(), pars = c("alpha", "sigma")) #' #' myfacets <- #' facet_bg(fill = "gray30", color = NA) + #' facet_text(face = "bold", color = "skyblue", size = 14) #' p4 + myfacets #' #' \donttest{ #' ########################## #' ### control tick marks ### #' ########################## #' p4 + #' myfacets + #' yaxis_text(FALSE) + #' yaxis_ticks(FALSE) + #' xaxis_ticks(linewidth = 1, color = "skyblue") #' } #' #' ############################## #' ### change plot background ### #' ############################## #' color_scheme_set("blue") #' #' # add grid lines #' ppc_stat(y, yrep) + grid_lines() #' #' # panel_bg vs plot_bg #' ppc_scatter_avg(y, yrep) + panel_bg(fill = "gray90") #' ppc_scatter_avg(y, yrep) + plot_bg(fill = "gray90") #' #' color_scheme_set("yellow") #' p5 <- ppc_scatter_avg(y, yrep, alpha = 1) #' p5 + panel_bg(fill = "gray20") + grid_lines(color = "white") #' \donttest{ #' color_scheme_set("purple") #' ppc_dens_overlay(y, yrep[1:30, ]) + #' legend_text(size = 14) + #' legend_move(c(0.75, 0.5)) + #' plot_bg(fill = "gray90") + #' panel_bg(color = "black", fill = "gray99", linewidth = 3) #' } #' #' #' ############################################### #' ### superimpose a function on existing plot ### #' ############################################### #' # compare posterior of beta[1] to Gaussian with same posterior mean #' # and sd as beta[1] #' x <- example_mcmc_draws(chains = 4) #' dim(x) #' purple_gaussian <- #' overlay_function( #' fun = dnorm, #' args = list(mean(x[,, "beta[1]"]), sd(x[,, "beta[1]"])), #' color = "purple", #' linewidth = 2 #' ) #' #' color_scheme_set("gray") #' mcmc_hist(x, pars = "beta[1]", freq = FALSE) + purple_gaussian #' \donttest{ #' mcmc_dens(x, pars = "beta[1]") + purple_gaussian #' } #' NULL # lines ------------------------------------------------------------------- #' @rdname bayesplot-helpers #' @export #' @param v Either a numeric vector specifying the value(s) at which to #' draw the vertical or horizontal line(s), or an object of any type to use as #' the first argument to `fun`. #' @param fun A function, or the name of a function, that returns a numeric #' vector. #' @param na.rm A logical scalar passed to the appropriate geom (e.g. #' [ggplot2::geom_vline()]). The default is `TRUE`. #' vline_at <- function(v, fun, ..., na.rm = TRUE) { geom_vline(xintercept = calc_v(v, fun), na.rm = na.rm, ...) } #' @rdname bayesplot-helpers #' @export hline_at <- function(v, fun, ..., na.rm = TRUE) { geom_hline(yintercept = calc_v(v, fun), na.rm = na.rm, ...) } #' @rdname bayesplot-helpers #' @export vline_0 <- function(..., na.rm = TRUE) { geom_vline(xintercept = 0, na.rm = na.rm, ...) } #' @rdname bayesplot-helpers #' @export #' hline_0 <- function(..., na.rm = TRUE) { geom_hline(yintercept = 0, na.rm = na.rm, ...) } #' @rdname bayesplot-helpers #' @export #' abline_01 <- function(..., na.rm = TRUE) { geom_abline(intercept = 0, slope = 1, na.rm = na.rm, ...) } # intervals --------------------------------------------------------------- #' @rdname bayesplot-helpers #' @export #' @param p The probability mass (in `[0,1]`) to include in the interval. #' @param med Should the median also be included in addition to the lower #' and upper bounds of the interval? #' lbub <- function(p, med = TRUE) { function(x) calc_intervals(x, p, med = med) } # internal calc_v <- function(v, fun, fun_args, ...) { if (missing(v)) { abort("'v' can't be missing.") } if (missing(fun)) { return(v) } f <- match.fun(fun) if (missing(fun_args)) { return(f(v)) } do.call(f, c(list(v), fun_args)) } calc_intervals <- function(x, p, med = TRUE, ...) { a <- (1 - p) / 2 pr <- c(a, if (med) 0.5, 1 - a) quantile(x, pr, ...) } # legend stuff ------------------------------------------------------------ #' @rdname bayesplot-helpers #' @export #' @param position The position of the legend. Either a numeric vector (of #' length 2) giving the relative coordinates (between 0 and 1) for the legend, #' or a string among `"right"`, `"left"`, `"top"`, #' `"bottom"`. Using `position = "none"` is also allowed and is #' equivalent to using `legend_none()`. #' legend_move <- function(position = "right") { if (is.numeric(position) && "legend.position.inside" %in% fn_fmls_names(theme)) { theme(legend.position = "inside", legend.position.inside = position) } else { theme(legend.position = position) } } #' @rdname bayesplot-helpers #' @export legend_none <- function() { theme(legend.position = "none") } #' @rdname bayesplot-helpers #' @export legend_text <- function(...) { theme(legend.text = element_text(...)) } # axis stuff -------------------------------------------------------------- #' @rdname bayesplot-helpers #' @export xaxis_title <- function(on = TRUE, ...) { if (!on) { return(xlab(NULL)) } theme(axis.title.x = element_text(...)) } #' @rdname bayesplot-helpers #' @export xaxis_text <- function(on = TRUE, ...) { theme(axis.text.x = if (on) element_text(...) else element_blank()) } #' @rdname bayesplot-helpers #' @export xaxis_ticks <- function(on = TRUE, ...) { theme(axis.ticks.x = if (on) element_line(...) else element_blank()) } #' @rdname bayesplot-helpers #' @export yaxis_title <- function(on = TRUE, ...) { if (!on) return(ylab(NULL)) theme(axis.title.y = element_text(...)) } #' @rdname bayesplot-helpers #' @export yaxis_text <- function(on = TRUE, ...) { theme(axis.text.y = if (on) element_text(...) else element_blank()) } #' @rdname bayesplot-helpers #' @export yaxis_ticks <- function(on = TRUE, ...) { theme(axis.ticks.y = if (on) element_line(...) else element_blank()) } # facet stuff ------------------------------------------------------------- #' @rdname bayesplot-helpers #' @export #' @param on For functions modifying ggplot [theme][ggplot2::theme] elements, #' set `on=FALSE` to set the element to [ggplot2::element_blank()]. For #' example, facet text can be removed by adding `facet_text(on=FALSE)`, or #' simply `facet_text(FALSE)` to a ggplot object. If `on=TRUE` (the default), #' then `...` can be used to customize the appearance of the theme element. #' facet_text <- function(on = TRUE, ...) { theme(strip.text = if (on) element_text(...) else element_blank()) } #' @rdname bayesplot-helpers #' @export facet_bg <- function(on = TRUE, ...) { theme(strip.background = if (on) element_rect(...) else element_blank()) } # plot background --------------------------------------------------------- #' @rdname bayesplot-helpers #' @export panel_bg <- function(on = TRUE, ...) { theme(panel.background = if (on) element_rect(...) else element_blank()) } #' @rdname bayesplot-helpers #' @export plot_bg <- function(on = TRUE, ...) { theme(plot.background = if (on) element_rect(...) else element_blank()) } #' @rdname bayesplot-helpers #' @export #' @param color,size Passed to [ggplot2::element_line()]. #' grid_lines <- function(color = "gray50", size = 0.2) { theme( panel.grid.major = element_line(color = color, linewidth = size), panel.grid.minor = element_line(color = color, linewidth = size * 0.5) ) } grid_lines_y <- function(color = "gray50", size = 0.2) { theme( panel.grid.major.y = element_line(color = color, linewidth = size), panel.grid.minor.y = element_line(color = color, linewidth = size * 0.5) ) } # overlay functions on an existing plot ----------------------------------- #' @rdname bayesplot-helpers #' @export overlay_function <- function(...) { stat_function(..., inherit.aes = FALSE) } bayesplot/R/helpers-shared.R0000644000176200001440000000272614212422623015513 0ustar liggesusers#' Check for suggested package with `requireNamespace` and throw error if necessary #' #' @noRd #' @param pkg Package name as a string. #' @param min_version Optionally, a minimum version number as a string. #' @return `TRUE`, invisibly, if no error is thrown. #' suggested_package <- function(pkg, min_version = NULL) { stopifnot(length(pkg) == 1, is.character(pkg)) if (!requireNamespace(pkg, quietly = TRUE)) { abort(paste("Please install the", pkg, "package to use this function.")) } if (!is.null(min_version)) { stopifnot(is.character(min_version)) if (utils::packageVersion(pkg) < package_version(min_version)) { abort(paste( "Version >= ", min_version, "of the", pkg, "package is required to use this function." )) } } invisible(TRUE) } # Return x if not NULL, otherwise y `%||%` <- function(x, y) if (!is.null(x)) x else y #' Warn about ignored arguments #' #' @param ... The `...` arguments from the calling function. #' @param ok_args A character vector of argument names to ignore. #' @return Nothing, but a warning may be thrown. #' @noRd check_ignored_arguments <- function(..., ok_args = character()) { dots <- list(...) nms <- names(dots) if (length(dots)) { unrecognized <- if (!length(ok_args)) nms else setdiff(nms, ok_args) if (length(unrecognized)) { warn(paste( "The following arguments were unrecognized and ignored:", paste(unrecognized, collapse = ", ") )) } } } bayesplot/R/bayesplot-colors.R0000644000176200001440000003225714335264324016120 0ustar liggesusers#' Set, get, or view **bayesplot** color schemes #' #' Set, get, or view color schemes. Choose from a preset scheme or create a #' custom scheme. See the **Available color schemes** section below for a list #' of available scheme names. The **Custom color schemes** section describes how #' to specify a custom scheme. #' #' @name bayesplot-colors #' @param scheme For `color_scheme_set()`, either a string naming one of the #' available color schemes or a character vector of _exactly six_ colors #' specifying a custom scheme. #' #' For `color_scheme_get()`, `scheme` can be missing (to get the #' current color scheme) or a string naming one of the preset schemes. #' #' For `color_scheme_view()`, `scheme` can be missing (to use the #' current color scheme) or a character vector containing a subset of the #' available scheme names. #' #' See the **Available color schemes** section below for a list of available #' scheme names. The **Custom color schemes** section describes how to specify #' a custom scheme. #' #' @return `color_scheme_set()` has the side effect of setting the color scheme #' used for plotting. It also returns ([invisibly][base::invisible]) a list of #' the hexadecimal color values used in `scheme`. #' #' `color_scheme_get()` returns a list of the hexadecimal color #' values (without changing the current scheme). If the `scheme` argument #' is not specified the returned values correspond to the current color #' scheme. If the optional argument `i` is specified then the returned #' list only contains `length(i)` elements. #' #' `color_scheme_view()` returns a ggplot object if only a single scheme is #' specified and a gtable object if multiple schemes names are specified. #' #' @section Available color schemes: Currently, the available preset color #' schemes are: #' * `"blue"`, `"brightblue"` #' * `"gray"`, `"darkgray"` #' * `"green"` #' * `"pink"` #' * `"purple"` #' * `"red"` #' * `"teal"` #' * `"yellow"` #' * [`"viridis"`](https://CRAN.R-project.org/package=viridis), `"viridisA"`, #' `"viridisB"`, `"viridisC"`, `"viridisD"`, `"viridisE"` #' * `"mix-x-y"`, replacing `x` and `y` with any two of #' the scheme names listed above (e.g. "mix-teal-pink", "mix-blue-red", #' etc.). The order of `x` and `y` matters, i.e., the color schemes #' `"mix-blue-red"` and `"mix-red-blue"` are not identical. There is no #' guarantee that every possible mixed scheme will look good with every #' possible plot. #' * `"brewer-x"`, replacing `x` with the name of a palette available from #' [RColorBrewer::brewer.pal()] (e.g., `brewer-PuBuGn`). #' #' If you have a suggestion for a new color scheme please let us know via the #' **bayesplot** [issue tracker](https://github.com/stan-dev/bayesplot/issues). #' #' @section Custom color schemes: A **bayesplot** color scheme consists of six #' colors. To specify a custom color scheme simply pass a character vector #' containing either the names of six [colors][grDevices::colors] or six #' hexadecimal color values (or a mix of names and hex values). The colors #' should be in order from lightest to darkest. See the end of the #' **Examples** section for a demonstration. #' #' @template seealso-theme #' #' @examples #' color_scheme_set("blue") #' color_scheme_view() #' #' color_scheme_get() #' color_scheme_get(i = c(3, 5)) # 3rd and 5th colors only #' #' color_scheme_get("brightblue") #' color_scheme_view("brightblue") #' #' # compare multiple schemes #' color_scheme_view(c("pink", "gray", "teal")) #' color_scheme_view(c("viridis", "viridisA", "viridisB", "viridisC")) #' #' color_scheme_set("pink") #' x <- example_mcmc_draws() #' mcmc_intervals(x) #' #' color_scheme_set("teal") #' color_scheme_view() #' mcmc_intervals(x) #' #' color_scheme_set("red") #' mcmc_areas(x, regex_pars = "beta") #' #' color_scheme_set("purple") #' color_scheme_view() #' y <- example_y_data() #' yrep <- example_yrep_draws() #' ppc_stat(y, yrep, stat = "mean") + legend_none() #' #' ############################ #' ### Mixing color schemes ### #' ############################ #' color_scheme_set("mix-teal-pink") #' ppc_stat(y, yrep, stat = "sd") + legend_none() #' mcmc_areas(x, regex_pars = "beta") #' #' ########################## #' ### ColorBrewer scheme ### #' ########################## #' color_scheme_set("brewer-Spectral") #' color_scheme_view() #' mcmc_trace(x, pars = "sigma") #' #' ########################### #' ### Custom color scheme ### #' ########################### #' orange_scheme <- c("#ffebcc", "#ffcc80", #' "#ffad33", "#e68a00", #' "#995c00", "#663d00") #' color_scheme_set(orange_scheme) #' color_scheme_view() #' mcmc_areas(x, regex_pars = "alpha") #' mcmc_dens_overlay(x) #' ppc_stat(y, yrep, stat = "var") + legend_none() #' NULL #' @rdname bayesplot-colors #' @export color_scheme_set <- function(scheme = "blue") { if (!is.character(scheme)) { abort("'scheme' should be a character vector of length 1 or 6.") } if (length(scheme) == 1) { x <- scheme_from_string(scheme) } else if (length(scheme) == 6) { x <- prepare_custom_colors(scheme) } else { abort("'scheme' should be a character vector of length 1 or 6.") } .bayesplot_aesthetics[["scheme"]] <- x invisible(x) } #' @rdname bayesplot-colors #' @export #' @param i For `color_scheme_get()`, an optional subset of the integers from `1` #' (lightest) to `6` (darkest) indicating which of the colors in the #' scheme to return. If `i` is not specified then all six colors in the #' scheme are included. #' color_scheme_get <- function(scheme = NULL, i = NULL) { if (!is.null(scheme)) { scheme <- scheme_from_string(scheme) } else { x <- .bayesplot_aesthetics$scheme scheme <- as.list(x)[scheme_level_names()] attr(scheme, "mixed") <- attr(x, "mixed") attr(scheme, "scheme_name") <- attr(x, "scheme_name") } class(scheme) <- c("bayesplot_scheme", "list") if (is.null(i)) { return(scheme) } else if (is.character(i)) { return(get_color(i)) } stopifnot( all(i %in% seq_along(scheme)), length(unique(i)) == length(i) ) scheme[i] } #' @rdname bayesplot-colors #' @export color_scheme_view <- function(scheme = NULL) { if (is.null(scheme) || length(scheme) == 1){ return(plot_scheme(scheme)) } bayesplot_grid( plots = lapply(scheme, plot_scheme), grid_args = list(ncol = length(scheme)) ) } #' @export print.bayesplot_scheme <- function(x, ...) { tab <- data.frame(unlist(x, use.names = FALSE), stringsAsFactors = FALSE) colnames(tab) <- attr(x, "scheme_name") %||% "hex_color" print(tab, ...) } #' @export plot.bayesplot_scheme <- function(x, ...) { scheme <- attr(x, "scheme_name") %||% abort("Scheme name not found.") plot_scheme(scheme) } # internal ----------------------------------------------------------------- #' Plot color scheme #' @noRd #' @param scheme A string (length 1) naming a scheme. If `NULL` the current #' scheme is used. #' @return A ggplot object. plot_scheme <- function(scheme = NULL) { if (is.null(scheme)) { x <- color_scheme_get() } else { x <- color_scheme_get(scheme) } color_data <- data.frame( name = factor(attr(x, "scheme_name")), group = factor(names(x), levels = rev(names(x))), value = rep(1, length(x)) ) ggplot(color_data, aes(x = .data$name, y = .data$value, fill = .data$group)) + geom_bar( width = .5, stat = "identity", color = "white", linewidth = 0.1 ) + scale_fill_manual("", values = unlist(x)) + theme_void() + legend_none() + xaxis_text( face = "bold", margin = margin(t = -3, b = 10, unit = "pt"), angle = 0, vjust = 1, debug = FALSE ) } # Color scheme level names scheme_level_names <- function() { c("light", "light_highlight", "mid", "mid_highlight", "dark", "dark_highlight") } #' Return a color scheme based on `scheme` argument specified as a string #' @noRd #' @param scheme A string (length 1) naming a scheme. scheme_from_string <- function(scheme) { if (identical(substr(scheme, 1, 4), "mix-")) { # user specified a mixed scheme (e.g., "mix-blue-red") to_mix <- unlist(strsplit(scheme, split = "-"))[2:3] x <- set_names(mixed_scheme(to_mix[1], to_mix[2]), scheme_level_names()) return(structure(x, mixed = TRUE, scheme_name = scheme)) } else if (identical(substr(scheme, 1, 7), "brewer-")) { # user specified a ColorBrewer scheme (e.g., "brewer-Blues") if (!requireNamespace("RColorBrewer", quietly = TRUE)) { abort("Please install the 'RColorBrewer' package to use a ColorBrewer scheme.") } clrs <- RColorBrewer::brewer.pal(n = 6, name = gsub("brewer-", "", scheme)) x <- set_names(as.list(clrs), scheme_level_names()) return(structure(x, mixed = FALSE, scheme_name = scheme)) } else { # check for scheme in master_color_list scheme <- match.arg(scheme, choices = names(master_color_list)) x <- set_names(master_color_list[[scheme]], scheme_level_names()) return(structure(x, mixed = FALSE, scheme_name = scheme)) } } # create mixed scheme from two existing schemes mixed_scheme <- function(scheme1, scheme2) { scheme1 <- color_scheme_get(scheme1) scheme2 <- color_scheme_get(scheme2) scheme <- unname(list( scheme1$light, scheme2$light_highlight, scheme2$mid, scheme1$mid_highlight, scheme1$dark, scheme2$dark_highlight )) attr(scheme, "mixed") <- TRUE scheme } #' Check if object returned by `color_scheme_get()` is a mixed scheme #' @noRd #' @param x object returned by `color_scheme_get()` #' @return T/F is_mixed_scheme <- function(x) { stopifnot(is.list(x)) isTRUE(attr(x, "mixed")) } #' Access a subset of the current scheme colors #' @noRd #' @param levels A character vector of level names in `scheme_level_names()`. #' The abbreviations `"l", "lh", "m", "mh", "d", "dh"` can also be used #' instead of the full names. #' @return A character vector of color values. #' get_color <- function(levels) { levels <- full_level_name(levels) stopifnot(all(levels %in% scheme_level_names())) color_vals <- color_scheme_get()[levels] unlist(color_vals, use.names = FALSE) } full_level_name <- function(x) { map <- c( l = "light", lh = "light_highlight", m = "mid", mh = "mid_highlight", d = "dark", dh = "dark_highlight", light = "light", light_highlight = "light_highlight", mid = "mid", mid_highlight = "mid_highlight", dark = "dark", dark_highlight = "dark_highlight" ) unname(map[x]) } # Custom color scheme if 6 colors specified prepare_custom_colors <- function(scheme) { if (length(scheme) != 6) { abort("Custom color schemes must contain exactly 6 colors.") } not_found <- character(0) for (j in seq_along(scheme)) { clr <- scheme[j] if (!is_hex_color(clr) && !clr %in% grDevices::colors()) { not_found <- c(not_found, clr) } } if (length(not_found)) { abort(paste( "Each color must specified as either a hexadecimal color value ", "(e.g. '#C79999') or the name of a color (e.g. 'blue'). ", "The following provided colors were not found:", paste(unlist(not_found), collapse = ", ") )) } x <- set_names(as.list(scheme), scheme_level_names()) attr(x, "scheme_name") <- "custom" x } is_hex_color <- function(x) { if (!identical(substr(x, 1, 1), "#")) { return(FALSE) } isTRUE(nchar(x) == 7) } # master color list ------------------------------------------------------- master_color_list <- list( blue = list("#d1e1ec", "#b3cde0", "#6497b1", "#005b96", "#03396c", "#011f4b"), brightblue = list("#cce5ff", "#99cbff", "#4ca5ff", "#198bff", "#0065cc", "#004c99"), darkgray = list("#bfbfbf", "#999999", "#737373", "#505050", "#383838", "#0d0d0d"), gray = list("#DFDFDF", "#bfbfbf", "#999999", "#737373", "#505050", "#383838"), green = list("#d9f2e6", "#9fdfbf", "#66cc99", "#40bf80", "#2d8659", "#194d33"), orange = list("#fecba2", "#feb174", "#fe8a2f", "#e47115", "#b15810", "#7f3f0c"), pink = list("#dcbccc", "#c799b0", "#b97c9b", "#a25079", "#8f275b", "#7c003e"), purple = list("#e5cce5", "#bf7fbf", "#a64ca6", "#800080", "#660066", "#400040"), red = list("#DCBCBC", "#C79999", "#B97C7C", "#A25050", "#8F2727", "#7C0000"), teal = list("#bcdcdc", "#99c7c7", "#7cb9b9", "#50a2a2", "#278f8f", "#007C7C"), yellow = list("#fbf3da", "#f8e8b5", "#f5dc90", "#dbc376", "#aa975c", "#7a6c42"), viridis = list("#FDE725FF", "#7AD151FF", "#22A884FF", "#2A788EFF", "#414487FF", "#440154FF"), viridisA = list("#FCFDBFFF", "#FE9F6DFF", "#DE4968FF", "#8C2981FF", "#3B0F70FF", "#000004FF"), viridisB = list("#FCFFA4FF", "#FCA50AFF", "#DD513AFF", "#932667FF", "#420A68FF", "#000004FF"), viridisC = list("#F0F921FF", "#FCA636FF", "#E16462FF", "#B12A90FF", "#6A00A8FF", "#0D0887FF"), # popular form of viridis is viridis option D viridisD = list("#FDE725FF", "#7AD151FF", "#22A884FF", "#2A788EFF", "#414487FF", "#440154FF"), viridisE = list("#FFEA46FF", "#CBBA69FF", "#958F78FF", "#666970FF", "#31446BFF", "#00204DFF") ) # instantiate aesthetics -------------------------------------------------- .bayesplot_aesthetics <- new.env(parent = emptyenv()) .bayesplot_aesthetics$scheme <- list() color_scheme_set() bayesplot/R/mcmc-parcoord.R0000644000176200001440000001646314556270424015351 0ustar liggesusers#' Parallel coordinates plot of MCMC draws #' #' Parallel coordinates plot of MCMC draws (one dimension per parameter). #' See the **Plot Descriptions** section below for details, #' and see [Gabry et al. (2019)](https://github.com/jgabry/bayes-vis-paper#readme) #' for more background and a real example. #' #' @name MCMC-parcoord #' @family MCMC #' #' @template args-mcmc-x #' @template args-pars #' @template args-regex_pars #' @template args-transformations #' @param ... Currently ignored. #' @param size,alpha Arguments passed on to [ggplot2::geom_line()]. #' @param np For models fit using [NUTS] (more generally, #' any [symplectic integrator](https://en.wikipedia.org/wiki/Symplectic_integrator)), #' an optional data frame providing NUTS diagnostic information. The data #' frame should be the object returned by [nuts_params()] or one with the same #' structure. #' @param np_style A call to the `parcoord_style_np()` helper function to #' specify arguments controlling the appearance of superimposed lines #' representing NUTS diagnostics (in this case divergences) if the `np` #' argument is specified. #' #' @template return-ggplot-or-data #' #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_parcoord()`}{ #' [Parallel coordinates plot](https://en.wikipedia.org/wiki/Parallel_coordinates) #' of MCMC draws. There is one dimension per parameter along the horizontal #' axis and each set of connected line segments represents a single MCMC draw #' (i.e., a vector of length equal to the number of parameters). #' #' The parallel coordinates plot is most useful if the optional HMC/NUTS #' diagnostic information is provided via the `np` argument. In that #' case divergences are highlighted in the plot. The appearance of the #' divergences can be customized using the `np_style` argument and the #' `parcoord_style_np` helper function. This version of the plot is the #' same as the parallel coordinates plot described in Gabry et al. (2019). #' #' When the plotted model parameters are on very different scales the #' `transformations` argument can be useful. For example, to standardize #' all variables before plotting you could use function `(x - mean(x))/sd(x)` #' when specifying the `transformations` argument to #' `mcmc_parcoord`. See the **Examples** section for how to do this. #' } #' } #' #' @template reference-vis-paper #' @references Hartikainen, A. (2017, Aug 23). Concentration of divergences #' (Msg 21). Message posted to The Stan Forums: #' . #' #' @examples #' color_scheme_set("pink") #' x <- example_mcmc_draws(params = 5) #' mcmc_parcoord(x) #' mcmc_parcoord(x, regex_pars = "beta") #' #' \dontrun{ #' # Example using a Stan demo model #' library(rstan) #' fit <- stan_demo("eight_schools") #' draws <- as.array(fit, pars = c("mu", "tau", "theta", "lp__")) #' np <- nuts_params(fit) #' str(np) #' levels(np$Parameter) #' #' color_scheme_set("brightblue") #' mcmc_parcoord(draws, alpha = 0.05) #' mcmc_parcoord(draws, np = np) #' #' # customize appearance of divergences #' color_scheme_set("darkgray") #' div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4) #' mcmc_parcoord(draws, size = 0.25, alpha = 0.1, #' np = np, np_style = div_style) #' #' # to use a transformation (e.g., standardizing all the variables can be helpful) #' # specify the 'transformations' argument (though partial argument name #' # matching means we can just use 'trans' or 'transform') #' mcmc_parcoord( #' draws, #' transform = function(x) {(x - mean(x)) / sd(x)}, #' size = 0.25, #' alpha = 0.1, #' np = np, #' np_style = div_style #' ) #' #' # mcmc_parcoord_data returns just the data in a conventient form for plotting #' d <- mcmc_parcoord_data(x, np = np) #' head(d) #' tail(d) #'} #' NULL #' @rdname MCMC-parcoord #' @export mcmc_parcoord <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., size = 0.2, alpha = 0.3, np = NULL, np_style = parcoord_style_np()) { check_ignored_arguments(...) stopifnot(inherits(np_style, "nuts_style")) data <- mcmc_parcoord_data( x = x, pars = pars, regex_pars = regex_pars, transformations = transformations, np = np ) divg <- sym("Divergent") draws <- dplyr::filter(data, UQ(divg) == 0) div_draws <- dplyr::filter(data, UQ(divg) == 1) has_divs <- isTRUE(nrow(div_draws) > 0) graph <- ggplot(draws, aes( x = .data$Parameter, y = .data$Value, group = factor(.data$Draw) )) + geom_line( linewidth = size, alpha = alpha, color = get_color("dh") ) + bayesplot_theme_get() if (has_divs) { graph <- graph + geom_line( data = div_draws, linewidth = np_style$size[["div"]], alpha = np_style$alpha[["div"]], color = np_style$color[["div"]] ) } graph + scale_x_discrete(expand = c(0,0), labels = levels(draws$Parameter)) + expand_limits(x = nlevels(draws$Parameter) + 0.25) + labs(x = NULL, y = NULL) } #' @rdname MCMC-parcoord #' @export #' @importFrom dplyr n left_join mutate group_by ungroup select arrange rename mcmc_parcoord_data <- function(x, pars = character(), regex_pars = character(), transformations = list(), np = NULL ) { x <- prepare_mcmc_array(x, pars, regex_pars, transformations) long_d <- melt_mcmc(x) if (num_params(long_d) < 2) { abort("'mcmc_parcoord' requires at least two parameters in 'x'.") } param <- sym("Parameter") value <- sym("Value") if (is.null(np)) { # still include 'Divergent' so returned object always has same columns long_d$Divergent <- 0 } else { # join with divergence info (both long_d and np have columns # 'Parameter' and 'Value' so need to be a little careful) divs <- np %>% validate_nuts_data_frame() %>% dplyr::filter(UQ(param) == "divergent__") %>% select(- !!param) %>% rename("Divergent" = !!value) long_d <- left_join(long_d, divs, by = c("Iteration", "Chain")) } keep_cols <- syms(c("Draw", "Parameter", "Value", "Divergent")) long_d %>% group_by(!! param) %>% mutate(Draw = 1:n()) %>% ungroup() %>% select(!!! keep_cols) } #' @rdname MCMC-parcoord #' @export #' @param div_color,div_size,div_alpha Optional arguments to the #' `parcoord_style_np()` helper function that are eventually passed to #' [ggplot2::geom_line()] if the `np` argument is also specified. They control #' the color, size, and transparency specifications for showing divergences in #' the plot. The default values are displayed in the **Usage** section above. parcoord_style_np <- function(div_color = "red", div_size = 0.2, div_alpha = 0.2) { stopifnot( is.character(div_color), is.numeric(div_size), is.numeric(div_alpha) && div_alpha >= 0 && div_alpha <= 1 ) style <- list( color = c(div = div_color), size = c(div = div_size), alpha = c(div = div_alpha) ) structure(style, class = c(class(style), "nuts_style")) } bayesplot/R/ppc-errors.R0000644000176200001440000003277114556465326014726 0ustar liggesusers#' PPC errors #' #' Various plots of predictive errors `y - yrep`. See the #' **Details** and **Plot Descriptions** sections, below. #' #' @name PPC-errors #' @family PPCs #' #' @template args-y-yrep #' @template args-group #' @template args-facet_args #' @param ... Currently unused. #' @param size,alpha For scatterplots, arguments passed to #' [ggplot2::geom_point()] to control the appearance of the points. For the #' binned error plot, arguments controlling the size of the outline and #' opacity of the shaded region indicating the 2-SE bounds. #' #' @details #' All of these functions (aside from the `*_scatter_avg` functions) #' compute and plot predictive errors for each row of the matrix `yrep`, so #' it is usually a good idea for `yrep` to contain only a small number of #' draws (rows). See **Examples**, below. #' #' For binomial and Bernoulli data the `ppc_error_binned()` function can be used #' to generate binned error plots. Bernoulli data can be input as a vector of 0s #' and 1s, whereas for binomial data `y` and `yrep` should contain "success" #' proportions (not counts). See the **Examples** section, below. #' #' @section Plot descriptions: #' \describe{ #' \item{`ppc_error_hist()`}{ #' A separate histogram is plotted for the predictive errors computed from #' `y` and each dataset (row) in `yrep`. For this plot `yrep` should have #' only a small number of rows. #' } #' \item{`ppc_error_hist_grouped()`}{ #' Like `ppc_error_hist()`, except errors are computed within levels of a #' grouping variable. The number of histograms is therefore equal to the #' product of the number of rows in `yrep` and the number of groups #' (unique values of `group`). #' } #' \item{`ppc_error_scatter()`}{ #' A separate scatterplot is displayed for `y` vs. the predictive errors #' computed from `y` and each dataset (row) in `yrep`. For this plot `yrep` #' should have only a small number of rows. #' } #' \item{`ppc_error_scatter_avg()`}{ #' A single scatterplot of `y` vs. the average of the errors computed from #' `y` and each dataset (row) in `yrep`. For each individual data point #' `y[n]` the average error is the average of the errors for `y[n]` computed #' over the the draws from the posterior predictive distribution. #' } #' \item{`ppc_error_scatter_avg_vs_x()`}{ #' Same as `ppc_error_scatter_avg()`, except the average is plotted on the #' y-axis and a predictor variable `x` is plotted on the x-axis. #' } #' \item{`ppc_error_binned()`}{ #' Intended for use with binomial data. A separate binned error plot (similar #' to `arm::binnedplot()`) is generated for each dataset (row) in `yrep`. For #' this plot `y` and `yrep` should contain proportions rather than counts, #' and `yrep` should have only a small number of rows. #' } #' } #' #' @template return-ggplot #' #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' #' @examples #' y <- example_y_data() #' yrep <- example_yrep_draws() #' ppc_error_hist(y, yrep[1:3, ]) #' #' # errors within groups #' group <- example_group_data() #' (p1 <- ppc_error_hist_grouped(y, yrep[1:3, ], group)) #' p1 + yaxis_text() # defaults to showing counts on y-axis #' \donttest{ #' table(group) # more obs in GroupB, can set freq=FALSE to show density on y-axis #' (p2 <- ppc_error_hist_grouped(y, yrep[1:3, ], group, freq = FALSE)) #' p2 + yaxis_text() #' } #' #' # scatterplots #' ppc_error_scatter(y, yrep[10:14, ]) #' ppc_error_scatter_avg(y, yrep) #' #' x <- example_x_data() #' ppc_error_scatter_avg_vs_x(y, yrep, x) #' #' \dontrun{ #' # binned error plot with binomial model from rstanarm #' library(rstanarm) #' example("example_model", package = "rstanarm") #' formula(example_model) #' #' # get observed proportion of "successes" #' y <- example_model$y # matrix of "success" and "failure" counts #' trials <- rowSums(y) #' y_prop <- y[, 1] / trials # proportions #' #' # get predicted success proportions #' yrep <- posterior_predict(example_model) #' yrep_prop <- sweep(yrep, 2, trials, "/") #' #' ppc_error_binned(y_prop, yrep_prop[1:6, ]) #' } #' NULL #' @rdname PPC-errors #' @export #' @template args-hist #' @template args-hist-freq ppc_error_hist <- function(y, yrep, ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { dots <- list(...) if (!from_grouped(dots)) { check_ignored_arguments(...) dots$group <- NULL } data <- ppc_error_data(y, yrep, group = dots$group) ggplot(data, set_hist_aes(freq)) + geom_histogram( fill = get_color("l"), color = get_color("lh"), size = 0.25, binwidth = binwidth, bins = bins, breaks = breaks ) + xlab(error_label()) + bayesplot_theme_get() + dont_expand_y_axis() + error_hist_facets( facet_args, grouped = FALSE, ignore = nrow(yrep) == 1 ) + force_axes_in_facets() + yaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + facet_text(FALSE) } #' @rdname PPC-errors #' @export ppc_error_hist_grouped <- function(y, yrep, group, ..., facet_args = list(), binwidth = NULL, bins = NULL, breaks = NULL, freq = TRUE) { check_ignored_arguments(...) call <- match.call(expand.dots = FALSE) g <- eval(ungroup_call("ppc_error_hist", call), parent.frame()) g + error_hist_facets(facet_args, grouped = TRUE) + facet_text() + theme(strip.text.y = element_blank()) } #' @rdname PPC-errors #' @export ppc_error_scatter <- function(y, yrep, ..., facet_args = list(), size = 2.5, alpha = 0.8) { check_ignored_arguments(...) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) errors <- compute_errors(y, yrep) ppc_scatter( y = y, yrep = errors, facet_args = facet_args, size = size, alpha = alpha, ref_line = FALSE ) + labs(x = error_label(), y = y_label()) } #' @rdname PPC-errors #' @export ppc_error_scatter_avg <- function(y, yrep, ..., size = 2.5, alpha = 0.8) { check_ignored_arguments(...) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) errors <- compute_errors(y, yrep) ppc_scatter_avg( y = y, yrep = errors, size = size, alpha = alpha, ref_line = FALSE ) + labs(x = error_avg_label(), y = y_label()) } #' @rdname PPC-errors #' @export ppc_error_scatter_avg_grouped <- function(y, yrep, group, ..., facet_args = list(), size = 2.5, alpha = 0.8) { check_ignored_arguments(...) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) errors <- compute_errors(y, yrep) ppc_scatter_avg_grouped( y = y, yrep = errors, group = group, size = size, alpha = alpha, facet_args = facet_args, ref_line = FALSE ) + labs(x = error_avg_label(), y = y_label()) } #' @rdname PPC-errors #' @export #' @param x A numeric vector the same length as `y` to use as the x-axis #' variable. #' ppc_error_scatter_avg_vs_x <- function(y, yrep, x, ..., size = 2.5, alpha = 0.8) { check_ignored_arguments(...) y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) x <- validate_x(x, y) errors <- compute_errors(y, yrep) ppc_scatter_avg( y = x, yrep = errors, size = size, alpha = alpha, ref_line = FALSE ) + labs(x = error_avg_label(), y = expression(italic(x))) + coord_flip() } #' @rdname PPC-errors #' @export #' @param bins For `ppc_error_binned()`, the number of bins to use (approximately). ppc_error_binned <- function(y, yrep, ..., facet_args = list(), bins = NULL, size = 1, alpha = 0.25) { check_ignored_arguments(...) data <- ppc_error_binnned_data(y, yrep, bins = bins) facet_layer <- if (nrow(yrep) == 1) { geom_ignore() } else { facet_args[["facets"]] <- "rep_id" do.call("facet_wrap", facet_args) } mixed_scheme <- is_mixed_scheme(color_scheme_get()) point_fill <- get_color(ifelse(mixed_scheme, "m", "d")) point_color <- get_color(ifelse(mixed_scheme, "mh", "dh")) ggplot(data, aes(x = .data$ey_bar)) + hline_0(linetype = 2, color = "black") + geom_ribbon( mapping = aes(ymax = .data$se2, ymin = -.data$se2), fill = get_color("l"), color = NA, alpha = alpha ) + geom_path( mapping = aes(y = .data$se2), color = get_color("l"), linewidth = size ) + geom_path( mapping = aes(y = -.data$se2), color = get_color("l"), linewidth = size ) + geom_point( mapping = aes(y = .data$err_bar), shape = 21, fill = point_fill, color = point_color ) + labs( x = "Predicted proportion", y = "Average Errors \n (with 2SE bounds)" ) + bayesplot_theme_get() + facet_layer + force_axes_in_facets() + facet_text(FALSE) } #' @rdname PPC-errors #' @export ppc_error_data <- function(y, yrep, group = NULL) { y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) if (!is.null(group)) { group <- validate_group(group, length(y)) } errors <- compute_errors(y, yrep) %>% melt_predictions() errors <- tibble::add_column(errors, y_obs = y[errors$y_id], .before = "rep_id") if (!is.null(group)) { errors <- tibble::add_column(errors, group = group[errors$y_id], .before = "y_id") } errors } # internal ---------------------------------------------------------------- #' Compute predictive errors `y` - `yrep` #' @noRd #' @param y,yrep User's `y` and `yrep` arguments. #' @return A matrix with the same dimensions as `yrep` compute_errors <- function(y, yrep) { suggested_package("rstantools") rstantools::predictive_error(object = yrep, y = y) } #' Create facet layer for PPC error plots #' #' The default is to use `scales="fixed"` (which I think makes sense for looking #' at errors, right?) if not specified in `facet_args`. #' #' @param User's `facet_args` argument. #' @param grouped If `FALSE` then does faceting by `rep_id`, if `TRUE` then both #' `rep_id` and `group`. #' @param ignore If `TRUE` then `geom_ignore()` is returned. This is intended to #' allow turning off facets if there is only one plot to make. #' @param scales_default What to use for the `scales` argument to `facet_*()` if #' not specified in `facet_args`. #' @return Object returned by `facet_wrap()` or `facet_grid()` (unless `ignore=TRUE`). #' @noRd error_hist_facets <- function(facet_args, grouped = FALSE, ignore = FALSE, scales_default = "fixed") { if (ignore) { return(geom_ignore()) } if (grouped) { facet_fun <- "facet_grid" facet_args[["rows"]] <- vars(.data$rep_id) facet_args[["cols"]] <- vars(.data$group) } else { facet_fun <- "facet_wrap" facet_args[["facets"]] <- vars(.data$rep_id) } facet_args[["scales"]] <- facet_args[["scales"]] %||% scales_default do.call(facet_fun, facet_args) } error_label <- function() { expression(italic(y) - italic(y)[rep]) } error_avg_label <- function() { expression(paste("Average ", italic(y) - italic(y)[rep])) } # Data for binned errors plots ppc_error_binnned_data <- function(y, yrep, bins = NULL) { y <- validate_y(y) yrep <- validate_predictions(yrep, length(y)) if (is.null(bins)) { bins <- n_bins(length(y)) } errors <- compute_errors(y, yrep) binned_errs <- list() for (s in 1:nrow(errors)) { binned_errs[[s]] <- bin_errors( ey = yrep[s, ], r = errors[s, ], bins = bins, rep_id = s ) } binned_errs <- dplyr::bind_rows(binned_errs) tibble::as_tibble(binned_errs) } # calculate number of bins binned_error_data() # @parmam N Number of data points, length(y) n_bins <- function(N) { if (N <= 10) { return(floor(N / 2)) } else if (N > 10 && N < 100) { return(10) } else { # N >= 100 return(floor(sqrt(N))) } } bin_errors <- function(ey, r, bins, rep_id = NULL) { N <- length(ey) break_ids <- floor(N * (1:(bins - 1)) / bins) if (any(break_ids == 0)) { bins <- 1 } if (bins == 1) { breaks <- c(-Inf, sum(range(ey)) / 2, Inf) } else { ey_sort <- sort(ey) breaks <- -Inf for (i in 1:(bins - 1)) { break_i <- break_ids[i] ey_range <- ey_sort[c(break_i, break_i + 1)] if (diff(ey_range) == 0) { if (ey_range[1] == min(ey)) { ey_range[1] <- -Inf } else { ey_range[1] <- max(ey[ey < ey_range[1]]) } } breaks <- c(breaks, sum(ey_range) / 2) } breaks <- unique(c(breaks, Inf)) } ey_binned <- as.numeric(cut(ey, breaks)) bins <- length(breaks) - 1 out <- matrix(NA, nrow = bins, ncol = 4) colnames(out) <- c("ey_bar", "err_bar", "se2", "bin") for (i in 1:bins) { mark <- which(ey_binned == i) ey_bar <- mean(ey[mark]) r_bar <- mean(r[mark]) s <- if (length(r[mark]) > 1) sd(r[mark]) else 0 out[i, ] <- c(ey_bar, r_bar, 2 * s / sqrt(length(mark)), i) } out <- as.data.frame(out) if (!is.null(rep_id)) { out$rep_id <- as.integer(rep_id) } return(out) } bayesplot/R/bayesplot-ggplot-themes.R0000644000176200001440000001405514556270424017375 0ustar liggesusers#' Default **bayesplot** plotting theme #' #' The [theme_default()] function returns the default ggplot #' [theme][ggplot2::theme] used by the **bayesplot** plotting functions. See #' [bayesplot_theme_set()] for details on setting and updating the plotting #' theme. #' #' @export #' @param base_size,base_family Base font size and family (passed to #' [ggplot2::theme_bw()]). It is possible to set `"bayesplot.base_size"` and #' `"bayesplot.base_family"` via [options()] to change the defaults, which are #' `12` and `"serif"`, respectively. #' @return A ggplot [theme][ggplot2::theme] object. #' #' @seealso [bayesplot_theme_set()] to change the ggplot theme. #' @template seealso-colors #' @template seealso-helpers #' #' @examples #' class(theme_default()) #' #' bayesplot_theme_set() # defaults to setting theme_default() #' x <- example_mcmc_draws() #' mcmc_hist(x) #' #' # change the default font size and family for bayesplots #' bayesplot_theme_set(theme_default(base_size = 8, base_family = "sans")) #' mcmc_hist(x) #' mcmc_areas(x, regex_pars = "beta") #' #' # change back #' bayesplot_theme_set() #' mcmc_areas(x, regex_pars = "beta") #' theme_default <- function(base_size = getOption("bayesplot.base_size", 12), base_family = getOption("bayesplot.base_family", "serif")) { theme_bw( base_family = base_family, base_size = base_size ) + theme( plot.background = element_blank(), panel.grid = element_blank(), panel.background = element_blank(), panel.border = element_blank(), axis.line = element_line(linewidth = 0.4), axis.ticks = element_line(linewidth = 0.3), strip.background = element_blank(), strip.text = element_text(size = rel(0.9)), strip.placement = "outside", # strip.background = element_rect(fill = "gray95", color = NA), panel.spacing = unit(1.5, "lines"), legend.position = "right", legend.background = element_blank(), legend.text = element_text(size = 13, hjust = 0), legend.key = element_blank() ) } #' Get, set, and modify the active **bayesplot** theme #' #' @description These functions are the **bayesplot** equivalent to #' **ggplot2**'s [ggplot2::theme_set()] and friends. They set, get, and update #' the active theme but only apply them to `bayesplots`. The current/active #' theme is automatically applied to every `bayesplot` you draw. #' #' Use `bayesplot_theme_get()` to get the current **bayesplot** theme and #' `bayesplot_theme_set()` to set a new theme. `bayesplot_theme_update()` and #' `bayesplot_theme_replace()` are shorthands for changing individual elements. #' #' @details `bayesplot_theme_set()` and friends only apply to `bayesplots`. #' However, [ggplot2::theme_set()] can also be used to change the #' **bayesplot** theme. Currently, setting a theme with `ggplot2::theme_set()` #' (other than the **ggplot2** default [ggplot2::theme_grey()]) will override #' the **bayesplot** theme. #' #' @export #' @param new The new theme (list of theme elements) to use. This is analogous #' to the `new` argument to [ggplot2::theme_set()]. #' @param ... A named list of theme settings. #' #' @return `bayesplot_theme_get()` returns the current theme. The other three #' functions (set, update, replace) invisibly return the *previous* theme #' so it can be saved and easily restored later. This is the same behavior as #' the **ggplot2** versions of these functions. #' #' @seealso [theme_default()] for the default **bayesplot** theme. #' @template seealso-helpers #' @template seealso-colors #' #' @examples #' library(ggplot2) #' #' # plot using the current value of bayesplot_theme_get() #' # (the default is bayesplot::theme_default()) #' x <- example_mcmc_draws() #' mcmc_hist(x) #' #' # change the bayesplot theme to theme_minimal and save the old theme #' old <- bayesplot_theme_set(theme_minimal()) #' mcmc_hist(x) #' #' # change back to the previous theme #' bayesplot_theme_set(old) #' mcmc_hist(x) #' #' # change the default font size and family for bayesplots #' bayesplot_theme_update(text = element_text(size = 16, family = "sans")) #' mcmc_hist(x) #' #' # change back to the default #' bayesplot_theme_set() # same as bayesplot_theme_set(theme_default()) #' mcmc_hist(x) #' #' # updating theme elements #' color_scheme_set("brightblue") #' bayesplot_theme_set(theme_dark()) #' mcmc_hist(x) #' #' bayesplot_theme_update(panel.background = element_rect(fill = "black")) #' mcmc_hist(x) #' #' # to get the same plot without updating the theme we could also have #' # used the bayeplot convenience function panel_bg() #' bayesplot_theme_set(theme_dark()) #' mcmc_hist(x) + panel_bg(fill = "black") #' #' # reset #' bayesplot_theme_set() #' bayesplot_theme_get <- function() { if (!identical(.bayesplot_theme_env$gg_current, ggplot2::theme_get())) { .bayesplot_theme_env$current <- ggplot2::theme_get() .bayesplot_theme_env$gg_current <- ggplot2::theme_get() thm <- .bayesplot_theme_env$gg_current } else { thm <- .bayesplot_theme_env$current } thm } #' @rdname bayesplot_theme_get #' @export bayesplot_theme_set <- function(new = theme_default()) { missing <- setdiff(names(ggplot2::theme_gray()), names(new)) if (length(missing)) { warn(paste( "New theme missing the following elements:", paste(missing, collapse = ", ") )) } old <- .bayesplot_theme_env$current .bayesplot_theme_env$current <- new .bayesplot_theme_env$gg_current <- ggplot2::theme_get() invisible(old) } #' @rdname bayesplot_theme_get #' @export bayesplot_theme_update <- function(...) { bayesplot_theme_set(bayesplot_theme_get() + ggplot2::theme(...)) } #' @rdname bayesplot_theme_get #' @export #' @importFrom ggplot2 %+replace% bayesplot_theme_replace <- function(...) { bayesplot_theme_set(bayesplot_theme_get() %+replace% ggplot2::theme(...)) } # internal ---------------------------------------------------------------- .bayesplot_theme_env <- new.env(parent = emptyenv()) .bayesplot_theme_env$current <- theme_default() .bayesplot_theme_env$gg_current <- ggplot2::theme_grey() bayesplot/R/available-module-functions.R0000644000176200001440000000710414212422623020011 0ustar liggesusers#' Get or view the names of available plotting or data functions #' #' @export #' @param pattern,fixed,invert Passed to [base::grep()]. #' @param plots_only If `TRUE` (the default) only plotting functions are #' searched for. If `FALSE` then functions that return data for plotting #' (functions ending in `_data()`) are also included. #' @return A possibly empty character vector of function names with several #' additional attributes (for use by a custom print method). If `pattern` #' is missing then the returned object contains the names of all available #' plotting functions in the [MCMC], [PPC], or [PPD] module, depending on #' which function is called. If `pattern` is specified then a subset of #' function names is returned. #' #' @examples #' available_mcmc() #' available_mcmc("nuts") #' available_mcmc("rhat|neff") #' #' available_ppc() #' available_ppc("grouped") #' available_ppc("grouped", invert = TRUE) #' #' available_ppd() #' available_ppd("grouped") #' #' # can also see which functions that return data are available #' available_ppc(plots_only = FALSE) #' #' # only show the _data functions #' available_ppc("_data", plots_only = FALSE) #' available_ppd("_data", plots_only = FALSE) #' available_mcmc("_data", plots_only = FALSE) #' available_ppc <- function(pattern = NULL, fixed = FALSE, invert = FALSE, plots_only = TRUE) { .list_module_functions( .module = "ppc", .pattern = pattern, fixed = fixed, invert = invert, plots_only = plots_only ) } #' @rdname available_ppc #' @export available_ppd <- function(pattern = NULL, fixed = FALSE, invert = FALSE, plots_only = TRUE) { .list_module_functions( .module = "ppd", .pattern = pattern, fixed = fixed, invert = invert, plots_only = plots_only ) } #' @rdname available_ppc #' @export available_mcmc <- function(pattern = NULL, fixed = FALSE, invert = FALSE, plots_only = TRUE) { .list_module_functions( .module = "mcmc", .pattern = pattern, fixed = fixed, invert = invert, plots_only = plots_only ) } #' @export print.bayesplot_function_list <- function(x, ...) { atts <- attributes(x) cat("bayesplot", toupper(atts[["module"]]), "module:\n") if (!is.null(atts[["pattern"]])) { msg <- paste0("(", ifelse(atts[["inverted"]], "excluding", "matching"), " pattern '", atts[["pattern"]], "')") cat(msg, "\n") } cat(paste0(" ", x), sep = "\n") invisible(x) } # internal ---------------------------------------------------------------- .list_module_functions <- function(.module = c("ppc", "ppd", "mcmc"), .pattern, fixed = FALSE, invert = FALSE, plots_only = TRUE) { .module <- match.arg(.module) all_funs <- grep( pattern = paste0("^", .module, "_"), x = getNamespaceExports("bayesplot"), value = TRUE ) return_funs <- sort(all_funs) if (plots_only) { # drop _data() functions return_funs <- grep( pattern = "_data()", x = return_funs, invert = TRUE, value = TRUE ) } if (!is.null(.pattern)) { return_funs <- grep( pattern = .pattern, x = return_funs, value = TRUE, fixed = fixed, invert = invert ) } structure( return_funs, class = c("bayesplot_function_list", "character"), module = .module, pattern = .pattern, inverted = invert ) } bayesplot/R/mcmc-diagnostics.R0000644000176200001440000004311314556465326016046 0ustar liggesusers#' General MCMC diagnostics #' #' Plots of Rhat statistics, ratios of effective sample size to total sample #' size, and autocorrelation of MCMC draws. See the **Plot Descriptions** #' section, below, for details. For models fit using the No-U-Turn-Sampler, see #' also [MCMC-nuts] for additional MCMC diagnostic plots. #' #' @name MCMC-diagnostics #' @family MCMC #' #' @template args-hist #' @param size Optional values to override [ggplot2::geom_point()]'s #' default size (for `mcmc_rhat()`, `mcmc_neff()`) or #' [ggplot2::geom_line()]'s default line width (for `mcmc_acf()`). #' @param ... Currently ignored. #' #' @template return-ggplot-or-data #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_rhat()`, `mcmc_rhat_hist()`}{ #' Rhat values as either points or a histogram. Values are colored using #' different shades (lighter is better). The chosen thresholds are somewhat #' arbitrary, but can be useful guidelines in practice. #' * _light_: below 1.05 (good) #' * _mid_: between 1.05 and 1.1 (ok) #' * _dark_: above 1.1 (too high) #' } #' #' \item{`mcmc_neff()`, `mcmc_neff_hist()`}{ #' Ratios of effective sample size to total sample size as either points or a #' histogram. Values are colored using different shades (lighter is better). #' The chosen thresholds are somewhat arbitrary, but can be useful guidelines #' in practice. #' * _light_: between 0.5 and 1 (high) #' * _mid_: between 0.1 and 0.5 (good) #' * _dark_: below 0.1 (low) #' } #' #' \item{`mcmc_acf()`, `mcmc_acf_bar()`}{ #' Grid of autocorrelation plots by chain and parameter. The `lags` argument #' gives the maximum number of lags at which to calculate the autocorrelation #' function. `mcmc_acf()` is a line plot whereas `mcmc_acf_bar()` is a #' barplot. #' } #'} #' #' @template reference-stan-manual #' @references #' Gelman, A. and Rubin, D. B. (1992). Inference from iterative #' simulation using multiple sequences. *Statistical Science*. 7(4), #' 457--472. #' #' @seealso #' * The [Visual MCMC Diagnostics](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) #' vignette. #' * [MCMC-nuts] for additional MCMC diagnostic plots for models fit #' using the No-U-Turn-Sampler. #' #' @examples #' # autocorrelation #' x <- example_mcmc_draws() #' dim(x) #' dimnames(x) #' #' color_scheme_set("green") #' mcmc_acf(x, pars = c("alpha", "beta[1]")) #' \donttest{ #' color_scheme_set("pink") #' (p <- mcmc_acf_bar(x, pars = c("alpha", "beta[1]"))) #' #' # add horiztonal dashed line at 0.5 #' p + hline_at(0.5, linetype = 2, size = 0.15, color = "gray") #' } #' #' # fake rhat values to use for demonstration #' rhat <- c(runif(100, 1, 1.15)) #' mcmc_rhat_hist(rhat) #' mcmc_rhat(rhat) #' #' # lollipops #' color_scheme_set("purple") #' mcmc_rhat(rhat[1:10], size = 5) #' #' color_scheme_set("blue") #' mcmc_rhat(runif(1000, 1, 1.07)) #' mcmc_rhat(runif(1000, 1, 1.3)) + legend_move("top") # add legend above plot #' #' # fake neff ratio values to use for demonstration #' ratio <- c(runif(100, 0, 1)) #' mcmc_neff_hist(ratio) #' mcmc_neff(ratio) #' #' \dontrun{ #' # Example using rstanarm model (requires rstanarm package) #' library(rstanarm) #' #' # intentionally use small 'iter' so there are some #' # problems with rhat and neff for demonstration #' fit <- stan_glm(mpg ~ ., data = mtcars, iter = 50, refresh = 0) #' rhats <- rhat(fit) #' ratios <- neff_ratio(fit) #' mcmc_rhat(rhats) #' mcmc_neff(ratios, size = 3) #' #' # there's a small enough number of parameters in the #' # model that we can display their names on the y-axis #' mcmc_neff(ratios) + yaxis_text(hjust = 1) #' #' # can also look at autocorrelation #' draws <- as.array(fit) #' mcmc_acf(draws, pars = c("wt", "cyl"), lags = 10) #' #' # increase number of iterations and plots look much better #' fit2 <- update(fit, iter = 500) #' mcmc_rhat(rhat(fit2)) #' mcmc_neff(neff_ratio(fit2)) #' mcmc_acf(as.array(fit2), pars = c("wt", "cyl"), lags = 10) #' } #' NULL # Rhat -------------------------------------------------------------------- #' @rdname MCMC-diagnostics #' @export #' @param rhat A vector of R-hat estimates. #' mcmc_rhat <- function(rhat, ..., size = NULL) { check_ignored_arguments(...) data <- mcmc_rhat_data(rhat) graph <- ggplot( data = data, mapping = aes( x = .data$value, y = .data$parameter, color = .data$rating, fill = .data$rating)) + geom_segment( mapping = aes( yend = .data$parameter, xend = ifelse(min(.data$value) < 1, 1, -Inf)), na.rm = TRUE) + bayesplot_theme_get() if (min(data$value) < 1) { graph <- graph + vline_at(1, color = "gray", linewidth = 1) } brks <- set_rhat_breaks(data$value) graph + diagnostic_points(size) + vline_at( brks[-1], color = "gray", linetype = 2, linewidth = 0.25) + labs(y = NULL, x = expression(hat(R))) + scale_fill_diagnostic("rhat") + scale_color_diagnostic("rhat") + scale_x_continuous(breaks = brks, expand = c(0, .01)) + scale_y_discrete(expand = c(.025,0)) + yaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) } #' @rdname MCMC-diagnostics #' @export mcmc_rhat_hist <- function(rhat, ..., binwidth = NULL, bins = NULL, breaks = NULL) { check_ignored_arguments(...) data <- mcmc_rhat_data(rhat) ggplot( data = data, mapping = aes( x = .data$value, color = .data$rating, fill = .data$rating)) + geom_histogram( linewidth = 0.25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks ) + scale_color_diagnostic("rhat") + scale_fill_diagnostic("rhat") + labs(x = expression(hat(R)), y = NULL) + dont_expand_y_axis(c(0.005, 0)) + bayesplot_theme_get() + yaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) } #' @rdname MCMC-diagnostics #' @export mcmc_rhat_data <- function(rhat, ...) { check_ignored_arguments(...) rhat <- drop_NAs_and_warn(new_rhat(rhat)) diagnostic_data_frame(rhat) } # effective sample size --------------------------------------------------- #' @rdname MCMC-diagnostics #' @export #' @param ratio A vector of *ratios* of effective sample size estimates to #' total sample size. See [neff_ratio()]. #' mcmc_neff <- function(ratio, ..., size = NULL) { check_ignored_arguments(...) data <- mcmc_neff_data(ratio) max_ratio <- max(ratio, na.rm = TRUE) if (max_ratio < 1.25) { additional_breaks <- numeric(0) } else if (max_ratio < 1.5) { additional_breaks <- 1.25 additional_labels <- "1.25" } else { additional_breaks <- seq(1.5, max_ratio, by = 0.5) } breaks <- c(0, 0.1, 0.25, 0.5, 0.75, 1, additional_breaks) ggplot( data, mapping = aes( x = .data$value, y = .data$parameter, color = .data$rating, fill = .data$rating)) + geom_segment( aes(yend = .data$parameter, xend = -Inf), na.rm = TRUE) + diagnostic_points(size) + vline_at( c(0.1, 0.5, 1), color = "gray", linetype = 2, linewidth = 0.25) + labs(y = NULL, x = expression(N[eff]/N)) + scale_fill_diagnostic("neff") + scale_color_diagnostic("neff") + scale_x_continuous( breaks = breaks, # as.character truncates trailing zeroes, while ggplot default does not labels = as.character(breaks), limits = c(0, max(1, max_ratio) + 0.05), expand = c(0, 0)) + bayesplot_theme_get() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) } #' @rdname MCMC-diagnostics #' @export mcmc_neff_hist <- function(ratio, ..., binwidth = NULL, bins = NULL, breaks = NULL) { check_ignored_arguments(...) data <- mcmc_neff_data(ratio) ggplot( data, mapping = aes( x = .data$value, color = .data$rating, fill = .data$rating)) + geom_histogram( linewidth = 0.25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks) + scale_color_diagnostic("neff") + scale_fill_diagnostic("neff") + labs(x = expression(N[eff]/N), y = NULL) + dont_expand_y_axis(c(0.005, 0)) + yaxis_title(FALSE) + yaxis_text(FALSE) + yaxis_ticks(FALSE) + bayesplot_theme_get() } #' @rdname MCMC-diagnostics #' @export mcmc_neff_data <- function(ratio, ...) { check_ignored_arguments(...) ratio <- drop_NAs_and_warn(new_neff_ratio(ratio)) diagnostic_data_frame(ratio) } # autocorrelation --------------------------------------------------------- #' @rdname MCMC-diagnostics #' @export #' @template args-mcmc-x #' @template args-pars #' @template args-regex_pars #' @template args-facet_args #' @param lags The number of lags to show in the autocorrelation plot. mcmc_acf <- function(x, pars = character(), regex_pars = character(), ..., facet_args = list(), lags = 20, size = NULL) { check_ignored_arguments(...) .mcmc_acf( x, pars = pars, regex_pars = regex_pars, facet_args = facet_args, lags = lags, size = size, style = "line" ) } #' @rdname MCMC-diagnostics #' @export mcmc_acf_bar <- function(x, pars = character(), regex_pars = character(), ..., facet_args = list(), lags = 20) { check_ignored_arguments(...) .mcmc_acf( x, pars = pars, regex_pars = regex_pars, facet_args = facet_args, lags = lags, style = "bar" ) } # internal ---------------------------------------------------------------- #' Convert numeric vector of diagnostic values to a factor #' #' @param x A numeric vector. #' @param breaks A numeric vector of length two. The resulting factor variable #' will have three levels ('low', 'ok', and 'high') corresponding to ( #' `x <= breaks[1]`, `breaks[1] < x <= breaks[2]`, `x > breaks[2]`). #' @return A factor the same length as `x` with three levels. #' @noRd diagnostic_factor <- function(x, ...) { UseMethod("diagnostic_factor") } #' @export diagnostic_factor.rhat <- function(x, ..., breaks = c(1.05, 1.1)) { cut(x, breaks = c(-Inf, breaks, Inf), labels = c("low", "ok", "high"), ordered_result = FALSE) } #' @export diagnostic_factor.neff_ratio <- function(x, ..., breaks = c(0.1, 0.5)) { cut(x, breaks = c(-Inf, breaks, Inf), labels = c("low", "ok", "high"), ordered_result = FALSE) } diagnostic_data_frame <- function(x) { x <- auto_name(sort(x)) stopifnot(!anyDuplicated(names(x))) diagnostic <- class(x)[1] d <- tibble::tibble( diagnostic = diagnostic, parameter = factor(seq_along(x), labels = names(x)), value = as.numeric(x), rating = diagnostic_factor(x)) labels <- diagnostic_color_labels[[diagnostic]] d$description <- as.character(labels[d$rating]) d } auto_name <- function(xs) { if (is.null(names(xs))) { names(xs) <- zero_pad_int(seq_along(xs)) } xs } # c(1, 2, 10, 20, 100) => c("001", "002", "010", "020", "100") zero_pad_int <- function(xs) { formatter <- paste0("%0", max(nchar(xs)), "d") sprintf(formatter, xs) } diagnostic_points <- function(size = NULL) { args <- list(shape = 21, na.rm = TRUE) do.call("geom_point", c(args, size = size)) } # Functions wrapping around scale_color_manual() and scale_fill_manual(), used to # color the intervals by rhat value scale_color_diagnostic <- function(diagnostic = c("rhat", "neff")) { d <- match.arg(diagnostic) diagnostic_color_scale(d, aesthetic = "color") } scale_fill_diagnostic <- function(diagnostic = c("rhat", "neff")) { d <- match.arg(diagnostic) diagnostic_color_scale(d, aesthetic = "fill") } diagnostic_color_scale <- function(diagnostic = c("rhat", "neff_ratio"), aesthetic = c("color", "fill")) { diagnostic <- match.arg(diagnostic) aesthetic <- match.arg(aesthetic) dc <- diagnostic_colors(diagnostic, aesthetic) do.call( match.fun(paste0("scale_", aesthetic, "_manual")), list( name = NULL, drop = FALSE, values = dc$values, labels = dc$color_labels ) ) } diagnostic_colors <- function(diagnostic = c("rhat", "neff_ratio"), aesthetic = c("color", "fill")) { diagnostic <- match.arg(diagnostic) aesthetic <- match.arg(aesthetic) color_levels <- c("light", "mid", "dark") if (diagnostic == "neff_ratio") { color_levels <- rev(color_levels) } if (aesthetic == "color") { color_levels <- paste0(color_levels, "_highlight") } color_labels <- diagnostic_color_labels[[diagnostic]] list(diagnostic = diagnostic, aesthetic = aesthetic, color_levels = color_levels, color_labels = color_labels, values = set_names(get_color(color_levels), c("low", "ok", "high"))) } diagnostic_color_labels <- list( rhat = c( low = expression(hat(R) <= 1.05), ok = expression(hat(R) <= 1.10), high = expression(hat(R) > 1.10) ), neff_ratio = c( low = expression(N[eff] / N <= 0.1), ok = expression(N[eff] / N <= 0.5), high = expression(N[eff] / N > 0.5) ) ) # set x-axis breaks based on rhat values set_rhat_breaks <- function(rhat) { br <- c(1, 1.05) if (any(rhat > 1.05)) { br <- c(br, 1.1) } for (k in c(1.5, 2)) { if (any(rhat > k)) { br <- c(br, k) } } if (max(rhat) >= max(br) + .1) { br <- c(br, round(max(rhat), 2)) } br } # drop NAs from a vector and issue warning drop_NAs_and_warn <- function(x) { is_NA <- is.na(x) if (anyNA(x)) { warn(paste0( "Dropped ", sum(is_NA), " NAs from '", deparse(substitute(x)), "'." )) } x[!is_NA] } # Autocorrelation plot (either bar or line) # @param size passed to geom_line() if style="line" .mcmc_acf <- function(x, pars = character(), regex_pars = character(), facet_args = list(), lags = 25, style = c("bar", "line"), size = NULL) { style <- match.arg(style) x <- prepare_mcmc_array(x, pars, regex_pars) plot_data <- acf_data(x = x, lags = lags) if (num_chains(x) > 1) { facet_args$rows <- vars(.data$Chain) facet_args$cols <- vars(.data$Parameter) facet_fun <- "facet_grid" } else { # 1 chain facet_args$facets <- "Parameter" facet_fun <- "facet_wrap" } graph <- ggplot(plot_data, aes(x = .data$Lag, y = .data$AC)) + bayesplot_theme_get() if (style == "bar") { graph <- graph + geom_bar( position = "identity", stat = "identity", linewidth = 0.2, fill = get_color("l"), color = get_color("lh"), width = 1 ) + hline_0(linewidth = 0.25, color = get_color("dh")) } else { graph <- graph + hline_0(linewidth = 0.25, color = get_color("m")) + geom_segment( aes(xend = .data$Lag), yend = 0, color = get_color("l"), linewidth = 0.2 ) + do.call( "geom_line", args = c(list(color = get_color("d")), linewidth = size) ) } graph + do.call(facet_fun, facet_args) + scale_y_continuous( limits = c(min(0, plot_data$AC), 1.05), breaks = c(0, 0.5, 1) ) + scale_x_continuous( limits = c(-0.5, lags + 0.5), breaks = function(x) as.integer(pretty(x, n = 3)), expand = c(0, 0) ) + labs(x = "Lag", y = "Autocorrelation") + force_axes_in_facets() } # Prepare data for autocorr plot # @param x object returned by prepare_mcmc_array # @param lags user's 'lags' argument acf_data <- function(x, lags) { stopifnot(is_mcmc_array(x)) n_iter <- num_iters(x) n_chain <- num_chains(x) n_param <- num_params(x) n_lags <- lags + 1 if (n_lags >= n_iter) { abort(paste0("Too few iterations for lags=", lags, ".")) } data <- melt_mcmc(x) ac_list <- tapply( data[["Value"]], # INDEX = list(data[["Chain"]], data[["Parameter"]]), INDEX = with(data, list(Chain, Parameter)), FUN = function(x, lag.max) { stats::acf(x, lag.max = lag.max, plot = FALSE)$acf[, , 1] }, lag.max = lags, simplify = FALSE ) data.frame( Chain = rep(rep(1:n_chain, each = n_lags), times = n_param), Parameter = factor(rep(1:n_param, each = n_chain * n_lags), labels = levels(data[["Parameter"]])), Lag = rep(seq(0, lags), times = n_chain * n_param), AC = do.call("c", ac_list) ) } ## interal [classes / objects] ------------------------------------------------ new_rhat <- function(x) { # Convert a 1-d arrays to a vectors if (is.array(x) && length(dim(x)) == 1) { x <- as.vector(x) } validate_rhat(as_rhat(x)) } validate_rhat <- function(x) { stopifnot(is.numeric(x), !is.list(x), !is.array(x)) if (any(x < 0, na.rm = TRUE)) { abort("All 'rhat' values must be positive.") } x } as_rhat <- function(x) { structure(x, class = c("rhat", "numeric"), names = names(x)) } #' Indexing method -- needed so that sort, etc. don't strip names. #' @export #' @keywords internal #' @noRd `[.rhat` <- function (x, i, j, drop = TRUE, ...) { as_rhat(NextMethod()) } new_neff_ratio <- function(x) { # Convert a 1-d arrays to a vectors if (is.array(x) && length(dim(x)) == 1) { x <- as.vector(x) } as_neff_ratio(validate_neff_ratio(x)) } validate_neff_ratio <- function(x) { stopifnot(is.numeric(x), !is.list(x), !is.array(x)) if (any(x < 0, na.rm = TRUE)) { abort("All neff ratios must be positive.") } x } as_neff_ratio <- function(x) { structure(x, class = c("neff_ratio", "numeric"), names = names(x)) } #' Indexing method -- needed so that sort, etc. don't strip names. #' @export #' @keywords internal #' @noRd `[.neff_ratio` <- function (x, i, j, drop = TRUE, ...) { as_neff_ratio(NextMethod()) } bayesplot/R/bayesplot-extractors.R0000644000176200001440000002126114127610043016776 0ustar liggesusers#' Extract quantities needed for plotting from model objects #' #' Generics and methods for extracting quantities needed for plotting from #' various types of model objects. Currently methods are provided for stanfit #' (**rstan**), CmdStanMCMC (**cmdstanr**), and stanreg (**rstanarm**) objects, #' but adding new methods should be relatively straightforward. #' #' @name bayesplot-extractors #' @param object The object to use. #' @param ... Arguments passed to individual methods. #' @param pars An optional character vector of parameter names. For #' `nuts_params()` these will be NUTS sampler parameter names rather than #' model parameters. If `pars` is omitted all parameters are included. #' #' @return #' \describe{ #' \item{`log_posterior()`}{ #' `log_posterior()` methods return a molten data frame (see [reshape2::melt()]). #' The data frame should have columns `"Iteration"` (integer), `"Chain"` #' (integer), and `"Value"` (numeric). See **Examples**, below. #' } #' \item{`nuts_params()`}{ #' `nuts_params()` methods return a molten data frame (see [reshape2::melt()]). #' The data frame should have columns `"Parameter"` (factor), `"Iteration"` #' (integer), `"Chain"` (integer), and `"Value"` (numeric). See **Examples**, below. #' } #' \item{`rhat()`, `neff_ratio()`}{ #' Methods return (named) vectors. #' } #' } #' #' @seealso [MCMC-nuts], [MCMC-diagnostics] #' #' @examples #' \dontrun{ #' library(rstanarm) #' fit <- stan_glm(mpg ~ wt, data = mtcars, refresh = 0) #' #' np <- nuts_params(fit) #' head(np) #' tail(np) #' #' lp <- log_posterior(fit) #' head(lp) #' tail(lp) #' } #' NULL # log_posterior ----------------------------------------------------------- #' @rdname bayesplot-extractors #' @export log_posterior <- function(object, ...) { UseMethod("log_posterior") } # nuts_params ------------------------------------------------------------- #' @rdname bayesplot-extractors #' @export nuts_params <- function(object, ...) { UseMethod("nuts_params") } # rhat ------------------------------------------------------------- #' @rdname bayesplot-extractors #' @export rhat <- function(object, ...) { UseMethod("rhat") } # neff_ratio ------------------------------------------------------------- #' @rdname bayesplot-extractors #' @export neff_ratio <- function(object, ...) { UseMethod("neff_ratio") } #' @rdname bayesplot-extractors #' @export #' @method log_posterior stanfit #' @param inc_warmup A logical scalar (defaulting to `FALSE`) indicating #' whether to include warmup draws, if applicable. #' log_posterior.stanfit <- function(object, inc_warmup = FALSE, ...) { lp <- rstan::get_logposterior(object, inc_warmup = inc_warmup, ...) lp <- lapply(lp, as.array) lp <- set_names(reshape2::melt(lp), c("Iteration", "Value", "Chain")) validate_df_classes(lp[, c("Chain", "Iteration", "Value")], c("integer", "integer", "numeric")) } #' @rdname bayesplot-extractors #' @export #' @method log_posterior stanreg #' log_posterior.stanreg <- function(object, inc_warmup = FALSE, ...) { log_posterior.stanfit(object$stanfit, inc_warmup = inc_warmup, ...) } #' @rdname bayesplot-extractors #' @export #' @method log_posterior CmdStanMCMC log_posterior.CmdStanMCMC <- function(object, inc_warmup = FALSE, ...) { lp <- object$draws("lp__", inc_warmup = inc_warmup) lp <- reshape2::melt(lp) lp$variable <- NULL lp <- dplyr::rename_with(lp, capitalize_first) validate_df_classes(lp[, c("Chain", "Iteration", "Value")], c("integer", "integer", "numeric")) } #' @rdname bayesplot-extractors #' @export #' @method nuts_params stanfit nuts_params.stanfit <- function(object, pars = NULL, inc_warmup = FALSE, ...) { suggested_package("rstan") np <- rstan::get_sampler_params(object, inc_warmup = inc_warmup) nuts_params.list(np, pars = pars, ...) } #' @rdname bayesplot-extractors #' @export #' @method nuts_params stanreg #' nuts_params.stanreg <- function(object, pars = NULL, inc_warmup = FALSE, ...) { nuts_params.stanfit(object$stanfit, pars = pars, inc_warmup = inc_warmup, ...) } #' @rdname bayesplot-extractors #' @export #' @method nuts_params list nuts_params.list <- function(object, pars = NULL, ...) { if (!all(sapply(object, is.matrix))) { abort("All list elements should be matrices.") } dd <- lapply(object, dim) if (length(unique(dd)) != 1) { abort("All matrices in the list must have the same dimensions.") } nms <- lapply(object, colnames) if (length(unique(nms)) != 1) { abort("All matrices in the list must have the same column names.") } if (length(pars)) { object <- lapply(object, function(x) x[, pars, drop = FALSE]) } out <- reshape2::melt(object) out <- set_names(out, c("Iteration", "Parameter", "Value", "Chain")) validate_df_classes(out[, c("Chain", "Iteration", "Parameter", "Value")], c("integer", "integer", "factor", "numeric")) } #' @rdname bayesplot-extractors #' @export #' @method nuts_params CmdStanMCMC nuts_params.CmdStanMCMC <- function(object, pars = NULL, ...) { arr <- object$sampler_diagnostics() if (!is.null(pars)) { arr <- arr[,, pars] } out <- reshape2::melt(arr) colnames(out)[colnames(out) == "variable"] <- "parameter" out <- dplyr::rename_with(out, capitalize_first) validate_df_classes(out[, c("Chain", "Iteration", "Parameter", "Value")], c("integer", "integer", "factor", "numeric")) } #' @rdname bayesplot-extractors #' @export #' @method rhat stanfit #' rhat.stanfit <- function(object, pars = NULL, ...) { suggested_package("rstan") s <- if (!is.null(pars)) { rstan::summary(object, pars = pars, ...) } else { rstan::summary(object, ...) } validate_rhat(s$summary[, "Rhat"]) } #' @rdname bayesplot-extractors #' @export #' @method rhat stanreg #' @template args-regex_pars #' rhat.stanreg <- function(object, pars = NULL, regex_pars = NULL, ...) { suggested_package("rstanarm") r <- summary(object, pars = pars, regex_pars = regex_pars, ...)[, "Rhat"] r <- validate_rhat(r) if (!is.null(pars) || !is.null(regex_pars)) { return(r) } r[!names(r) %in% c("mean_PPD", "log-posterior")] } #' @rdname bayesplot-extractors #' @export #' @method rhat CmdStanMCMC rhat.CmdStanMCMC <- function(object, pars = NULL, ...) { .rhat <- utils::getFromNamespace("rhat", "posterior") s <- object$summary(pars, rhat = .rhat)[, c("variable", "rhat")] r <- setNames(s$rhat, s$variable) r <- validate_rhat(r) r[!names(r) %in% "lp__"] } #' @rdname bayesplot-extractors #' @export #' @method neff_ratio stanfit #' neff_ratio.stanfit <- function(object, pars = NULL, ...) { suggested_package("rstan") s <- if (!is.null(pars)) { rstan::summary(object, pars = pars, ...) } else { rstan::summary(object, ...) } tss <- nrow(as.matrix(object, pars = "lp__")) ratio <- s$summary[, "n_eff"] / tss validate_neff_ratio(ratio) } #' @rdname bayesplot-extractors #' @export #' @method neff_ratio stanreg #' neff_ratio.stanreg <- function(object, pars = NULL, regex_pars = NULL, ...) { suggested_package("rstanarm") s <- summary(object, pars = pars, regex_pars = regex_pars, ...) ess <- s[, "n_eff"] tss <- attr(s, "posterior_sample_size") ratio <- ess / tss ratio <- validate_neff_ratio(ratio) if (!is.null(pars) || !is.null(regex_pars)) { return(ratio) } ratio[!names(ratio) %in% c("mean_PPD", "log-posterior")] } #' @rdname bayesplot-extractors #' @export #' @method neff_ratio CmdStanMCMC neff_ratio.CmdStanMCMC <- function(object, pars = NULL, ...) { s <- object$summary(pars, "n_eff" = "ess_basic")[, c("variable", "n_eff")] ess <- setNames(s$n_eff, s$variable) tss <- prod(dim(object$draws())[1:2]) ratio <- ess / tss ratio <- validate_neff_ratio(ratio) ratio[!names(ratio) %in% "lp__"] } # internals --------------------------------------------------------------- # Check that variables in a data.frame have certain classes # @param x data.frame # @param classes character vector of classes (in the order of the column in x) # @return x, unless an error is thrown # validate_df_classes <- function(x, classes = character()) { stopifnot( is.data.frame(x), is.character(classes), ncol(x) >= 1, ncol(x) == length(classes) ) for (j in 1:ncol(x)) { if (!inherits(x[, j], classes[j])) { abort(paste0(colnames(x)[j], " does not have class ", classes[j])) } } x } # capitalize first letter in a string only capitalize_first <- function(name) { name <- tolower(name) # in case whole string is capitalized substr(name, 1, 1) <- toupper(substr(name, 1, 1)) name } bayesplot/R/ppc-overview.R0000644000176200001440000001374214212422623015233 0ustar liggesusers#' Graphical posterior predictive checking #' #' @name PPC-overview #' @aliases PPC #' @family PPCs #' #' @description The **bayesplot** PPC module provides various plotting functions #' for creating graphical displays comparing observed data to simulated data #' from the posterior (or prior) predictive distribution. See the sections #' below for a brief discussion of the ideas behind posterior predictive #' checking, an overview of the available PPC plots, and tips on providing an #' interface to **bayesplot** from another package. #' #' For plots of posterior (or prior) predictive distributions that do _not_ #' include observed data see [PPD-overview] instead. #' #' @details The idea behind posterior predictive checking is simple: if a model #' is a good fit then we should be able to use it to generate data that looks #' a lot like the data we observed. #' #' \subsection{Posterior predictive distribution}{ #' To generate the data used for posterior predictive checks we simulate from #' the *posterior predictive distribution*. The posterior predictive #' distribution is the distribution of the outcome variable implied by a model #' after using the observed data \eqn{y} (a vector of outcome values), and #' typically predictors \eqn{X}, to update our beliefs about the unknown #' parameters \eqn{\theta} in the model. For each draw of the parameters #' \eqn{\theta} from the posterior distribution #' \eqn{p(\theta \,|\, y, X)}{p(\theta | y, X)} #' we generate an entire vector of outcomes. The result is #' an \eqn{S \times N}{S x N} matrix of simulations, where \eqn{S} is the the #' size of the posterior sample (number of draws from the posterior #' distribution) and \eqn{N} is the number of data points in \eqn{y}. That is, #' each row of the matrix is an individual "replicated" dataset of \eqn{N} #' observations. #' } #' \subsection{Notation}{ #' When simulating from the posterior predictive distribution we can use either #' the same values of the predictors \eqn{X} that we used when fitting the model #' or new observations of those predictors. When we use the same values of #' \eqn{X} we denote the resulting simulations by \eqn{y^{rep}}{yrep} as they #' can be thought of as *replications* of the outcome \eqn{y} rather than #' predictions for future observations. This corresponds to the notation from #' Gelman et. al. (2013) and is the notation used throughout the documentation #' for this package. #' } #' \subsection{Graphical posterior predictive checking}{ #' Using the datasets \eqn{y^{rep}}{yrep} drawn from the posterior predictive #' distribution, the functions in the **bayesplot** package produce various #' graphical displays comparing the observed data \eqn{y} to the replications. #' For a more thorough discussion of posterior predictive checking see #' Chapter 6 of Gelman et. al. (2013). #' } #' \subsection{Prior predictive checking}{ #' To use **bayesplot** for *prior* predictive checks you can simply use draws #' from the prior predictive distribution instead of the posterior predictive #' distribution. See Gabry et al. (2019) for more on prior predictive checking #' and when it is reasonable to compare the prior predictive distribution to the #' observed data. If you want to avoid using the observed data for prior #' predictive checks then you can use the **bayesplot** [PPD] plots instead, #' which do not take a `y` argument, or you can use the PPC plots but provide #' plausible or implausible `y` values that you want to compare to the prior #' predictive realizations. #' } #' #' @section PPC plotting functions: The plotting functions for prior and #' posterior predictive checking all have the prefix `ppc_` and all require #' the arguments `y`, a vector of observations, and `yrep`, a matrix of #' replications (in-sample predictions). The plots are organized into several #' categories, each with its own documentation: #' * [PPC-distributions]: Histograms, kernel density estimates, boxplots, and #' other plots comparing the empirical distribution of data `y` to the #' distributions of individual simulated datasets (rows) in `yrep`. #' #' * [PPC-test-statistics]: The distribution of a statistic, or a pair of #' statistics, over the simulated datasets (rows) in `yrep` compared to value of #' the statistic(s) computed from `y`. #' #' * [PPC-intervals]: Interval estimates of `yrep` with `y` #' overlaid. The x-axis variable can be optionally specified by the user #' (e.g. to plot against a predictor variable or over time). #' #' * [PPC-errors]: Plots of predictive errors (`y - yrep`) computed from `y` and #' each of the simulated datasets (rows) in `yrep`. For binomial models binned #' error plots are also available. #' #' * [PPC-scatterplots]: Scatterplots (and similar visualizations) of the data #' `y` vs. individual simulated datasets (rows) in `yrep`, or vs. the average #' value of the distributions of each data point (columns) in `yrep`. #' #' * [PPC-discrete]: PPC functions that can only be used if `y` and `yrep` are #' discrete. For example, rootograms for count outcomes and bar plots for #' ordinal, categorical, and multinomial outcomes. #' #' * [PPC-loo]: PPC functions for predictive checks based on (approximate) #' leave-one-out (LOO) cross-validation. #'' #' * [PPC-censoring]: PPC functions comparing the empirical #' distribution of censored data `y` to the distributions of individual #' simulated datasets (rows) in `yrep`. #' #' @section Providing an interface for predictive checking from another package: #' #' In addition to the various plotting functions, the **bayesplot** package #' provides the S3 generic [pp_check()]. Authors of \R packages for #' Bayesian inference are encouraged to define `pp_check()` methods for the #' fitted model objects created by their packages. See the package vignettes for #' more details and a simple example, and see the **rstanarm** and **brms** #' packages for full examples of `pp_check()` methods. #' #' @template reference-vis-paper #' @templateVar bdaRef (Ch. 6) #' @template reference-bda #' NULL bayesplot/R/mcmc-diagnostics-nuts.R0000644000176200001440000004242414556270424017032 0ustar liggesusers#' Diagnostic plots for the No-U-Turn-Sampler (NUTS) #' #' Diagnostic plots for the No-U-Turn-Sampler (NUTS), the default MCMC algorithm #' used by [Stan](https://mc-stan.org). See the **Plot Descriptions** section, #' below. #' #' @name MCMC-nuts #' @aliases NUTS #' @family MCMC #' #' @param x A molten data frame of NUTS sampler parameters, either created by #' [nuts_params()] or in the same form as the object returned by #' [nuts_params()]. #' @param lp A molten data frame of draws of the log-posterior or, more #' commonly, of a quantity equal to the log-posterior up to a constant. #' `lp` should either be created via [log_posterior()] or be an #' object with the same form as the object returned by #' [log_posterior()]. #' @param chain A positive integer for selecting a particular chain. The default #' (`NULL`) is to merge the chains before plotting. If `chain = k` #' then the plot for chain `k` is overlaid (in a darker shade but with #' transparency) on top of the plot for all chains. The `chain` argument #' is not used by `mcmc_nuts_energy()`. #' @param ... Currently ignored. #' #' @return A gtable object (the result of calling #' [gridExtra::arrangeGrob()]) created from several ggplot objects, #' except for `mcmc_nuts_energy()`, which returns a ggplot object. #' #' @section Quick Definitions: #' For more details see Stan Development Team (2016) and Betancourt (2017). #' * `accept_stat__`: the average acceptance probabilities of all #' possible samples in the proposed tree. #' * `divergent__`: the number of leapfrog transitions with diverging #' error. Because NUTS terminates at the first divergence this will be either #' 0 or 1 for each iteration. #' * `stepsize__`: the step size used by NUTS in its Hamiltonian #' simulation. #' * `treedepth__`: the depth of tree used by NUTS, which is the log #' (base 2) of the number of leapfrog steps taken during the Hamiltonian #' simulation. #' * `energy__`: the value of the Hamiltonian (up to an additive #' constant) at each iteration. #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_nuts_acceptance()`}{ #' Three plots: #' * Histogram of `accept_stat__` with vertical lines indicating the #' mean (solid line) and median (dashed line). #' * Histogram of `lp__` with vertical #' lines indicating the mean (solid line) and median (dashed line). #' * Scatterplot of `accept_stat__` vs `lp__`. #' } #' #' \item{`mcmc_nuts_divergence()`}{ #' Two plots: #' * Violin plots of `lp__|divergent__=1` and `lp__|divergent__=0`. #' * Violin plots of `accept_stat__|divergent__=1` and #' `accept_stat__|divergent__=0`. #' } #' #' \item{`mcmc_nuts_stepsize()`}{ #' Two plots: #' * Violin plots of `lp__` by chain ordered by `stepsize__` value. #' * Violin plots of `accept_stat__` by chain ordered by `stepsize__` value. #' } #' #' \item{`mcmc_nuts_treedepth()`}{ #' Three plots: #' * Violin plots of `lp__` by value of `treedepth__`. #' * Violin plots of `accept_stat__` by value of `treedepth__`. #' * Histogram of `treedepth__`. #' } #' #' \item{`mcmc_nuts_energy()`}{ #' Overlaid histograms showing `energy__` vs the change in #' `energy__`. See Betancourt (2016) for details. #' } #' } #' #' @template reference-betancourt #' @template reference-nuts #' @template reference-stan-manual #' #' @seealso #' * The [Visual MCMC Diagnostics](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) #' vignette. #' * Several other plotting functions are not NUTS-specific but take optional #' extra arguments if the model was fit using NUTS: #' * [mcmc_trace()]: show divergences as tick marks below the #' trace plot. #' * [mcmc_parcoord()]: change the color/size/transparency of lines #' corresponding to divergences. #' * [mcmc_scatter()]: change the color/size/shape of points #' corresponding to divergences. #' * [mcmc_pairs()]: change the color/size/shape of points #' corresponding divergences and/or max treedepth saturation. #' #' @examples #' \dontrun{ #' library(ggplot2) #' library(rstanarm) #' fit <- stan_glm(mpg ~ wt + am, data = mtcars, iter = 1000, refresh = 0) #' np <- nuts_params(fit) #' lp <- log_posterior(fit) #' #' color_scheme_set("brightblue") #' mcmc_nuts_acceptance(np, lp) #' mcmc_nuts_acceptance(np, lp, chain = 2) #' #' mcmc_nuts_divergence(np, lp) #' mcmc_nuts_stepsize(np, lp) #' mcmc_nuts_treedepth(np, lp) #' #' color_scheme_set("red") #' mcmc_nuts_energy(np) #' mcmc_nuts_energy(np, merge_chains = TRUE, binwidth = .15) #' mcmc_nuts_energy(np) + #' facet_wrap(vars(Chain), nrow = 1) + #' coord_fixed(ratio = 150) + #' ggtitle("NUTS Energy Diagnostic") #' } #' NULL #' @rdname MCMC-nuts #' @export #' @template args-hist #' mcmc_nuts_acceptance <- function(x, lp, chain = NULL, ..., binwidth = NULL, bins = NULL, breaks = NULL) { suggested_package("gridExtra") check_ignored_arguments(...) x <- validate_nuts_data_frame(x, lp) n_chain <- length(unique(lp$Chain)) chain <- validate_enough_chains(chain, num_chains(x)) overlay_chain <- !is.null(chain) accept_stat <- dplyr::filter(x, .data$Parameter == "accept_stat__") data <- suppressWarnings( dplyr::bind_rows(accept_stat, data.frame(lp, Parameter = "lp__")) ) grp_par <- group_by(data, .data$Parameter) stats_par <- summarise(grp_par, Mean = mean(.data$Value), Median = median(.data$Value)) hists <- ggplot(data, aes(x = .data$Value, y = after_stat(density))) + geom_histogram( fill = get_color("l"), color = get_color("lh"), linewidth = 0.25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks ) + bayesplot_theme_get() if (!overlay_chain) { hists <- hists + geom_vline( aes(xintercept = .data$Mean), data = stats_par, color = get_color("dh") ) + geom_vline( aes(xintercept = .data$Median), data = stats_par, color = get_color("d"), linetype = 2 ) } hists <- hists + dont_expand_y_axis(c(0.005, 0)) + facet_wrap(vars(.data$Parameter), scales = "free") + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) + xaxis_title(FALSE) scatter_data <- data.frame( x = accept_stat$Value, y = lp$Value ) scatter <- ggplot(scatter_data) + geom_point( aes(x = .data$x, y = .data$y), alpha = 0.75, shape = 21, fill = get_color(ifelse(overlay_chain, "l", "m")), color = get_color(ifelse(overlay_chain, "lh", "mh")) ) + labs(x = "accept_stat__", y = "lp__") + bayesplot_theme_get() if (overlay_chain) { hists <- hists + geom_histogram( data = dplyr::filter(data, .data$Chain == chain), fill = get_color("d"), color = NA, alpha = 0.5, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks ) chain_scatter_data <- data.frame( x = accept_stat$Value[accept_stat$Chain == chain], y = lp$Value[lp$Chain == chain] ) scatter <- scatter + geom_point( aes(x = .data$x, y = .data$y), color = get_color("d"), alpha = 0.5, data = chain_scatter_data ) } nuts_plot <- gridExtra::arrangeGrob(hists, scatter, nrow = 2) as_bayesplot_grid(nuts_plot) } #' @rdname MCMC-nuts #' @export mcmc_nuts_divergence <- function(x, lp, chain = NULL, ...) { suggested_package("gridExtra") check_ignored_arguments(...) x <- validate_nuts_data_frame(x, lp) chain <- validate_enough_chains(chain, num_chains(x)) overlay_chain <- !is.null(chain) accept_stat <- dplyr::filter(x, .data$Parameter == "accept_stat__") divergent <- dplyr::filter(x, .data$Parameter == "divergent__") divergent$Value <- factor(divergent$Value, levels = c(0, 1), labels = c("No divergence", "Divergence")) violin_lp_data <- data.frame(divergent, lp = lp$Value) violin_lp <- ggplot(violin_lp_data, aes(x = .data$Value, y = .data$lp)) + geom_violin(fill = get_color("l"), color = get_color("lh")) + ylab("lp__") + xaxis_title(FALSE) + bayesplot_theme_get() violin_accept_stat_data <- data.frame(divergent, as = accept_stat$Value) violin_accept_stat <- ggplot(violin_accept_stat_data, aes(x = .data$Value, y = .data$as)) + geom_violin(fill = get_color("l"), color = get_color("lh")) + ylab("accept_stat__") + scale_y_continuous(limits = c(NA, 1.05)) + xaxis_title(FALSE) + bayesplot_theme_get() div_count <- table(divergent$Value)[[2]] div_text <- ngettext(div_count, "divergence", "divergences") div_count_label <- paste(div_count, div_text) if (!is.null(chain)) { violin_lp <- violin_lp + chain_violin(violin_lp_data, chain) violin_accept_stat <- violin_accept_stat + chain_violin(violin_accept_stat_data, chain) div_count_by_chain <- table(divergent$Value, divergent$Chain)["Divergence", chain] div_count_label <- paste0(div_count_label, " (", div_count_by_chain, " from chain ", chain, ")") } violin_lp <- violin_lp + labs(subtitle = div_count_label) nuts_plot <- gridExtra::arrangeGrob(violin_lp, violin_accept_stat, nrow = 2) as_bayesplot_grid(nuts_plot) } #' @rdname MCMC-nuts #' @export mcmc_nuts_stepsize <- function(x, lp, chain = NULL, ...) { suggested_package("gridExtra") check_ignored_arguments(...) x <- validate_nuts_data_frame(x, lp) chain <- validate_enough_chains(chain, num_chains(x)) overlay_chain <- !is.null(chain) stepsize <- dplyr::filter(x, .data$Parameter == "stepsize__") accept_stat <- dplyr::filter(x, .data$Parameter == "accept_stat__") stepsize_by_chain <- stepsize %>% group_by(.data$Chain) %>% summarise(ss = dplyr::first(.data$Value)) stepsize_labels_text <- stepsize_by_chain %>% arrange(.data$ss) %>% mutate(value = format(round(.data$ss, 3), digits = 3), label = paste0(.data$value, "\n(chain ", .data$Chain, ")")) %>% pull() stepsize_labels <- scale_x_discrete(labels = stepsize_labels_text) violin_lp_data <- dplyr::left_join(lp, stepsize_by_chain, by = "Chain") violin_lp <- ggplot(violin_lp_data, aes(x = as.factor(.data$ss), y = .data$Value)) + geom_violin(fill = get_color("l"), color = get_color("lh")) + ylab("lp__") + stepsize_labels + xaxis_title(FALSE) + bayesplot_theme_get() violin_accept_stat_data <- dplyr::left_join(accept_stat, stepsize_by_chain, by = "Chain") violin_accept_stat <- ggplot(violin_accept_stat_data, aes(x = as.factor(.data$ss), y = .data$Value)) + geom_violin(fill = get_color("l"), color = get_color("lh")) + ylab("accept_stat__") + scale_y_continuous(limits = c(NA, 1.05)) + stepsize_labels + xaxis_title(FALSE) + bayesplot_theme_get() if (!is.null(chain)) { violin_lp <- violin_lp + chain_violin(violin_lp_data, chain) violin_accept_stat <- violin_accept_stat + chain_violin(violin_accept_stat_data, chain) } nuts_plot <- gridExtra::arrangeGrob(violin_lp, violin_accept_stat, nrow = 2) as_bayesplot_grid(nuts_plot) } #' @rdname MCMC-nuts #' @export mcmc_nuts_treedepth <- function(x, lp, chain = NULL, ...) { suggested_package("gridExtra") check_ignored_arguments(...) x <- validate_nuts_data_frame(x, lp) chain <- validate_enough_chains(chain, num_chains(x)) overlay_chain <- !is.null(chain) treedepth <- dplyr::filter(x, .data$Parameter == "treedepth__") accept_stat <- dplyr::filter(x, .data$Parameter == "accept_stat__") hist_td <- ggplot(treedepth, aes(x = .data$Value, y = after_stat(density))) + geom_histogram( fill = get_color("l"), color = get_color("lh"), linewidth = 0.2, na.rm = TRUE, binwidth = 1 ) + xlab("treedepth__") + bayesplot_theme_get() + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) violin_lp_data <- data.frame(treedepth, lp = lp$Value) violin_lp <- ggplot(violin_lp_data, aes(x = factor(.data$Value), y = .data$lp)) + geom_violin(fill = get_color("l"), color = get_color("lh")) + labs(x = "treedepth__", y = "lp__") + bayesplot_theme_get() violin_accept_stat_data <- data.frame(treedepth, as = accept_stat$Value) violin_accept_stat <- ggplot(violin_accept_stat_data, aes(x = factor(.data$Value), y = .data$as)) + geom_violin(fill = get_color("l"), color = get_color("lh")) + labs(x = "treedepth__", y = "accept_stat__") + scale_y_continuous(breaks = c(0, 0.5, 1)) + bayesplot_theme_get() if (overlay_chain) { hist_td <- hist_td + geom_histogram( data = dplyr::filter(treedepth, .data$Chain == chain), fill = get_color("d"), color = NA, alpha = 0.5, na.rm = TRUE, binwidth = 1 ) + dont_expand_y_axis() violin_lp <- violin_lp + chain_violin(violin_lp_data, chain) violin_accept_stat <- violin_accept_stat + chain_violin(violin_accept_stat_data, chain) } nuts_plot <- gridExtra::grid.arrange( gridExtra::arrangeGrob(violin_lp, violin_accept_stat, nrow = 1), hist_td, nrow = 2 ) as_bayesplot_grid(nuts_plot) } #' @rdname MCMC-nuts #' @export #' @param alpha For `mcmc_nuts_energy()` only, the transparency (alpha) level #' in `[0,1]` used for the overlaid histogram. #' @param merge_chains For `mcmc_nuts_energy()` only, should all chains be #' merged or displayed separately? The default is `FALSE`, i.e., to show #' the chains separately. #' mcmc_nuts_energy <- function(x, ..., binwidth = NULL, bins = NULL, breaks = NULL, alpha = 0.5, merge_chains = FALSE) { check_ignored_arguments(...) x <- validate_nuts_data_frame(x) energy <- dplyr::filter(x, .data$Parameter == "energy__") # lag() (stats::lag()) here doesn't work, but dplyr::lag() does data <- energy %>% group_by(.data$Chain) %>% mutate( Ediff = .data$Value - dplyr::lag(.data$Value), E_centered = .data$Value - mean(.data$Value), Ediff_centered = .data$Ediff - mean(.data$Ediff, na.rm = TRUE) ) fills <- set_names(get_color(c("l", "m")), c("E_fill", "Ediff_fill")) clrs <- set_names(get_color(c("lh", "mh")), c("E_fill", "Ediff_fill")) aes_labs <- c(expression(pi[E]), expression(pi[paste(Delta, E)])) graph <- ggplot(data, aes(y = after_stat(density))) + geom_histogram( aes( x = .data$Ediff_centered, fill = "Ediff_fill", color = "Ediff_fill" ), linewidth = 0.25, na.rm = TRUE, binwidth = binwidth, bins = bins, breaks = breaks ) + geom_histogram( aes( x = .data$E_centered, fill = "E_fill", color = "E_fill" ), linewidth = 0.25, na.rm = TRUE, alpha = alpha, binwidth = binwidth, bins = bins, breaks = breaks ) + scale_fill_manual("", values = fills, labels = aes_labs) + scale_color_manual("", values = clrs, labels = aes_labs) + dont_expand_y_axis(c(0.005, 0)) + scale_x_continuous(expand = c(0.2, 0)) + labs(y = NULL, x = expression(E - bar(E))) + bayesplot_theme_get() + space_legend_keys() + theme(legend.text = element_text(size = rel(1.1))) + yaxis_text(FALSE) + yaxis_title(FALSE) + yaxis_ticks(FALSE) if (merge_chains) { return(graph) } graph + facet_wrap(vars(.data$Chain)) + force_axes_in_facets() } # internal ---------------------------------------------------------------- validate_enough_chains <- function(chain = NULL, n_chain) { if (!is.null(chain)) { stopifnot(chain >= 1) if (!isTRUE(n_chain >= chain)) { abort(paste("'chain' is", chain, "but only", n_chain, "chains found.")) } } chain } #' @param x data frame with nuts params #' @param lp data frame with `lp__` #' @noRd validate_nuts_data_frame <- function(x, lp) { if (!is.data.frame(x)) { abort("NUTS parameters should be in a data frame.") } valid_cols <- sort(c("Iteration", "Parameter", "Value", "Chain")) if (!identical(sort(colnames(x)), valid_cols)) { abort(paste( "NUTS parameter data frame must have columns:", paste(valid_cols, collapse = ", ") )) } if (missing(lp)) { lp <- NULL } if (!is.null(lp)) { if (!is.data.frame(lp)) { abort("lp should be in a data frame.") } valid_lp_cols <- sort(c("Iteration", "Value", "Chain")) if (!identical(sort(colnames(lp)), valid_lp_cols)) { abort(paste( "lp data frame must have columns:", paste(valid_lp_cols, collapse = ", ") )) } n_chain <- num_chains(x) n_lp_chain <- num_chains(lp) if (n_chain != n_lp_chain) { abort(paste( "Number of chains for NUTS parameters is", n_chain, "but number of chains for lp is", n_lp_chain )) } } x } chain_violin <- function(df, chain, fill = "d", color = NA, alpha = 0.5) { geom_violin( data = dplyr::filter(df, .data$Chain == chain), fill = get_color(fill), color = color, alpha = alpha ) } bayesplot/R/pp_check.R0000644000176200001440000000564514127610043014364 0ustar liggesusers#' Posterior (or prior) predictive checks (S3 generic and default method) #' #' S3 generic with simple default method. The intent is to provide a generic so #' authors of other \R packages who wish to provide interfaces to the functions #' in **bayesplot** will be encouraged to include `pp_check()` methods in their #' package, preserving the same naming conventions for posterior (and prior) #' predictive checking across many \R packages for Bayesian inference. This is #' for the convenience of both users and developers. See the **Details** and #' **Examples** sections, below, and the package vignettes for examples of #' defining `pp_check()` methods. #' #' @export #' @param object Typically a fitted model object. The default method, however, #' takes `object` to be a `y` (outcome) vector. #' @param ... For the generic, arguments passed to individual methods. For the #' default method, these are additional arguments to pass to `fun`. #' @return The exact form of the value returned by `pp_check()` may vary by #' the class of `object`, but for consistency we encourage authors of #' methods to return the ggplot object created by one of **bayesplot**'s #' plotting functions. The default method returns the object returned by `fun`. #' #' @details A package that creates fitted model objects of class `"foo"` #' can include a method `pp_check.foo()` that prepares the appropriate #' inputs (`y`, `yrep`, etc.) for the **bayesplot** functions. The #' `pp_check.foo()` method may, for example, let the user choose between #' various plots, calling the functions from **bayesplot** internally as #' needed. See **Examples**, below, and the package vignettes. #' #' @examples #' # default method #' y <- example_y_data() #' yrep <- example_yrep_draws() #' pp_check(y, yrep[1:50,], ppc_dens_overlay) #' #' g <- example_group_data() #' pp_check(y, yrep, fun = "stat_grouped", group = g, stat = "median") #' #' # defining a method #' x <- list(y = rnorm(50), yrep = matrix(rnorm(5000), nrow = 100, ncol = 50)) #' class(x) <- "foo" #' pp_check.foo <- function(object, ..., type = c("multiple", "overlaid")) { #' y <- object[["y"]] #' yrep <- object[["yrep"]] #' switch(match.arg(type), #' multiple = ppc_hist(y, yrep[1:min(8, nrow(yrep)),, drop = FALSE]), #' overlaid = ppc_dens_overlay(y, yrep)) #' } #' pp_check(x) #' pp_check(x, type = "overlaid") #' pp_check <- function(object, ...) { UseMethod("pp_check") } #' @rdname pp_check #' @export #' @param yrep For the default method, a `yrep` matrix passed to `fun`. #' @param fun For the default method, the plotting function to call. Can be any #' of the [PPC] functions. The `"ppc_"` prefix can optionally be dropped if #' `fun` is specified as a string. #' pp_check.default <- function(object, yrep, fun, ...) { if (is.character(fun) && substr(fun, 1, 4) != "ppc_") { fun <- paste0("ppc_", fun) } .ppcfun <- match.fun(fun) .ppcfun(y = object, yrep = yrep, ...) } bayesplot/R/helpers-testthat.R0000644000176200001440000000123514127610043016077 0ustar liggesusersexpect_gg <- function(x) { testthat::expect_s3_class(x, "ggplot") invisible(ggplot_build(x)) } expect_gtable <- function(x) testthat::expect_s3_class(x, "gtable") expect_mcmc_array <- function(x) testthat::expect_true(is_mcmc_array(x)) expect_bayesplot_grid <- function(x) testthat::expect_true(is_bayesplot_grid(x)) #' Insert fake divergences for testing purposes #' #' @noRd #' @param np Data frame returned by `nuts_params()` #' @return `np` with every other iter marked as a divergence ensure_divergences <- function(np) { divs <- rep_len(c(0,1), length.out = sum(np$Parameter=="divergent__")) np$Value[np$Parameter=="divergent__"] <- divs return(np) } bayesplot/R/helpers-gg.R0000644000176200001440000000762314335264324014653 0ustar liggesusers# ggplot2 convenience functions for internal use -------------------------- #' Do nothing plotting geom #' #' Adding `geom_ignore()` to a ggplot will not affect its appearance. This #' function is useful for when plot elements should appear conditionally. #' #' @param ... arguments to a ggplot2 function to be ignored #' @noRd #' @examples #' # Draw a vertical line at zero (or do nothing) #' xs <- -2:2 #' maybe_vertical_line <- if (0 > min(xs) && 0 < max(xs)) { #' vline_0(color = "gray90", linewidth = 0.5) #' } else { #' geom_ignore() #' } geom_ignore <- function(...) { geom_blank( mapping = NULL, data = NULL, show.legend = FALSE, inherit.aes = FALSE) } #' Add new aesthetic mappings to a list of aesthetic mappings #' #' @param mapping a list of `uneval` aesthetic mappings (created by `aes_()`) #' @param ... additional mappings to add, e.g., `color = ~ parameter` #' @return the updated list #' @noRd modify_aes_ <- function(mapping, ...) { utils::modifyList(mapping, aes_(...)) } #' Same as `modify_aes_` but using `aes()` instead of `aes_()` (now deprecated). #' Often `...` will need to contain expression of the form `.data$x` to avoid R cmd check warnings #' @noRd modify_aes <- function(mapping, ...) { utils::modifyList(mapping, aes(...)) } facet_wrap_parsed <- function(...) { facet_wrap(..., labeller = label_parsed) } dont_expand_y_axis <- function(expand = c(0,0)) { scale_y_continuous(expand = expand) } dont_expand_x_axis <- function(expand = c(0,0)) { scale_x_continuous(expand = expand) } dont_expand_axes <- function() { coord_cartesian(expand = FALSE) } force_axes_in_facets <- function() { thm <- bayesplot_theme_get() annotate( "segment", x = c(-Inf, -Inf), xend = c(Inf,-Inf), y = c(-Inf,-Inf), yend = c(-Inf, Inf), color = thm$axis.line$colour %||% thm$line$colour %||% "black", linewidth = thm$axis.line$linewidth %||% thm$line$linewidth %||% 0.5 ) } force_x_axis_in_facets <- function() { thm <- bayesplot_theme_get() annotate( "segment", x = -Inf, xend = Inf, y = -Inf, yend = -Inf, color = thm$axis.line$colour %||% thm$line$colour %||% "black", linewidth = thm$axis.line$linewidth %||% thm$line$linewidth %||% 0.5 ) } no_legend_spacing <- function() { theme(legend.spacing.y = unit(0, "cm")) } reduce_legend_spacing <- function(cm) { theme(legend.spacing.y = unit(-cm, "cm")) } space_legend_keys <- function(relative_size = 2, color = "white") { theme(legend.key = element_rect(linewidth = rel(relative_size), color = color)) } # set aesthetic mapping for histograms depending on freq argument set_hist_aes <- function(freq = TRUE, ...) { if (freq) { # aes_(x = ~ value, ...) aes(x = .data$value, ...) } else { # aes_(x = ~ value, y = ~ after_stat(density), ...) aes(x = .data$value, y = after_stat(density), ...) } } scale_color_ppc <- function(name = NULL, values = NULL, labels = NULL, ...) { scale_color_manual( name = name %||% "", values = values %||% get_color(c("dh", "lh")), labels = labels %||% c(y_label(), yrep_label()), ... ) } scale_fill_ppc <- function(name = NULL, values = NULL, labels = NULL, ...) { scale_fill_manual( name = name %||% "", values = values %||% get_color(c("d", "l")), labels = labels %||% c(y_label(), yrep_label()), ... ) } scale_color_ppd <- function(name = NULL, values = get_color("mh"), labels = ypred_label(), ...) { scale_color_ppc(name = name, values = values, labels = labels, ...) } scale_fill_ppd <- function(name = NULL, values = get_color("m"), labels = ypred_label(), ...) { scale_fill_ppc(name = name, values = values, labels = labels, ...) } bayesplot/R/mcmc-intervals.R0000644000176200001440000007026014556270424015542 0ustar liggesusers#' Plot interval estimates from MCMC draws #' #' Plot central (quantile-based) posterior interval estimates from MCMC draws. #' See the **Plot Descriptions** section, below, for details. #' #' @name MCMC-intervals #' @family MCMC #' #' @template args-mcmc-x #' @template args-pars #' @template args-regex_pars #' @template args-transformations #' @param ... Currently unused. #' @param prob The probability mass to include in the inner interval (for #' `mcmc_intervals()`) or in the shaded region (for `mcmc_areas()`). The #' default is `0.5` (50% interval) and `1` for `mcmc_areas_ridges()`. #' @param prob_outer The probability mass to include in the outer interval. The #' default is `0.9` for `mcmc_intervals()` (90% interval) and #' `1` for `mcmc_areas()` and for `mcmc_areas_ridges()`. #' @param area_method How to constrain the areas in `mcmc_areas()`. The #' default is `"equal area"`, setting the density curves to have the same #' area. With `"equal height"`, the curves are scaled so that the highest #' points across the curves are the same height. The method `"scaled #' height"` tries a compromise between to the two: the heights from #' `"equal height"` are scaled using `height*sqrt(height)` #' @param point_est The point estimate to show. Either `"median"` (the #' default), `"mean"`, or `"none"`. #' @param inner_size,outer_size For `mcmc_intervals()`, the size of #' the inner and interval segments, respectively. #' @param point_size For `mcmc_intervals()`, the size of point estimate. #' @param rhat An optional numeric vector of R-hat estimates, with one element #' per parameter included in `x`. If `rhat` is provided, the intervals/areas #' and point estimates in the resulting plot are colored based on R-hat value. #' See [rhat()] for methods for extracting R-hat estimates. #' @template args-density-controls #' #' @template return-ggplot-or-data #' #' @section Plot Descriptions: #' \describe{ #' \item{`mcmc_intervals()`}{ #' Plots of uncertainty intervals computed from posterior draws with all #' chains merged. #' } #' \item{`mcmc_areas()`}{ #' Density plots computed from posterior draws with all chains merged, #' with uncertainty intervals shown as shaded areas under the curves. #' } #' \item{`mcmc_areas_ridges()`}{ #' Density plot, as in `mcmc_areas()`, but drawn with overlapping #' ridgelines. This plot provides a compact display of (hierarchically) #' related distributions. #' } #' } #' #' @examples #' set.seed(9262017) #' #' # load ggplot2 to use its functions to modify our plots #' library(ggplot2) #' #' # some parameter draws to use for demonstration #' x <- example_mcmc_draws(params = 6) #' dim(x) #' dimnames(x) #' #' color_scheme_set("brightblue") #' mcmc_intervals(x) #' mcmc_intervals(x, pars = c("beta[1]", "beta[2]")) #' mcmc_areas(x, regex_pars = "beta\\[[1-3]\\]", prob = 0.8) + #' labs( #' title = "Posterior distributions", #' subtitle = "with medians and 80% intervals" #' ) #' #' color_scheme_set("red") #' p <- mcmc_areas( #' x, #' pars = c("alpha", "beta[4]"), #' prob = 2/3, #' prob_outer = 0.9, #' point_est = "mean", #' border_size = 1.5 # make the ridgelines fatter #' ) #' plot(p) #' #' \donttest{ #' # control spacing at top and bottom of plot #' # see ?ggplot2::expansion #' p + scale_y_discrete( #' limits = c("beta[4]", "alpha"), #' expand = expansion(add = c(1, 2)) #' ) #' p + scale_y_discrete( #' limits = c("beta[4]", "alpha"), #' expand = expansion(add = c(.1, .3)) #' ) #' #' # relabel parameters #' p + scale_y_discrete( #' labels = c("alpha" = "param label 1", #' "beta[4]" = "param label 2") #') #' #' # relabel parameters and define the order #' p + scale_y_discrete( #' labels = c("alpha" = "param label 1", #' "beta[4]" = "param label 2"), #' limits = c("beta[4]", "alpha") #' ) #' #' # color by rhat value #' color_scheme_set("blue") #' fake_rhat_values <- c(1, 1.07, 1.3, 1.01, 1.15, 1.005) #' mcmc_intervals(x, rhat = fake_rhat_values) #' #' # get the dataframe that is used in the plotting functions #' mcmc_intervals_data(x) #' mcmc_intervals_data(x, rhat = fake_rhat_values) #' mcmc_areas_data(x, pars = "alpha") #' #' color_scheme_set("gray") #' p <- mcmc_areas(x, pars = c("alpha", "beta[4]"), rhat = c(1, 1.1)) #' p + legend_move("bottom") #' p + legend_move("none") # or p + legend_none() #' #' } #' #' # Different area calculations #' b3 <- c("beta[1]", "beta[2]", "beta[3]") #' #' mcmc_areas(x, pars = b3, area_method = "equal area") + #' labs( #' title = "Curves have same area", #' subtitle = "A wide, uncertain interval is spread thin when areas are equal" #' ) #' #' mcmc_areas(x, pars = b3, area_method = "equal height") + #' labs( #' title = "Curves have same maximum height", #' subtitle = "Local curvature is clearer but more uncertain curves use more area" #' ) #' #' mcmc_areas(x, pars = b3, area_method = "scaled height") + #' labs( #' title = "Same maximum heights but heights scaled by square-root", #' subtitle = "Compromise: Local curvature is accentuated and less area is used" #' ) #' #' \donttest{ #' # apply transformations #' mcmc_intervals( #' x, #' pars = c("beta[2]", "sigma"), #' transformations = list("sigma" = "log", "beta[2]" = function(x) x + 3) #' ) #' #' # apply same transformation to all selected parameters #' mcmc_intervals(x, regex_pars = "beta", transformations = "exp") #' } #' #' \dontrun{ #' # example using fitted model from rstanarm package #' library(rstanarm) #' fit <- stan_glm( #' mpg ~ 0 + wt + factor(cyl), #' data = mtcars, #' iter = 500, #' refresh = 0 #' ) #' x <- as.matrix(fit) #' #' color_scheme_set("teal") #' mcmc_intervals(x, point_est = "mean", prob = 0.8, prob_outer = 0.95) #' mcmc_areas(x, regex_pars = "cyl", bw = "SJ", #' rhat = rhat(fit, regex_pars = "cyl")) #' } #' #' \dontrun{ #' # Example of hierarchically related parameters #' # plotted with ridgelines #' m <- shinystan::eight_schools@posterior_sample #' mcmc_areas_ridges(m, pars = "mu", regex_pars = "theta", border_size = 0.75) + #' ggtitle("Treatment effect on eight schools (Rubin, 1981)") #' } #' NULL #' @rdname MCMC-intervals #' @export mcmc_intervals <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., prob = 0.5, prob_outer = 0.9, point_est = c("median", "mean", "none"), outer_size = 0.5, inner_size = 2, point_size = 4, rhat = numeric()) { check_ignored_arguments(...) data <- mcmc_intervals_data(x, pars, regex_pars, transformations, prob = prob, prob_outer = prob_outer, point_est = point_est, rhat = rhat) color_by_rhat <- rlang::has_name(data, "rhat_rating") no_point_est <- all(data$point_est == "none") x_lim <- range(c(data$ll, data$hh)) x_range <- diff(x_lim) x_lim[1] <- x_lim[1] - 0.05 * x_range x_lim[2] <- x_lim[2] + 0.05 * x_range # faint vertical line at zero if zero is within x_lim layer_vertical_line <- if (0 > x_lim[1] && 0 < x_lim[2]) { vline_0(color = "gray90", linewidth = 0.5) } else { geom_ignore() } args_outer <- list( mapping = aes(x = .data$ll, xend = .data$hh, y = .data$parameter, yend = .data$parameter), color = get_color("mid"), linewidth = outer_size ) args_inner <- list( mapping = aes(x = .data$l, xend = .data$h, y = .data$parameter, yend = .data$parameter), linewidth = inner_size, show.legend = FALSE ) args_point <- list( mapping = aes(x = .data$m, y = .data$parameter), data = data, size = point_size, shape = 21 ) if (color_by_rhat) { args_inner$mapping <- args_inner$mapping %>% modify_aes(color = .data$rhat_rating) args_point$mapping <- args_point$mapping %>% modify_aes(color = .data$rhat_rating, fill = .data$rhat_rating) } else { args_inner$color <- get_color("dark") args_point$color <- get_color("dark_highlight") args_point$fill <- get_color("light") } point_func <- if (no_point_est) geom_ignore else geom_point layer_outer <- do.call(geom_segment, args_outer) layer_inner <- do.call(geom_segment, args_inner) layer_point <- do.call(point_func, args_point) # Do something or add an invisible layer if (color_by_rhat) { scale_color <- scale_color_diagnostic("rhat") scale_fill <- scale_fill_diagnostic("rhat") } else { scale_color <- geom_ignore() scale_fill <- geom_ignore() } ggplot(data) + layer_vertical_line + layer_outer + layer_inner + layer_point + scale_color + scale_fill + scale_y_discrete(limits = unique(rev(data$parameter))) + xlim(x_lim) + bayesplot_theme_get() + legend_move(ifelse(color_by_rhat, "top", "none")) + yaxis_text(face = "bold") + yaxis_title(FALSE) + yaxis_ticks(linewidth = 1) + xaxis_title(FALSE) } #' @rdname MCMC-intervals #' @export #' @param border_size For `mcmc_areas()` and `mcmc_areas_ridges()`, the size of #' the ridgelines. mcmc_areas <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., area_method = c("equal area", "equal height", "scaled height"), prob = 0.5, prob_outer = 1, point_est = c("median", "mean", "none"), rhat = numeric(), border_size = NULL, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL) { check_ignored_arguments(...) area_method <- match.arg(area_method) data <- mcmc_areas_data( x, pars, regex_pars, transformations, prob = prob, prob_outer = prob_outer, point_est = point_est, rhat = rhat, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens ) datas <- split(data, data$interval) # Use a dummy empty dataframe if no point estimate no_point_est <- !rlang::has_name(datas, "point") datas$point <- if (no_point_est) { dplyr::filter(datas$inner, FALSE) } else { datas$point } color_by_rhat <- rlang::has_name(data, "rhat_rating") # faint vertical line at zero if zero is within x_lim x_lim <- range(datas$outer$x) x_range <- diff(x_lim) x_lim[1] <- x_lim[1] - 0.05 * x_range x_lim[2] <- x_lim[2] + 0.05 * x_range layer_vertical_line <- if (0 > x_lim[1] && 0 < x_lim[2]) { vline_0(color = "gray90", linewidth = 0.5) } else { geom_ignore() } # Need to include rhat rating as a grouping variable if coloring by rhat so # that datas$bottom has an rhat_rating column that can map to color aesthetic groups <- if (color_by_rhat) { rlang::syms(c("parameter", "rhat_rating")) } else { rlang::syms(c("parameter")) } datas$bottom <- datas$outer %>% group_by(!!! groups) %>% summarise( ll = min(.data$x), hh = max(.data$x), .groups = "drop_last" ) %>% ungroup() args_bottom <- list( mapping = aes(x = .data$ll, xend = .data$hh, yend = .data$parameter), data = datas$bottom ) args_inner <- list( mapping = aes(scale = 0.9), data = datas$inner ) args_point <- list( mapping = aes(scale = 0.9), data = datas$point, color = NA ) args_outer <- list( mapping = aes(scale = 0.9), fill = NA ) if (area_method == "equal height") { args_inner$mapping <- modify_aes(args_inner$mapping, height = .data$scaled_density) args_point$mapping <- modify_aes(args_point$mapping, height = .data$scaled_density) args_outer$mapping <- modify_aes(args_outer$mapping, height = .data$scaled_density) } else if (area_method == "scaled height") { args_inner$mapping <- modify_aes(args_inner$mapping, height = .data$scaled_density * sqrt(.data$scaled_density)) args_point$mapping <- modify_aes(args_point$mapping, height = .data$scaled_density * sqrt(.data$scaled_density)) args_outer$mapping <- modify_aes(args_outer$mapping, height = .data$scaled_density * sqrt(.data$scaled_density)) } else { args_inner$mapping <- modify_aes(args_inner$mapping, height = .data$plotting_density) args_point$mapping <- modify_aes(args_point$mapping, height = .data$plotting_density) args_outer$mapping <- modify_aes(args_outer$mapping, height = .data$plotting_density) } if (!is.null(border_size)) { args_bottom$linewidth <- border_size args_outer$linewidth <- border_size args_inner$linewidth <- border_size } if (color_by_rhat) { args_bottom$mapping <- args_bottom$mapping %>% modify_aes(color = .data$rhat_rating) args_inner$mapping <- args_inner$mapping %>% modify_aes(color = .data$rhat_rating, fill = .data$rhat_rating) args_outer$mapping <- args_outer$mapping %>% modify_aes(color = .data$rhat_rating) # rhat fill color scale uses light/mid/dark colors. The point estimate needs # to be drawn with highlighted color scale, so we manually set the color for # the rhat fills. dc <- diagnostic_colors("rhat", "color")[["values"]] args_point$fill <- dc[datas$point$rhat_rating] } else { args_bottom$color <- get_color("dark") args_inner$color <- get_color("dark") args_inner$fill <- get_color("light") args_point$fill <- get_color("mid_highlight") args_outer$color <- get_color("dark") } # An invisible layer that is 2.5% taller than the plotted one args_outer2 <- args_outer args_outer2$mapping <- args_outer2$mapping %>% modify_aes(scale = .925) args_outer2$color <- NA layer_bottom <- do.call(geom_segment, args_bottom) layer_inner <- do.call(ggridges::geom_ridgeline, args_inner) layer_outer <- do.call(ggridges::geom_ridgeline, args_outer) layer_outer2 <- do.call(ggridges::geom_ridgeline, args_outer2) point_geom <- if (no_point_est) { geom_ignore } else { ggridges::geom_ridgeline } layer_point <- do.call(point_geom, args_point) # Do something or add an invisible layer if (color_by_rhat) { scale_color <- scale_color_diagnostic("rhat") scale_fill <- scale_fill_diagnostic("rhat") } else { scale_color <- geom_ignore() scale_fill <- geom_ignore() } ggplot(datas$outer) + aes(x = .data$x, y = .data$parameter) + layer_vertical_line + layer_inner + layer_point + layer_outer + layer_outer2 + layer_bottom + scale_color + scale_fill + scale_y_discrete( limits = unique(rev(data$parameter)), expand = expansion( add = c(0, 0.5 + 1/(2 * nlevels(data$parameter))), mult = c(0.05, 1/(2 * nlevels(data$parameter))) ) ) + xlim(x_lim) + bayesplot_theme_get() + legend_move(ifelse(color_by_rhat, "top", "none")) + yaxis_text(face = "bold") + yaxis_title(FALSE) + yaxis_ticks(linewidth = 1) + xaxis_title(FALSE) } #' @rdname MCMC-intervals #' @export mcmc_areas_ridges <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., prob_outer = 1, prob = 1, border_size = NULL, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL) { check_ignored_arguments(...) data <- mcmc_areas_ridges_data(x, pars = pars, regex_pars = regex_pars, transformations = transformations, prob = prob, prob_outer = prob_outer, bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens) datas <- data %>% split(data$interval) # faint vertical line at zero if zero is within x_lim x_lim <- range(datas$outer$x) x_range <- diff(x_lim) x_lim[1] <- x_lim[1] - 0.05 * x_range x_lim[2] <- x_lim[2] + 0.05 * x_range layer_vertical_line <- if (0 > x_lim[1] && 0 < x_lim[2]) { vline_0(color = "gray90", linewidth = 0.5) } else { geom_ignore() } args_outer <- list( mapping = aes(height = .data$density), color = get_color("dark"), fill = NA, stat = "identity" ) if (!is.null(border_size)) { args_outer$linewidth <- border_size } layer_outer <- do.call(ggridges::geom_density_ridges, args_outer) # Force ggridges to compute the scaling now test_plot <- ggplot(datas$outer) + aes(x = .data$x, y = .data$parameter) + layer_outer soft_build <- ggplot_build(test_plot) scaler1 <- unique(soft_build$data[[1]][["scale"]]) scaler2 <- unique(soft_build$data[[1]][["iscale"]]) scale <- scaler1 * scaler2 # Draw each ridgeline from top the bottom layer_list_inner <- list() par_draw_order <- levels(unique(data$parameter)) bg <- bayesplot_theme_get()[["panel.background"]][["fill"]] %||% "white" for (par_num in seq_along(unique(data$parameter))) { # Basically, draw the current ridgeline normally, but draw all the ones # under it (which would overlap it vertically) with a blank fill this_par <- par_draw_order[par_num] next_pars <- par_draw_order[par_num < seq_along(par_draw_order)] this_par_data <- datas$inner %>% dplyr::filter(.data$parameter == this_par) %>% mutate(color = get_color("dark"), fill = get_color("light")) next_par_data <- datas$outer %>% dplyr::filter(.data$parameter %in% next_pars) %>% mutate(color = get_color("dark"), fill = bg) args_inner <- list( mapping = aes(height = .data$density, color = .data$color, fill = .data$fill), data = dplyr::bind_rows(this_par_data, next_par_data), scale = scale, stat = "identity") if (!is.null(border_size)) { args_inner$linewidth <- border_size } layer_list_inner[[par_num]] <- do.call(ggridges::geom_ridgeline, args_inner) } ggplot(datas$outer) + aes(x = .data$x, y = .data$parameter) + layer_outer + scale_y_discrete(limits = unique(rev(data$parameter)), expand = expansion( add = c(0, 1.4 + 1/(2 * nlevels(data$parameter))), mult = c(0.05, 1/(2 * nlevels(data$parameter))) )) + layer_list_inner + layer_vertical_line + scale_fill_identity() + scale_color_identity() + xlim(x_lim) + yaxis_title(FALSE) + xaxis_title(FALSE) + bayesplot_theme_get() + grid_lines_y(color = "gray90") + theme(axis.text.y = element_text(hjust = 1, vjust = 0)) } #' @rdname MCMC-intervals #' @export mcmc_intervals_data <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., prob = 0.5, prob_outer = 0.9, point_est = c("median", "mean", "none"), rhat = numeric()) { check_ignored_arguments(...) probs <- check_interval_widths(prob, prob_outer) prob <- probs[1] prob_outer <- probs[2] x <- prepare_mcmc_array(x, pars, regex_pars, transformations) x <- merge_chains(x) data_long <- melt_mcmc(x) %>% dplyr::as_tibble() %>% rlang::set_names(tolower) probs <- c(0.5 - prob_outer / 2, 0.5 - prob / 2, 0.5 + prob / 2, 0.5 + prob_outer / 2) point_est <- match.arg(point_est) m_func <- if (point_est == "mean") mean else median data <- data_long %>% group_by(.data$parameter) %>% summarise( outer_width = prob_outer, inner_width = prob, point_est = point_est, ll = unname(quantile(.data$value, probs[1])), l = unname(quantile(.data$value, probs[2])), m = m_func(.data$value), h = unname(quantile(.data$value, probs[3])), hh = unname(quantile(.data$value, probs[4])) ) if (point_est == "none") { data$m <- NULL } color_by_rhat <- isTRUE(length(rhat) > 0) if (color_by_rhat) { rhat <- drop_NAs_and_warn(new_rhat(rhat)) if (length(rhat) != nrow(data)) { abort(paste( "'rhat' has length", length(rhat), "but 'x' has", nrow(data), "parameters." )) } rhat <- set_names(rhat, data$parameter) rhat_tbl <- rhat %>% mcmc_rhat_data() %>% select(one_of("parameter"), rhat_value = "value", rhat_rating = "rating", rhat_description = "description") %>% mutate(parameter = factor(.data$parameter, levels(data$parameter))) data <- dplyr::inner_join(data, rhat_tbl, by = "parameter") } data } # Don't import `filter`: otherwise, you get a warning when using # `devtools::load_all(".")` because stats also has a `filter` function #' @importFrom dplyr inner_join one_of top_n #' @rdname MCMC-intervals #' @export mcmc_areas_data <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., prob = 0.5, prob_outer = 1, point_est = c("median", "mean", "none"), rhat = numeric(), bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL) { probs <- check_interval_widths(prob, prob_outer) # First compute normal intervals so we know the width of the data, point # estimates, and have prepared rhat values. # Compute intervals with a median (for now) if no point estimate. It will be # cleaner to ignore results later than to have two branching code paths. point_est <- match.arg(point_est) temp_point_est <- if (point_est == "none") "median" else point_est intervals <- mcmc_intervals_data(x, pars, regex_pars, transformations, prob = probs[1], prob_outer = probs[2], point_est = temp_point_est, rhat = rhat) x <- prepare_mcmc_array(x, pars, regex_pars, transformations) x <- merge_chains(x) data_long <- melt_mcmc(x) %>% dplyr::as_tibble() %>% rlang::set_names(tolower) # Compute the density intervals data_inner <- data_long %>% compute_column_density( group_vars = "parameter", value_var = "value", interval_width = probs[1], bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens) %>% mutate(interval = "inner") data_outer <- data_long %>% compute_column_density( group_vars = "parameter", value_var = "value", interval_width = probs[2], bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens) %>% mutate(interval = "outer") # Point estimates will be intervals that take up .8% of the x-axis x_lim <- range(data_outer$x) x_range <- diff(x_lim) x_lim[1] <- x_lim[1] - 0.05 * x_range x_lim[2] <- x_lim[2] + 0.05 * x_range half_point_width <- .004 * diff(x_lim) # Find the density values closest to the point estimate point_ests <- intervals %>% select(one_of("parameter", "m")) point_centers <- data_inner %>% inner_join(point_ests, by = "parameter") %>% group_by(.data$parameter) %>% mutate(diff = abs(.data$m - .data$x)) %>% dplyr::top_n(1, -.data$diff) %>% select(one_of("parameter", "x", "m")) %>% rename(center = "x") %>% ungroup() # Keep density values that are within +/- .4% of x-axis of the point estimate points <- point_centers %>% left_join(data_inner, by = "parameter") %>% group_by(.data$parameter) %>% dplyr::filter(abs(.data$center - .data$x) <= half_point_width) %>% mutate( interval_width = 0, interval = "point" ) %>% select(-c("center"), "m") %>% ungroup() # Ignore points calculcation if no point estimate was requested if (point_est == "none") { points <- dplyr::filter(points, FALSE) } data <- dplyr::bind_rows(data_inner, data_outer, points) %>% select(one_of("parameter", "interval", "interval_width", "x", "density", "scaled_density")) %>% # Density scaled so the highest in entire dataframe has height 1 mutate(plotting_density = .data$density / max(.data$density)) if (rlang::has_name(intervals, "rhat_value")) { rhat_info <- intervals %>% select(one_of("parameter", "rhat_value", "rhat_rating", "rhat_description")) data <- inner_join(data, rhat_info, by = "parameter") } data } #' @rdname MCMC-intervals #' @export mcmc_areas_ridges_data <- function(x, pars = character(), regex_pars = character(), transformations = list(), ..., prob_outer = 1, prob = 1, bw = NULL, adjust = NULL, kernel = NULL, n_dens = NULL) { check_ignored_arguments(...) mcmc_areas_data(x, pars = pars, regex_pars = regex_pars, transformations = transformations, prob = prob, prob_outer = prob_outer, point_est = "none", bw = bw, adjust = adjust, kernel = kernel, n_dens = n_dens) } # internal ---------------------------------------------------------------- #' Compute density for a dataframe column. #' #' @param df a dataframe of posterior samples #' @param group_vars columns to group by. e.g., `c(Parameter, Chain)` #' @param value_var column containing posterior samples #' @param ... arguments passed onto density calculation #' @noRd compute_column_density <- function(df, group_vars, value_var, ...) { value_var <- enquo(value_var) group_vars <- enquos(group_vars) # Convert the vector of bare column names to a list of symbols group_cols <- df %>% dplyr::select(!!! group_vars) %>% names() %>% syms() # Tuck away the subgroups to compute densities on into nested dataframes sub_df <- dplyr::select(df, !!! group_cols, !! value_var) group_df <- df %>% dplyr::select(!!! group_cols, !! value_var) %>% group_by(!!! group_cols) by_group <- group_df %>% split(dplyr::group_indices(group_df)) %>% lapply(pull, !! value_var) nested <- df %>% dplyr::distinct(!!! group_cols) %>% mutate(data = by_group) nested$density <- lapply(nested$data, compute_interval_density, ...) nested$data <- NULL # Manually unnest the data reconstructed <- as.list(seq_len(nrow(nested))) for (df_i in seq_along(nested$density)) { row <- nested[df_i, ] parent <- row %>% select(-c("density")) groups <- rep(list(parent), nrow(row$density[[1]])) %>% dplyr::bind_rows() reconstructed[[df_i]] <- dplyr::bind_cols(groups, row$density[[1]]) } dplyr::bind_rows(reconstructed) } # Given a vector of values, compute a density dataframe. compute_interval_density <- function(x, interval_width = 1, n_dens = 1024, bw = NULL, adjust = NULL, kernel = NULL) { n_dens <- n_dens %||% 1024 tail_width <- (1 - interval_width) / 2 qs <- quantile(x, probs = c(tail_width, 1 - tail_width)) args <- c( # can't be null list(x = x, from = min(qs), to = max(qs), n = n_dens), # might be null bw = bw, adjust = adjust, kernel = kernel) dens <- do.call(stats::density, args) data.frame( interval_width = interval_width, x = dens$x, density = dens$y, scaled_density = dens$y / max(dens$y, na.rm = TRUE) ) } check_interval_widths <- function(prob, prob_outer) { if (!(is.numeric(prob) && is.numeric(prob_outer))) abort("`prob` and `prob_outer` must be numeric") if (prob < 0 || prob > 1 || prob_outer < 0 || prob_outer > 1) abort("`prob` and `prob_outer` must be in [0,1].") if (prob_outer < prob) { x <- sprintf( "`prob_outer` (%s) is less than `prob` (%s)\n... %s", prob_outer, prob, "Swapping the values of `prob_outer` and `prob`" ) warn(x) } sort(c(prob, prob_outer)) } bayesplot/R/helpers-ppc.R0000644000176200001440000004613014560773547015050 0ustar liggesusers# input validation and type checking ---------------------------------------- # Check if an object is a vector (but not list) or a 1-D array is_vector_or_1Darray <- function(x) { if (is.vector(x) && !is.list(x)) { return(TRUE) } isTRUE(is.array(x) && length(dim(x)) == 1) } # Check if x consists of whole numbers (very close to integers) # Implementation here follows example ?integer is_whole_number <- function(x, tol = .Machine$double.eps) { if (!is.numeric(x)) { FALSE } else { abs(x - round(x)) < tol } } # Check if all values in x are whole numbers or counts (non-negative whole # numbers) all_whole_number <- function(x, ...) { all(is_whole_number(x, ...)) } all_counts <- function(x, ...) { all_whole_number(x, ...) && min(x) >= 0 } #' Validate y #' #' Checks that `y` is numeric, doesn't have any NAs, and is either a vector, 1-D #' array, or univariate time series object of class `ts`. #' #' @param y The `y` object from the user. #' @return Either throws an error or returns a numeric vector. #' @noRd validate_y <- function(y) { stopifnot(is.numeric(y)) if (!(inherits(y, "ts") && is.null(dim(y)))) { if (!is_vector_or_1Darray(y)) { abort("'y' must be a vector or 1D array.") } y <- as.vector(y) } if (anyNA(y)) { abort("NAs not allowed in 'y'.") } unname(y) } #' Validate predictions (`yrep` or `ypred`) #' #' Checks that `predictions` is a numeric matrix, doesn't have any NAs, and has #' the correct number of columns. #' #' @param predictions The user's `yrep` or `ypred` object (SxN matrix). #' @param `n_obs` The number of observations (columns) that `predictions` should #' have, if applicable. #' @return Either throws an error or returns a numeric matrix. #' @noRd validate_predictions <- function(predictions, n_obs = NULL) { # sanity checks stopifnot(is.matrix(predictions), is.numeric(predictions)) if (!is.null(n_obs)) { stopifnot(length(n_obs) == 1, n_obs == as.integer(n_obs)) } if (is.integer(predictions)) { if (nrow(predictions) == 1) { predictions[1, ] <- as.numeric(predictions[1,, drop = FALSE]) } else { predictions <- apply(predictions, 2, as.numeric) } } if (anyNA(predictions)) { abort("NAs not allowed in predictions.") } if (!is.null(n_obs) && (ncol(predictions) != n_obs)) { abort("ncol(yrep) must be equal to length(y).") } # get rid of names but keep them as an attribute in case we want them obs_names <- colnames(predictions) predictions <- unclass(unname(predictions)) attr(predictions, "obs_names") <- obs_names predictions } #' Validate PIT #' #' Checks that `pit` is numeric, doesn't have any NAs, and is either a vector, #' or a 1-D array with values in [0,1]. #' #' @param pit The 'pit' object from the user. #' @return Either throws an error or returns a numeric vector. #' @noRd validate_pit <- function(pit) { if (anyNA(pit)) { abort("NAs not allowed in 'pit'.") } stopifnot(is.numeric(pit)) if (!is_vector_or_1Darray(pit)) { abort("'pit' must be a vector or 1D array.") } if (any(pit > 1) || any(pit < 0)) { abort("'pit' must only contain values between 0 and 1.") } unname(pit) } #' Validate group #' #' Checks that grouping variable has correct number of observations and is #' either a factor variable or vector (which is coerced to factor). #' #' @param group The user's `group` argument. #' @param n_obs The number of observations that `group` should contain (e.g., #' `length(y)`, `ncol(yrepd)`, etc.). Unlike for `validate_predictions()`, #' this is always required for `validate_group()`. #' @return Either throws an error or returns `group` (coerced to a factor). #' @noRd validate_group <- function(group, n_obs) { # sanity checks stopifnot(is.vector(group) || is.factor(group), length(n_obs) == 1, n_obs == as.integer(n_obs)) if (!is.factor(group)) { group <- as.factor(group) } if (anyNA(group)) { abort("NAs not allowed in 'group'.") } if (length(group) != n_obs) { abort("length(group) must be equal to the number of observations.") } unname(group) } #' Validate x #' #' Checks that x is a numeric vector, doesn't have any NAs, and has the #' same length as y. #' #' @param x,y The user's `x` vector and the `y` object returned by `validate_y()`. #' @param unique_x `TRUE` or `FALSE` indicating whether to require all unique #' values in `x`. #' @return Either throws an error or returns a numeric vector. #' @noRd validate_x <- function(x = NULL, y, unique_x = FALSE) { if (is.null(x)) { if (inherits(y, "ts") && is.null(dim(y))) { x <- stats::time(y) } else { x <- seq_along(y) } } stopifnot(is.numeric(x)) if (!is_vector_or_1Darray(x)) { abort("'x' must be a vector or 1D array.") } x <- as.vector(x) if (length(x) != length(y)) { abort("length(x) must be equal to length(y).") } if (anyNA(x)) { abort("NAs not allowed in 'x'.") } if (unique_x) { stopifnot(identical(length(x), length(unique(x)))) } unname(x) } # Internals for grouped plots --------------------------------------------- #' Modify a call to a `_grouped` function to a call to the ungrouped version #' @param fn The new function to call (a string). #' @param call The original call (from `match.call(expand.dots = FALSE)`). #' @return The new unevaluated call, with additional argument #' `called_from_internal=TRUE` which can be detected by the function to be #' called so it knows not to warn about the `group` and `facet_args` arguments. #' @noRd ungroup_call <- function(fn, call) { args <- rlang::call_args(call) args$called_from_internal <- TRUE args[["..."]] <- NULL rlang::call2(.fn = fn, !!!args, .ns = "bayesplot") } #' Check if the `...` to a plotting function was passed from it's `_grouped` version #' @param dots The `...` arguments already in a list, i.e., `list(...)`. #' @return `TRUE` or `FALSE` #' @noRd from_grouped <- function(dots) { isTRUE(dots[["called_from_internal"]]) && !is.null(dots[["group"]]) } # reshaping --------------------------------------------------- #' Convert matrix of predictions into a molten data frame #' #' @param predictions A matrix (`yrep` or `ypred`), already validated using #' `validate_predictions()`. #' @return A data frame with columns: #' * `y_id`: integer indicating the observation number (`predictions` column). #' * `rep_id`: integer indicating the simulation number (`predictions` row). #' * `rep_label`: factor with S levels, where S is `nrow(predictions)`, i.e. #' the number of simulations included in `predictions`. #' * `value`: the simulation values. #' @noRd melt_predictions <- function(predictions) { obs_names <- attr(predictions, "obs_names") out <- predictions %>% reshape2::melt(varnames = c("rep_id", "y_id")) %>% tibble::as_tibble() rep_labels <- create_rep_ids(out$rep_id) y_names <- obs_names[out$y_id] %||% out$y_id out$rep_label <- factor(rep_labels, levels = unique(rep_labels)) out$y_name <- factor(y_names, levels = unique(y_names)) out[c("y_id", "y_name", "rep_id", "rep_label", "value")] } #' Stack `y` below melted `yrep` data #' #' @param y Validated `y` input. #' @param yrep Validated `yrep` input. #' @return A data frame with the all the columns as the one returned by #' `melt_predictions()`, plus additional columns: #' * `is_y`: logical indicating whether the values are observations (`TRUE`) #' or simulations (`FALSE`). #' * `is_y_label`: factor with levels `italic(y)` for observations and #' `italic(y)[rep]` for simulations. #' @noRd melt_and_stack <- function(y, yrep) { y_text <- as.character(y_label()) yrep_text <- as.character(yrep_label()) molten_preds <- melt_predictions(yrep) # Add a level in the labels for the observed y values levels(molten_preds$rep_label) <- c(levels(molten_preds$rep_label), y_text) y_names <- attr(yrep, "obs_names") %||% seq_along(y) ydat <- tibble::tibble( rep_label = factor(y_text, levels = levels(molten_preds$rep_label)), rep_id = NA_integer_, y_id = seq_along(y), y_name = factor(y_names, levels = unique(y_names)), value = y) data <- dplyr::bind_rows(molten_preds, ydat) %>% mutate( rep_label = relevel(.data$rep_label, y_text), is_y = is.na(.data$rep_id), is_y_label = ifelse(.data$is_y, y_text, yrep_text) %>% factor(levels = c(y_text, yrep_text))) cols <- c("y_id", "y_name", "rep_id", "rep_label", "is_y", "is_y_label", "value") data[cols] } #' Obtain the coverage parameter for simultaneous confidence bands for ECDFs #' #' @param N Length of sample. #' @param L Number of chains. Used for MCMC, defaults to 1 for ppc. #' @param K Number of equally spaced evaluation points (1:K / K). Defaults to N. #' @param prob Desired simultaneous coverage (0,1). #' @param M number of simulations to run, if simulation method is used. #' @param interpolate_adj Boolean defining whether to interpolate the confidence #' bands from precomputed values. Interpolation provides a faster plot with the #' trade-off of possible loss of accuracy. #' @return The adjusted coverage parameter yielding the desired simultaneous #' coverage of the ECDF traces. #' @noRd adjust_gamma <- function(N, L = 1, K = N, prob = 0.99, M = 1000, interpolate_adj = FALSE) { if (! all_counts(c(K, N, L))) { abort("Parameters 'N', 'L' and 'K' must be positive integers.") } if (prob >= 1 || prob <= 0) { abort("Value of 'prob' must be in (0,1).") } if (is.null(interpolate_adj)) { if (K <= 200 || N < 100) { interpolate_adj <- FALSE } else { interpolate_adj <- TRUE } } if (interpolate_adj == TRUE) { gamma <- interpolate_gamma(N = N, K = K, prob = prob, L = L) } else if (L == 1) { gamma <- adjust_gamma_optimize(N = N, K = K, prob = prob) } else { gamma <- adjust_gamma_simulate(N = N, L = L, K = K, prob = prob, M = M) } gamma } #' Adjust coverage parameter for single sample using the optimization method. #' @param N Length of sample. #' @param K Number of equally spaced evaluation points (1:K / K). Defaults to N. #' @param prob Desired simultaneous coverage (0,1). #' @return The adjusted coverage parameter yielding the desired simultaneous #' coverage of the ECDF traces. #' @noRd adjust_gamma_optimize <- function(N, K, prob) { target <- function(gamma, prob, N, K) { z <- 1:(K - 1) / K z1 <- c(0, z) z2 <- c(z, 1) # pre-compute quantiles and use symmetry for increased efficiency. x2_lower <- qbinom(gamma / 2, N, z2) x2_upper <- c(N - rev(x2_lower)[2:K], 1) # Compute the total probability of trajectories inside the confidence # intervals. Initialize the set and corresponding probabilities known # to be 0 and 1 for the starting value z1 = 0. x1 <- 0 p_int <- 1 for (i in seq_along(z1)) { p_int <- p_interior( p_int = p_int, x1 = x1, x2 = x2_lower[i]: x2_upper[i], z1 = z1[i], z2 = z2[i], N = N ) x1 <- x2_lower[i]:x2_upper[i] } return(abs(prob - sum(p_int))) } optimize(target, c(0, 1 - prob), prob = prob, N = N, K = K)$minimum } #' Adjust coverage parameter for multiple chains using the simulation method. #' In short, 'M' simulations of 'L' standard uniform chains are run and the #' confidence bands are set to cover 100 * 'prob' % of these simulations. #' @param N Length of sample. #' @param L Number of chains. Used for MCMC, defaults to 1 for ppc. #' @param K Number of equally spaced evaluation points (1:K / K). Defaults to N. #' @param prob Desired simultaneous coverage (0,1). #' @param M number of simulations to run. #' @return The adjusted coverage parameter yielding the desired simultaneous #' coverage of the ECDF traces. #' @noRd adjust_gamma_simulate <- function(N, L, K, prob, M) { gamma <- numeric(M) z <- (1:(K - 1)) / K # Rank ECDF evaluation points. n <- N * (L - 1) k <- floor(z * N * L) for (m in seq_len(M)) { u <- u_scale(replicate(L, runif(N))) # Fractional ranks of sample chains scaled_ecdfs <- apply(outer(u, z, "<="), c(2, 3), sum) # Find the smalles marginal probability of the simulation run gamma[m] <- 2 * min( apply( scaled_ecdfs, 1, phyper, m = N, n = n, k = k ), apply( scaled_ecdfs - 1, 1, phyper, m = N, n = n, k = k, lower.tail = FALSE ) ) } alpha_quantile(gamma, 1 - prob) } #' Approximate the required adjustement to obtain simultaneous confidence bands #' of an ECDF plot with interpolation with regards to N and K from precomputed #' values for a fixed set of prob and L values. #' @param N Length of sample. #' @param L Number of chains. Used for MCMC, defaults to 1 for ppc. #' @param prob Desired simultaneous coverage (0,1). #' @param K Number of equally spaced evaluation points (1:K / K). Defaults to N. #' @return The approximated adjusted coverage parameter yielding the desired #' simultaneous coverage of the ECDF traces. #' @noRd interpolate_gamma <- function(N, K, prob, L) { # Find the precomputed values useful for the interpolation task. vals <- get_interpolation_values(N, K, L, prob) # Largest lower bound and smalles upper bound for N among precomputed values. N_lb <- max(vals[vals$N <= N, ]$N) N_ub <- min(vals[vals$N >= N, ]$N) # Approximate largest lower bound and smallest upper bound for gamma. log_gamma_lb <- approx( x = log(vals[vals$N == N_lb, ]$K), y = log(vals[vals$N == N_lb, ]$val), xout = log(K) )$y log_gamma_ub <- approx( x = log(vals[vals$N == N_ub, ]$K), y = log(vals[vals$N == N_ub, ]$val), xout = log(K) )$y if (N_ub == N_lb) { log_gamma_approx <- log_gamma_lb } else { # Approximate log_gamma for the desired value of N. log_gamma_approx <- approx( x = log(c(N_lb, N_ub)), y = c(log_gamma_lb, log_gamma_ub), xout = log(N) )$y } exp(log_gamma_approx) } #' Filter the precomputed values useful for the interpolation task given to #' interpolate_gamma. Check, if the task is possible with the availabel data. #' @param N Length of sample. #' @param K Number of equally spaced evaluation points (1:K / K). Defaults to N. #' @param L Number of chains. Used for MCMC, defaults to 1 for ppc. #' @param prob Desired simultaneous coverage (0,1). #' @return A data.frame containing the relevant precomputed values. #' @noRd get_interpolation_values <- function(N, K, L, prob) { for (dim in c("L", "prob")) { if (all(get(dim) != .gamma_adj[, dim])) { stop(paste( "No precomputed values to interpolate from for '", dim, "' = ", get(dim), ".\n", "Values of '", dim, "' available for interpolation: ", paste(unique(.gamma_adj[, dim]), collapse = ", "), ".", sep = "" )) } } vals <- .gamma_adj[.gamma_adj$L == L & .gamma_adj$prob == prob, ] if (N > max(vals$N)) { stop(paste( "No precomputed values to interpolate from for sample length of ", N, ".\n", "Please use a subsample of length ", max(vals$N), " or smaller, or consider setting 'interpolate_adj' = FALSE.", sep = "" )) } if (N < min(vals$N)) { stop(paste( "No precomputed values to interpolate from for sample length of ", N, ".\n", "Please use a subsample of length ", min(vals$N), " or larger, or consider setting 'interpolate_adj' = FALSE.", sep = "" )) } if (K > max(vals[vals$N <= N, ]$K)) { stop(paste( "No precomputed values available for interpolation for 'K' = ", K, ".\n", "Try either setting a value of 'K' <= ", max(vals[vals$N <= N, ]$K), "or 'interpolate_adj' = FALSE.", sep = "" )) } if (K < min(vals[vals$N <= N, ]$K)) { stop(paste( "No precomputed values available for interpolation for 'K' = ", K, ".\n", "Try either setting a value of 'K' >= ", min(vals[vals$N <= N, ]$K), " or 'interpolate_adj' = FALSE.", sep = "" )) } vals } #' A helper function for 'adjust_gamma_optimize' defining the probability that #' a scaled ECDF stays within the supplied bounds between two evaluation points. #' @param p_int For each value in x1, the probability that the ECDF has stayed #' within the bounds until z1 and takes the value in x1 at z1. #' @param x1 Vector of scaled ECDF values at the left end of the interval, z1. #' @param x2 Vector of scaled ECDF values at the right end of the interval, z2. #' @param z1 Left evaluation point in [0,1] #' @param z2 Right evaluation point in [0,1] with z2 > z1. #' @param N Total number of values in the sample. #' @return A vector containing the probability to transitioning from the values #' in x1 to each of the values in x2 weighted by the probabilities in p_int. #' @noRd p_interior <- function(p_int, x1, x2, z1, z2, N) { # Ratio between the length of the evaluation interval and the total length of # the interval left to cover by ECDF. z_tilde <- (z2 - z1) / (1 - z1) # Number of samples left to cover by ECDF. N_tilde <- rep(N - x1, each = length(x2)) p_int <- rep(p_int, each = length(x2)) x_diff <- outer(x2, x1, "-") # Pobability of each transition from a value in x1 to a value in x2. p_x2_int <- p_int * dbinom(x_diff, N_tilde, z_tilde) rowSums(p_x2_int) } #' A helper function for 'adjust_alpha_simulate' #' 100 * `alpha` percent of the trials in 'gamma' are allowed to be rejected. #' In case of ties, return the largest value dominating at most #' 100 * (alpha + tol) percent of the values. #' @noRd alpha_quantile <- function(gamma, alpha, tol = 0.001) { a <- unname(quantile(gamma, probs = alpha)) a_tol <- unname(quantile(gamma, probs = alpha + tol)) if (a == a_tol) { if (min(gamma) < a) { # take the largest value that doesn't exceed the tolerance. a <- max(gamma[gamma < a]) } } a } #' Compute simultaneous confidence intervals with the given adjusted coverage #' parameter gamma. #' @param gamma Adjusted coverage parameter for the marginal distribution #' (binomial for PIT values and hypergeometric for rank transformed chains). #' @param N Sample length. #' @param K Number of uniformly spaced evaluation points. #' @param L Number of MCMC-chains. (1 for ppc) #' @return A list with upper and lower confidence interval values at the #' evaluation points. #' @noRd ecdf_intervals <- function(gamma, N, K, L = 1) { lims <- list() z <- seq(0, 1, length.out = K + 1) if (L == 1) { lims$lower <- qbinom(gamma / 2, N, z) lims$upper <- qbinom(1 - gamma / 2, N, z) } else { n <- N * (L - 1) k <- floor(z * L * N) lims$lower <- qhyper(gamma / 2, N, n, k) lims$upper <- qhyper(1 - gamma / 2, N, n, k) } lims } #' Helper for 'adjust_gamma_simulate` #' Transforms observations in 'x' into their corresponding fractional ranks. #' @noRd u_scale <- function(x) { array(rank(x) / length(x), dim = dim(x), dimnames = dimnames(x)) } # labels ---------------------------------------------------------------- create_rep_ids <- function(ids) paste('italic(y)[rep] (', ids, ")") y_label <- function() expression(italic(y)) yrep_label <- function() expression(italic(y)[rep]) ypred_label <- function() expression(italic(y)[pred]) bayesplot/NEWS.md0000644000176200001440000004342114562715444013372 0ustar liggesusers# bayesplot 1.11.1 * Limit maximum number of evaluation points in `ppc_pit_ecdf` functions by default to 1000. by @TeemuSailynoja in #318 # bayesplot 1.11.0 * Update for new ggplot2 release by @teunbrand in #309 * Add `bins` argument to many histogram plots by @paul-buerkner in #300 * Follow ggplot2 updates on `facet_grid()` and `facet_wrap()` by @heavywatal in #305 * Better `ppc_loo_pit_qq` plots by @avehtari in #307 * Check `prob` is numeric for intervals plots by @tony-stone in #299 * Add `bins` and `breaks` arguments to more histogram and hex plots by @heavywatal in #313 * Replace `size` argument with `linewidth` for `geom_line` and `geom_ridgeline` by @heavywatal in #314 * All LOO plots now accept `psis_object` argument by @jgabry in #311 * `ppc_pit_ecdf()` and `ppc_pit_ecdf_grouped()` now support discrete variables, and their default method for selecting the number of ECDF evaluation points has been updated. by @TeemuSailynoja in #316 # bayesplot 1.10.0 * New function `mcmc_rank_ecdf()` for rank ecdf plots with confidence bands for assessing if two or more chains sample the same distribution (#282, @TeemuSailynoja) * New functions `ppc_pit_ecdf()`, `ppc_pit_ecdf_grouped()`, PIT ecdf plots with confidence bands to assess if `y` and `yrep` contain samples from the same distribution. (#282, @TeemuSailynoja) * Several `ppc` and `ppd` functions now accept the new `linewidth` argument introduced in ggplot2 3.4.0: `ppc_bars()`, `ppc_bars_grouped()`, `ppc_intervals()`, `ppc_intervals_grouped()`, `ppd_intervals()`, `ppd_intervals_grouped()`. * Fix bug in how `mcmc_pairs()` detected hitting `max_treedepth`, thanks to @dmphillippo. (#281) * Fix failing tests due to changes in ggplot2 3.4.0 (#289) # bayesplot 1.9.0 * New module PPD (posterior/prior predictive distribution) with a lot of new plotting functions with `ppd_` prefix. These functions plot draws from the prior or posterior predictive distributions (PPD) without comparing to observed data (i.e., no `y` argument). Because these are not "checks" against the observed data we use PPD instead of PPC. These plots are essentially the same as the corresponding PPC plots but without showing any observed data (e.g., `ppd_intervals()` is like `ppc_intervals()` but without plotting `y`). See `help("PPD-overview")` for details. (#151, #222) * All PPC categories now have one or more `_data()` functions that return the data frame used for plotting (#97, #222). Many of these have already been in previous releases, but the new ones in this release are: - `ppc_bars_data()` - `ppc_error_data()` - `ppc_error_binnned_data()` - `ppc_scatter_data()` - `ppc_scatter_avg_data()` - `ppc_stat_data()` * Many functions gain an argument `facet_args` for controlling ggplot2 faceting (many other functions have had this argument for a long time). The ones that just now got the argument are: - `ppc_scatter()` - `ppc_scatter_avg_grouped()` - `ppc_error_hist()` - `ppc_error_hist_grouped()` - `ppc_error_scatter()` - `ppc_error_binned()` * New plotting function `ppc_km_overlay_grouped()`, the grouped variant of `ppc_km_overlay()`. (#260, @fweber144) * `ppc_scatter()`, `ppc_scatter_avg()`, and `ppc_scatter_avg_grouped()` gain an argument `ref_line`, which can be set to `FALSE` to turn off the `x=y` line drawn behind the scatterplot. * `ppc_ribbon()` and `ppc_ribbon_grouped()` gain argument `y_draw` that specifies whether the observed y should be plotted using a point, line, or both. (#257, @charlesm93) * `mcmc_*()` functions now support all draws formats from the **posterior** package. (#277, @Ozan147) * `mcmc_dens()` and `mcmc_dens_overlay()` gain arguments for controlling the the density calculation. (#258) * `mcmc_hist()` and `mcmc_dens()` gain argument `alpha` for controlling transparency. (#244) * `mcmc_areas()` and `mcmc_areas_ridges()` gain an argument `border_size` for controlling the thickness of the ridgelines. (#224) # bayesplot 1.8.1 * Fix R cmd check error on linux for CRAN # bayesplot 1.8.0 ### Bug fixes * `mcmc_areas()` tries to use less vertical blank space. (#218, #230) * Fix bug in `color_scheme_view()` minimal theme (#213). * Fix error in `mcmc_acf()` for certain input types. (#244, #245, @hhau) ### New features * New plotting functions `ppc_dens_overlay_grouped()` and `ppc_ecdf_overlay_grouped()` for plotting density and cumulative distributions of the posterior predictive distribution (versus observed data) by group. (#212) * New plotting function `ppc_km_overlay()` for outcome variables that are right-censored. Empirical CCDF estimates of `yrep` are compared with the Kaplan-Meier estimate of `y`. (#233, #234, @fweber144) * `ppc_loo_pit_overlay()` now uses a boundary correction for an improved kernel density estimation. The new argument `boundary_correction` defaults to TRUE but can be set to FALSE to recover the old version of the plot. (#171, #235, @ecoronado92) * CmdStanMCMC objects (from CmdStanR) can now be used with extractor functions `nuts_params()`, `log_posterior()`, `rhat()`, and `neff_ratio()`. (#227) * On the y axis, `ppc_loo_pit_qq(..., compare = "normal")` now plots standard normal quantiles calculated from the PIT values (instead of the standardized PIT values). (#240, #243, @fweber144) * `mcmc_rank_overlay()` gains argument `facet_args`. (#221, @hhau) * For `mcmc_intervals()` the size` of the points and interval lines can be set with `mcmc_intervals(..., outer_size, inner_size, point_size)`. (#215, #228, #229) # bayesplot 1.7.2 Compatibility with dplyr 1.0.0 (#219) # bayesplot 1.7.1 Release requested by CRAN to fix errors at https://cran.r-project.org/web/checks/check_results_bayesplot.html due to matrices also inheriting from "array" in R 4.0. # bayesplot 1.7.0 (GitHub issue/PR numbers in parentheses) * The `pars` argument of all MCMC plotting functions now supports tidy variable selection. See `help("tidy-params", package="bayesplot")` for details and examples. (#161, #183, #188) * Two new plots have been added for inspecting the distribution of ranks. Rank histograms were introduced by the Stan team's [new paper on MCMC diagnostics](https://arxiv.org/abs/1903.08008). (#178, #179) `mcmc_rank_hist()`: A traditional traceplot (`mcmc_trace()`) visualizes how sampled values the MCMC chains mix over the course of sampling. A rank histogram (`mcmc_rank_hist()`) visualizes how the *ranks* of values from the chains mix together. An ideal plot would show the ranks mixing or overlapping in a uniform distribution. `mcmc_rank_overlay()`: Instead of drawing each chain's histogram in a separate panel, this plot draws the top edge of the chains' histograms in a single panel. * Added `mcmc_trace_data()`, which returns the data used for plotting the trace plots and rank histograms. (Advances #97) * [ColorBrewer](https://colorbrewer2.org/) palettes are now available as color schemes via [`color_scheme_set()`](https://mc-stan.org/bayesplot/reference/bayesplot-colors.html). For example, `color_scheme_set("brewer-Spectral")` will use the Spectral palette. (#177, #190) * MCMC plots now also accept objects with an `as.array` method as input (e.g., stanfit objects). (#175, #184) * [`mcmc_trace()`](https://mc-stan.org/bayesplot/reference/MCMC-traces.html) gains an argument `iter1` which can be used to label the traceplot starting from the first iteration after warmup. (#14, #155, @mcol) * [`mcmc_areas()`](https://mc-stan.org/bayesplot/reference/MCMC-intervals.html) gains an argument `area_method` which controls how to draw the density curves. The default `"equal area"` constrains the heights so that the curves have the same area. As a result, a narrow interval will appear as a spike of density, while a wide, uncertain interval is spread thin over the _x_ axis. Alternatively `"equal height"` will set the maximum height on each curve to the same value. This works well when the intervals are about the same width. Otherwise, that wide, uncertain interval will dominate the visual space compared to a narrow, less uncertain interval. A compromise between the two is `"scaled height"` which scales the curves from `"equal height"` using `height * sqrt(height)`. (#163, #169) * `mcmc_areas()` correctly plots density curves where the point estimate does not include the highest point of the density curve. (#168, #169, @jtimonen) * `mcmc_areas_ridges()` draws the vertical line at *x* = 0 over the curves so that it is always visible. * `mcmc_intervals()` and `mcmc_areas()` raise a warning if `prob_outer` is ever less than `prob`. It sorts these two values into the correct order. (#138) * MCMC parameter names are now *always* converted to factors prior to plotting. We use factors so that the order of parameters in a plot matches the order of the parameters in the original MCMC data. This change fixes a case where factor-conversion failed. (#162, #165, @wwiecek) * The examples in [`?ppc_loo_pit_overlay()`](https://mc-stan.org/bayesplot/reference/PPC-loo.html) now work as expected. (#166, #167) * Added `"viridisD"` as an alternative name for `"viridis"` to the supported colors. * Added `"viridisE"` (the [cividis](https://github.com/marcosci/cividis) version of viridis) to the supported colors. * `ppc_bars()` and `ppc_bars_grouped()` now allow negative integers as input. (#172, @jeffpollock9) # bayesplot 1.6.0 (GitHub issue/PR numbers in parentheses) * Loading **bayesplot** no longer overrides the ggplot theme! Rather, it sets a theme specific for **bayesplot**. Some packages using **bayesplot** may still override the default **ggplot** theme (e.g., **rstanarm** does but only until next release), but simply loading **bayesplot** itself will not. There are new functions for controlling the ggplot theme for **bayesplot** that work like their **ggplot2** counterparts but only affect plots made using **bayesplot**. Thanks to Malcolm Barrett. (#117, #149). - `bayesplot_theme_set()` - `bayesplot_theme_get()` - `bayesplot_theme_update()` - `bayesplot_theme_replace()` * The [Visual MCMC Diagnostics vignette](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) has been reorganized and has a lot of useful new content thanks to Martin Modrák. (#144, #153) * The [LOO predictive checks](https://mc-stan.org/bayesplot/reference/PPC-loo.html) now require **loo** version `>= 2.0.0`. (#139) * Histogram plots gain a `breaks` argument that can be used as an alternative to `binwidth`. (#148) * [`mcmc_pairs()`](https://mc-stan.org/bayesplot/reference/MCMC-scatterplots.html) now has an argument `grid_args` to provide a way of passing optional arguments to `gridExtra::arrangeGrob()`. This can be used to add a title to the plot, for example. (#143) * [`ppc_ecdf_overlay()`](https://mc-stan.org/bayesplot/reference/PPC-distributions.html) gains an argument `discrete`, which is `FALSE` by default, but can be used to make the Geom more appropriate for discrete data. (#145) * [PPC intervals plots](https://mc-stan.org/bayesplot/reference/PPC-intervals.html) and [LOO predictive checks](https://mc-stan.org/bayesplot/reference/PPC-loo.html) now draw both an outer and an inner probability interval, which can be controlled through the new argument `prob_outer` and the already existing `prob`. This is consistent with what is produced by `mcmc_intervals()`. (#152, #154, @mcol) # bayesplot 1.5.0 (GitHub issue/PR numbers in parentheses) * New package documentation website: * Two new plots that visualize posterior density using [ridgelines][ggridges]. These work well when parameters have similar values and similar densities, as in hierarchical models. (#104) - `mcmc_dens_chains()` draws the kernel density of each sampling chain. - `mcmc_areas_ridges()` draws the kernel density combined across chains. - Both functions have a `_data()` function to return the data plotted by each function. * `mcmc_intervals()` and `mcmc_areas()` have been rewritten. (#103) - They now use a discrete *y*-axis. Previously, they used a continuous scale with numeric breaks relabelled with parameter names; this design caused some unexpected behavior when customizing these plots. - `mcmc_areas()` now uses geoms from the ggridges package to draw density curves. * Added `mcmc_intervals_data()` and `mcmc_areas_data()` that return data plotted by `mcmc_intervals()` and `mcmc_areas()`. (Advances #97) * New `ppc_data()` function returns the data plotted by many of the PPC plotting functions. (Advances #97) * Added `ppc_loo_pit_overlay()` function for a better LOO PIT predictive check. (#123) * Started using **vdiffr** to add visual unit tests to the existing PPC unit tests. (#137) # bayesplot 1.4.0 (GitHub issue/PR numbers in parentheses) * New plotting function `mcmc_parcoord()` for parallel coordinates plots of MCMC draws (optionally including HMC/NUTS diagnostic information). (#108) * `mcmc_scatter` gains an `np` argument for specifying NUTS parameters, which allows highlighting divergences in the plot. (#112) * New functions with names ending with suffix `_data` don't make the plots, they just return the data prepared for plotting (more of these to come in future releases): - `ppc_intervals_data()` (#101) - `ppc_ribbon_data()` (#101) - `mcmc_parcoord_data()` (#108) - `mcmc_rhat_data()` (#110) - `mcmc_neff_data()` (#110) * `ppc_stat_grouped()`, `ppc_stat_freqpoly_grouped()` gain a `facet_args` argument for controlling **ggplot2** faceting (many of the `mcmc_` functions already have this). * The `divergences` argument to `mcmc_trace()` has been deprecated in favor of `np` (NUTS parameters) to match the other functions that have an `np` argument. * Fixed an issue where duplicated rhat values would break `mcmc_rhat()` (#105). # bayesplot 1.3.0 (GitHub issue/PR numbers in parentheses) * `bayesplot::theme_default()` is now set as the default ggplot2 plotting theme when **bayesplot** is loaded, which makes changing the default theme using `ggplot2::theme_set()` possible. Thanks to @gavinsimpson. (#87) * `mcmc_hist()` and `mcmc_hist_by_chain()` now take a `freq` argument that defaults to `TRUE` (behavior is like `freq` argument to R's `hist` function). * Using a `ts` object for `y` in PPC plots no longer results in an error. Thanks to @helske. (#94) * `mcmc_intervals()` doesn't use round lineends anymore as they slightly exaggerate the width of the intervals. Thanks to @tjmahr. (#96) # bayesplot 1.2.0 A lot of new stuff in this release. (GitHub issue/PR numbers in parentheses) ## Fixes * Avoid error in some cases when `divergences` is specified in call to `mcmc_trace()` but there are not actually any divergent transitions. * The `merge_chains` argument to `mcmc_nuts_energy()` now defaults to `FALSE`. ## New features in existing functions * For `mcmc_*()` functions, transformations are recycled if `transformations` argument is specified as a single function rather than a named list. Thanks to @tklebel. (#64) * For `ppc_violin_grouped()` there is now the option of showing `y` as a violin, points, or both. Thanks to @silberzwiebel. (#74) * `color_scheme_get()` now has an optional argument `i` for selecting only a subset of the colors. * New color schemes: darkgray, orange, viridis, viridisA, viridisB, viridisC. The viridis schemes are better than the other schemes for trace plots (the colors are very distinct from each other). ## New functions * `mcmc_pairs()`, which is essentially a ggplot2+grid implementation of rstan's `pairs.stanfit()` method. (#67) * `mcmc_hex()`, which is similar to `mcmc_scatter()` but using `geom_hex()` instead of `geom_point()`. This can be used to avoid overplotting. (#67) * `overlay_function()` convenience function. Example usage: add a Gaussian (or any distribution) density curve to a plot made with `mcmc_hist()`. * `mcmc_recover_scatter()` and `mcmc_recover_hist()`, which are similar to `mcmc_recover_intervals()` and compare estimates to "true" values used to simulate data. (#81, #83) * New PPC category **Discrete** with functions: - `ppc_rootogram()` for use with models for count data. Thanks to @paul-buerkner. (#28) - `ppc_bars()`, `ppc_bars_grouped()` for use with models for ordinal, categorical and multinomial data. Thanks to @silberzwiebel. (#73) * New PPC category **LOO** (thanks to suggestions from @avehtari) with functions: - `ppc_loo_pit()` for assessing the calibration of marginal predictions. (#72) - `ppc_loo_intervals()`, `ppc_loo_ribbon()` for plotting intervals of the LOO predictive distribution. (#72) # bayesplot 1.1.0 (GitHub issue/PR numbers in parentheses) ## Fixes * Images in vignettes should now render properly using `png` device. Thanks to TJ Mahr. (#51) * `xaxis_title(FALSE)` and `yaxis_title(FALSE)` now set axis titles to `NULL` rather than changing theme elements to `element_blank()`. This makes it easier to add axis titles to plots that don’t have them by default. Thanks to Bill Harris. (#53) ## New features in existing functions * Add argument `divergences` to `mcmc_trace()` function. For models fit using HMC/NUTS this can be used to display divergences as a rug at the bottom of the trace plot. (#42) * The `stat` argument for all `ppc_stat_*()` functions now accepts a function instead of only the name of a function. (#31) ## New functions * `ppc_error_hist_grouped()` for plotting predictive errors by level of a grouping variable. (#40) * `mcmc_recover_intervals)(` for comparing MCMC estimates to "true" parameter values used to simulate the data. (#56) * `bayesplot_grid()` for juxtaposing plots and enforcing shared axis limits. 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bayesplot/inst/0000755000176200001440000000000014563223213013232 5ustar liggesusersbayesplot/inst/doc/0000755000176200001440000000000014563223213013777 5ustar liggesusersbayesplot/inst/doc/plotting-mcmc-draws.Rmd0000644000176200001440000003043414556465326020361 0ustar liggesusers--- title: "Plotting MCMC draws using the bayesplot package" author: "Jonah Gabry" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") vignette: > %\VignetteIndexEntry{Plotting MCMC draws} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, child="children/SETTINGS-knitr.txt"} ``` ```{r pkgs, include=FALSE} library("ggplot2") library("rstanarm") ``` ## Introduction This vignette focuses on plotting parameter estimates from MCMC draws. MCMC diagnostic plots are covered in the separate vignette [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html), and graphical posterior predictive model checking is covered in the vignette [_Graphical posterior predictive checks_](https://mc-stan.org/bayesplot/articles/graphical-ppcs.html). ### Setup In addition to __bayesplot__ we'll load the following packages: * __ggplot2__, in case we want to customize the ggplot objects created by __bayesplot__ * __rstanarm__, for fitting the example models used throughout the vignette ```{r, eval=FALSE} library("bayesplot") library("ggplot2") library("rstanarm") ``` ### Example model The **bayesplot** package provides various plotting functions for visualizing Markov chain Monte Carlo (MCMC) draws from the posterior distribution of the parameters of a Bayesian model. In this vignette we demonstrate a few of these functions. Example usage of the functions not demonstrated here can be found in the package documentation. For demonstration we will use draws obtained using the `stan_glm` function in the **rstanarm** package (Gabry and Goodrich, 2017), but MCMC draws from using any package can be used with the functions in the **bayesplot** package. See, for example, **brms**, which, like **rstanarm**, calls the **rstan** package internally to use [Stan](https://mc-stan.org/)'s MCMC sampler. ```{r mtcars} head(mtcars) # see help("mtcars") ``` ```{r, eval=FALSE} # linear regression model using stan_glm # using '~ .' to include all variables fit <- stan_glm(mpg ~ ., data = mtcars, seed = 1111) print(fit) ``` ```{r stan_glm, include=FALSE} fit <- stan_glm(mpg ~ ., data = mtcars, QR = TRUE, seed = 1111) ``` ```{r print-fit, echo=FALSE} print(fit) ``` To use the posterior draws with the functions in the **bayesplot** package we'll extract them from the fitted model object: ```{r get-draws} posterior <- as.array(fit) dim(posterior) dimnames(posterior) ``` We've used `as.array` above (as opposed to `as.matrix`) because it keeps the Markov chains separate (`stan_glm` runs four chains by default). Most of the plots don't actually need the chains to be separate, but for a few of the plots we make in this vignette we'll want to show the chains individually.
    ## Posterior uncertainty intervals For models fit using MCMC we can compute posterior uncertainty intervals (sometimes called "credible intervals") in various ways. **bayesplot** currently provides plots of central intervals based on quantiles, although additional options may be provided in future releases (e.g., HDIs, which can be useful in particular cases). **Documentation:** * `help("MCMC-intervals")` * [mc-stan.org/bayesplot/reference/MCMC-intervals](https://mc-stan.org/bayesplot/reference/MCMC-intervals.html) ------ #### mcmc_intervals, mcmc_areas Central posterior uncertainty intervals can be plotted using the `mcmc_intervals` function. ```{r mcmc_intervals} color_scheme_set("red") mcmc_intervals(posterior, pars = c("cyl", "drat", "am", "sigma")) ``` The default is to show 50% intervals (the thick segments) and 90% intervals (the thinner outer lines). These defaults can be changed using the `prob` and `prob_outer` arguments, respectively. The points in the above plot are posterior medians. The `point_est` argument can be used to select posterior means instead or to omit the point estimates. To show the uncertainty intervals as shaded areas under the estimated posterior density curves we can use the `mcmc_areas` function. ```{r mcmc_areas} mcmc_areas( posterior, pars = c("cyl", "drat", "am", "sigma"), prob = 0.8, # 80% intervals prob_outer = 0.99, # 99% point_est = "mean" ) ```
    ## Univariate marginal posterior distributions **bayesplot** provides functions for looking at histograms or kernel density estimates of marginal posterior distributions, either with all Markov chains combined or with the chains separate. **Documentation:** * `help("MCMC-distributions")` * [mc-stan.org/bayesplot/reference/MCMC-distributions](https://mc-stan.org/bayesplot/reference/MCMC-distributions.html) ------ #### mcmc_hist The `mcmc_hist` function plots marginal posterior distributions (combining all chains): ```{r mcmc_hist, message=FALSE} color_scheme_set("green") mcmc_hist(posterior, pars = c("wt", "sigma")) ``` If we want to plot `log(sigma)` rather than `sigma` we can either transform the draws in advance or use the `transformations` argument. ```{r mcmc_hist-transform, message=FALSE} color_scheme_set("blue") mcmc_hist(posterior, pars = c("wt", "sigma"), transformations = list("sigma" = "log")) ``` Most of the other functions for plotting MCMC draws also have a `transformations` argument. #### mcmc_hist_by_chain To view separate histograms of each of the four Markov chains we can use `mcmc_hist_by_chain`, which plots each chain in a separate facet in the plot. ```{r mcmc_hist_by_chain, message=FALSE} color_scheme_set("brightblue") mcmc_hist_by_chain(posterior, pars = c("wt", "sigma")) ``` #### mcmc_dens The `mcmc_dens` function is similar to `mcmc_hist` but plots kernel density estimates instead of histograms. ```{r mcmc_dens, message=FALSE} color_scheme_set("purple") mcmc_dens(posterior, pars = c("wt", "sigma")) ``` #### mcmc_dens_overlay Like `mcmc_hist_by_chain`, the `mcmc_dens_overlay` function separates the Markov chains. But instead of plotting each chain individually, the density estimates are overlaid. ```{r mcmc_dens_overlay, message=FALSE} mcmc_dens_overlay(posterior, pars = c("wt", "sigma")) ``` #### mcmc_violin The `mcmc_violin` function plots the density estimates of each chain as violins and draws horizontal line segments at user-specified quantiles. ```{r mcmc_violin} color_scheme_set("teal") mcmc_violin(posterior, pars = c("wt", "sigma"), probs = c(0.1, 0.5, 0.9)) ```
    ## Bivariate plots Various functions are available for plotting bivariate marginal posterior distributions. Some of these functions also take optional arguments for adding MCMC diagnostic information to the plots. That additional functionality is discussed in the separate [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette. **Documentation:** * `help("MCMC-scatterplots")` * [mc-stan.org/bayesplot/reference/MCMC-scatterplots](https://mc-stan.org/bayesplot/reference/MCMC-scatterplots.html) ------ #### mcmc_scatter The `mcmc_scatter` function creates a simple scatterplot of two parameters. ```{r mcmc_scatter} color_scheme_set("gray") mcmc_scatter(posterior, pars = c("(Intercept)", "wt"), size = 1.5, alpha = 0.5) ``` #### mcmc_hex The `mcmc_hex` function creates a similar plot but using hexagonal binning, which can be useful to avoid overplotting. ```{r mcmc_hex} # requires hexbin package if (requireNamespace("hexbin", quietly = TRUE)) { mcmc_hex(posterior, pars = c("(Intercept)", "wt")) } ``` #### mcmc_pairs In addition to `mcmc_scatter` and `mcmc_hex`, __bayesplot__ now provides an `mcmc_pairs` function for creating pairs plots with more than two parameters. ```{r mcmc_pairs, message=FALSE} color_scheme_set("pink") mcmc_pairs(posterior, pars = c("(Intercept)", "wt", "sigma"), off_diag_args = list(size = 1.5)) ``` The univariate marginal posteriors are shown along the diagonal as histograms, but this can be changed to densities by setting `diag_fun="dens"`. Bivariate plots are displayed above and below the diagonal as scatterplots, but it is also possible to use hex plots by setting `off_diag_fun="hex"`. By default, `mcmc_pairs` shows some of the Markov chains (half, if an even number of chains) above the diagonal and the others below. There are many more options for controlling how the draws should be split between the plots above and below the diagonal (see the documentation for the `condition` argument), but they are more useful when MCMC diagnostic information is included. This is discussed in the [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette.
    ## Trace plots Trace plots are time series plots of Markov chains. In this vignette we show the standard trace plots that **bayesplot** can make. For models fit using any Stan interface (or Hamiltonian Monte Carlo in general), the [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette provides an example of also adding information about divergences to trace plots. **Documentation:** * `help("MCMC-traces")` * [mc-stan.org/bayesplot/reference/MCMC-traces](https://mc-stan.org/bayesplot/reference/MCMC-traces.html) ------- #### mcmc_trace The `mcmc_trace` function creates standard trace plots: ```{r mcmc_trace} color_scheme_set("blue") mcmc_trace(posterior, pars = c("wt", "sigma")) ``` If it's hard to see the difference between the chains we can change to a mixed color scheme, for example: ```{r change-scheme} color_scheme_set("mix-blue-red") mcmc_trace(posterior, pars = c("wt", "sigma"), facet_args = list(ncol = 1, strip.position = "left")) ``` The code above also illustrates the use of the `facet_args` argument, which is a list of parameters passed to `facet_wrap` in __ggplot2__. Specifying `ncol=1` means the trace plots will be stacked in a single column rather than placed side by side, and `strip.position="left"` moves the facet labels to the y-axis (instead of above each facet). The [`"viridis"` color scheme](https://CRAN.R-project.org/package=viridis) is also useful for trace plots because it is comprised of very distinct colors: ```{r viridis-scheme, eval=FALSE} color_scheme_set("viridis") mcmc_trace(posterior, pars = "(Intercept)") ``` #### mcmc_trace_highlight The `mcmc_trace_highlight` function uses points instead of lines and reduces the opacity of all but a single chain (which is specified using the `highlight` argument). ```{r mcmc_trace_highlight} mcmc_trace_highlight(posterior, pars = "sigma", highlight = 3) ```
    ## References Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.15.3. https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. \doi:10.1111/rssa.12378. ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [arXiv preprint](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper))     Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third edition. Stan Development Team. (2017). *Stan Modeling Language Users Guide and Reference Manual*. https://mc-stan.org/users/documentation/ bayesplot/inst/doc/graphical-ppcs.Rmd0000644000176200001440000003634314335264530017355 0ustar liggesusers--- title: "Graphical posterior predictive checks using the bayesplot package" author: "Jonah Gabry" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") vignette: > %\VignetteIndexEntry{Graphical posterior predictive checks} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, child="children/SETTINGS-knitr.txt"} ``` ```{r pkgs, include=FALSE} library("ggplot2") library("rstanarm") set.seed(840) ``` ## Introduction This vignette focuses on graphical posterior predictive checks (PPC). Plots of parameter estimates from MCMC draws are covered in the separate vignette [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html), and MCMC diagnostics are covered in the [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html) vignette. ### Graphical posterior predictive checks (PPCs) The **bayesplot** package provides various plotting functions for _graphical posterior predictive checking_, that is, creating graphical displays comparing observed data to simulated data from the posterior predictive distribution ([Gabry et al, 2019](#gabry2019)). The idea behind posterior predictive checking is simple: if a model is a good fit then we should be able to use it to generate data that looks a lot like the data we observed. To generate the data used for posterior predictive checks (PPCs) we simulate from the _posterior predictive distribution_. This is the distribution of the outcome variable implied by a model after using the observed data $y$ (a vector of $N$ outcome values) to update our beliefs about unknown model parameters $\theta$. The posterior predictive distribution for observation $\widetilde{y}$ can be written as $$p(\widetilde{y} \,|\, y) = \int p(\widetilde{y} \,|\, \theta) \, p(\theta \,|\, y) \, d\theta.$$ Typically we will also condition on $X$ (a matrix of predictor variables). For each draw (simulation) $s = 1, \ldots, S$ of the parameters from the posterior distribution, $\theta^{(s)} \sim p(\theta \,|\, y)$, we draw an entire vector of $N$ outcomes $\widetilde{y}^{(s)}$ from the posterior predictive distribution by simulating from the data model conditional on parameters $\theta^{(s)}$. The result is an $S \times N$ matrix of draws $\widetilde{y}$. When simulating from the posterior predictive distribution we can use either the same values of the predictors $X$ that we used when fitting the model or new observations of those predictors. When we use the same values of $X$ we denote the resulting simulations by $y^{rep}$, as they can be thought of as replications of the outcome $y$ rather than predictions for future observations ($\widetilde{y}$ using predictors $\widetilde{X}$). This corresponds to the notation from Gelman et al. (2013) and is the notation used throughout the package documentation. Using the replicated datasets drawn from the posterior predictive distribution, the functions in the **bayesplot** package create various graphical displays comparing the observed data $y$ to the replications. The names of the **bayesplot** plotting functions for posterior predictive checking all have the prefix `ppc_`. ### Setup In addition to **bayesplot** we'll load the following packages: * __ggplot2__, in case we want to customize the ggplot objects created by __bayesplot__ * __rstanarm__, for fitting the example models used throughout the vignette ```{r, eval=FALSE} library("bayesplot") library("ggplot2") library("rstanarm") ``` ### Example models To demonstrate some of the various PPCs that can be created with the **bayesplot** package we'll use an example of comparing Poisson and Negative binomial regression models from one of the **rstanarm** [package vignettes](https://mc-stan.org/rstanarm/articles/count.html) (Gabry and Goodrich, 2017). > We want to make inferences about the efficacy of a certain pest management system at reducing the number of roaches in urban apartments. [...] The regression predictors for the model are the pre-treatment number of roaches `roach1`, the treatment indicator `treatment`, and a variable `senior` indicating whether the apartment is in a building restricted to elderly residents. Because the number of days for which the roach traps were used is not the same for all apartments in the sample, we include it as an exposure [...]. First we fit a Poisson regression model with outcome variable `y` representing the roach count in each apartment at the end of the experiment. ```{r roaches-data} head(roaches) # see help("rstanarm-datasets") roaches$roach100 <- roaches$roach1 / 100 # pre-treatment number of roaches (in 100s) ``` ```{r roaches-model-pois, message=FALSE} # using rstanarm's default priors. For details see the section on default # weakly informative priors at https://mc-stan.org/rstanarm/articles/priors.html fit_poisson <- stan_glm( y ~ roach100 + treatment + senior, offset = log(exposure2), family = poisson(link = "log"), data = roaches, seed = 1111, refresh = 0 # suppresses all output as of v2.18.1 of rstan ) ``` ```{r print-pois} print(fit_poisson) ``` We'll also fit the negative binomial model that we'll compare to the Poisson: ```{r roaches-model-nb, message=FALSE} fit_nb <- update(fit_poisson, family = "neg_binomial_2") ``` ```{r print-nb} print(fit_nb) ``` ### Defining `y` and `yrep` In order to use the PPC functions from the **bayesplot** package we need a vector `y` of outcome values, ```{r y} y <- roaches$y ``` and a matrix `yrep` of draws from the posterior predictive distribution, ```{r yrep} yrep_poisson <- posterior_predict(fit_poisson, draws = 500) yrep_nb <- posterior_predict(fit_nb, draws = 500) dim(yrep_poisson) dim(yrep_nb) ``` Each row of the matrix is a draw from the posterior predictive distribution, i.e. a vector with one element for each of the data points in `y`. Since we fit the models using __rstanarm__ we used its special `posterior_predict` function, but if we were using a model fit with the __rstan__ package we could create `yrep` in the `generated quantities` block of the Stan program or by doing simulations in R after fitting the model. Draws from the posterior predictive distribution can be used with **bayesplot** regardless of whether or not the model was fit using an interface to Stan. **bayesplot** just requires a `yrep` matrix that has `number_of_draws` rows and `number_of_observations` columns.
    ## Histograms and density estimates #### ppc_dens_overlay The first PPC we'll look at is a comparison of the distribution of `y` and the distributions of some of the simulated datasets (rows) in the `yrep` matrix. ```{r ppc_dens_overlay} color_scheme_set("brightblue") ppc_dens_overlay(y, yrep_poisson[1:50, ]) ``` In the plot above, the dark line is the distribution of the observed outcomes `y` and each of the 50 lighter lines is the kernel density estimate of one of the replications of `y` from the posterior predictive distribution (i.e., one of the rows in `yrep`). This plot makes it easy to see that this model fails to account for the large proportion of zeros in `y`. That is, the model predicts fewer zeros than were actually observed. To see the discrepancy at the lower values of more clearly we can use the `xlim` function from **ggplot2** to restrict the range of the x-axis: ```{r ppc_dens_overlay-2, message=FALSE, warning=FALSE} ppc_dens_overlay(y, yrep_poisson[1:50, ]) + xlim(0, 150) ``` See Figure 6 in [Gabry et al. (2019)](#gabry2019) for another example of using `ppc_dens_overlay`. #### ppc_hist We could see the same thing from a different perspective by looking at separate histograms of `y` and some of the `yrep` datasets using the `ppc_hist` function: ```{r ppc_hist, message=FALSE} ppc_hist(y, yrep_poisson[1:5, ]) ``` The same plot for the negative binomial model looks much different: ```{r ppc_hist-nb, message=FALSE} ppc_hist(y, yrep_nb[1:5, ]) ``` The negative binomial model does better handling the number of zeros in the data, but it occasionally predicts values that are way too large, which is why the x-axes extend to such high values in the plot and make it difficult to read. To see the predictions for the smaller values more clearly we can zoom in: ```{r ppc_hist-nb-2, message=FALSE} ppc_hist(y, yrep_nb[1:5, ], binwidth = 20) + coord_cartesian(xlim = c(-1, 300)) ```
    ## Distributions of test statistics Another way to see that the Poisson model predicts too few zeros is to look at the distribution of the proportion of zeros over the replicated datasets from the posterior predictive distribution in `yrep` and compare to the proportion of observed zeros in `y`. #### ppc_stat First we define a function that takes a vector as input and returns the proportion of zeros: ```{r prop_zero} prop_zero <- function(x) mean(x == 0) prop_zero(y) # check proportion of zeros in y ``` The `stat` argument to `ppc_stat` accepts a function or the name of a function for computing a test statistic from a vector of data. In our case we can specify `stat = "prop_zero"` since we've already defined the `prop_zero` function, but we also could have used `stat = function(x) mean(x == 0)`. ```{r ppc_stat, message=FALSE} ppc_stat(y, yrep_poisson, stat = "prop_zero", binwidth = 0.005) ``` The dark line is at the value $T(y)$, i.e. the value of the test statistic computed from the observed $y$, in this case `prop_zero(y)`. The lighter area on the left is actually a histogram of the proportion of zeros in in the `yrep` simulations, but it can be hard to see because almost none of the simulated datasets in `yrep` have any zeros. Here's the same plot for the negative binomial model: ```{r ppc_stat-nb, message=FALSE} ppc_stat(y, yrep_nb, stat = "prop_zero") ``` Again we see that the negative binomial model does a much better job predicting the proportion of observed zeros than the Poisson. However, if we look instead at the distribution of the maximum value in the replications, we can see that the Poisson model makes more realistic predictions than the negative binomial: ```{r ppc_stat-max, message=FALSE} ppc_stat(y, yrep_poisson, stat = "max") ppc_stat(y, yrep_nb, stat = "max") ppc_stat(y, yrep_nb, stat = "max", binwidth = 100) + coord_cartesian(xlim = c(-1, 5000)) ``` See Figure 7 in [Gabry et al. (2019)](#gabry2019) for another example of using `ppc_stat`.
    ## Other PPCs and PPCs by group There are many additional PPCs available, including plots of predictive intervals, distributions of predictive errors, and more. For links to the documentation for all of the various PPC plots see `help("PPC-overview")` from R or the [online documentation](https://mc-stan.org/bayesplot/reference/index.html#section-ppc) on the Stan website. The `available_ppc` function can also be used to list the names of all PPC plotting functions: ```{r available_ppc} available_ppc() ``` Many of the available PPCs can also be carried out within levels of a grouping variable. Any function for PPCs by group will have a name ending in `_grouped` and will accept an additional argument `group`. The full list of currently available `_grouped` functions is: ```{r available_ppc-grouped} available_ppc(pattern = "_grouped") ``` #### ppc_stat_grouped For example, `ppc_stat_grouped` is the same as `ppc_stat` except that the test statistic is computed within levels of the grouping variable and a separate plot is made for each level: ```{r ppc_stat_grouped, message=FALSE} ppc_stat_grouped(y, yrep_nb, group = roaches$treatment, stat = "prop_zero") ``` See Figure 8 in [Gabry et al. (2019)](#gabry2019) for another example of using `ppc_stat_grouped`.
    ## Providing an interface to bayesplot PPCs from another package The **bayesplot** package provides the S3 generic function `pp_check`. Authors of R packages for Bayesian inference are encouraged to define methods for the fitted model objects created by their packages. This will hopefully be convenient for both users and developers and contribute to the use of the same naming conventions across many of the R packages for Bayesian data analysis. To provide an interface to **bayesplot** from your package, you can very easily define a `pp_check` method (or multiple `pp_check` methods) for the fitted model objects created by your package. All a `pp_check` method needs to do is provide the `y` vector and `yrep` matrix arguments to the various plotting functions included in **bayesplot**. ### Defining a `pp_check` method Here is an example for how to define a simple `pp_check` method in a package that creates fitted model objects of class `"foo"`. We will define a method `pp_check.foo` that extracts the data `y` and the draws from the posterior predictive distribution `yrep` from an object of class `"foo"` and then calls one of the plotting functions from **bayesplot**. Suppose that objects of class `"foo"` are lists with named components, two of which are `y` and `yrep`. Here's a simple method `pp_check.foo` that offers the user the option of two different plots: ```{r pp_check.foo} # @param object An object of class "foo". # @param type The type of plot. # @param ... Optional arguments passed on to the bayesplot plotting function. pp_check.foo <- function(object, type = c("multiple", "overlaid"), ...) { type <- match.arg(type) y <- object[["y"]] yrep <- object[["yrep"]] stopifnot(nrow(yrep) >= 50) samp <- sample(nrow(yrep), size = ifelse(type == "overlaid", 50, 5)) yrep <- yrep[samp, ] if (type == "overlaid") { ppc_dens_overlay(y, yrep, ...) } else { ppc_hist(y, yrep, ...) } } ``` To try out `pp_check.foo` we can just make a list with `y` and `yrep` components and give it class `foo`: ```{r foo-object} x <- list(y = rnorm(200), yrep = matrix(rnorm(1e5), nrow = 500, ncol = 200)) class(x) <- "foo" ``` ```{r pp_check-1, message=FALSE} color_scheme_set("purple") pp_check(x, type = "multiple", binwidth = 0.3) ``` ```{r pp_check-2} color_scheme_set("darkgray") pp_check(x, type = "overlaid") ``` ### Examples of `pp_check` methods in other packages Several packages currently use this approach to provide an interface to **bayesplot**'s graphical posterior predictive checks. See, for example, the `pp_check` methods in the [**rstanarm**](https://CRAN.R-project.org/package=rstanarm) and [**brms**](https://CRAN.R-project.org/package=brms) packages.
    ## References Buerkner, P. (2017). brms: Bayesian Regression Models using Stan. R package version 1.7.0. https://CRAN.R-project.org/package=brms Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.15.3. https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. \doi:10.1111/rssa.12378. ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [arXiv preprint](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper)) Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third edition. Stan Development Team. _Stan Modeling Language Users Guide and Reference Manual_. https://mc-stan.org/users/documentation/ bayesplot/inst/doc/graphical-ppcs.R0000644000176200001440000001132214563223017017020 0ustar liggesusersparams <- list(EVAL = TRUE) ## ----SETTINGS-knitr, include=FALSE-------------------------------------------- stopifnot(require("knitr")) library("bayesplot") knitr::opts_chunk$set( dev = "png", dpi = 150, fig.asp = 0.618, fig.width = 5, out.width = "60%", fig.align = "center", comment = NA, eval = if (isTRUE(exists("params"))) params$EVAL else FALSE ) ## ----pkgs, include=FALSE------------------------------------------------------ library("ggplot2") library("rstanarm") set.seed(840) ## ----eval=FALSE--------------------------------------------------------------- # library("bayesplot") # library("ggplot2") # library("rstanarm") ## ----roaches-data------------------------------------------------------------- head(roaches) # see help("rstanarm-datasets") roaches$roach100 <- roaches$roach1 / 100 # pre-treatment number of roaches (in 100s) ## ----roaches-model-pois, message=FALSE---------------------------------------- # using rstanarm's default priors. For details see the section on default # weakly informative priors at https://mc-stan.org/rstanarm/articles/priors.html fit_poisson <- stan_glm( y ~ roach100 + treatment + senior, offset = log(exposure2), family = poisson(link = "log"), data = roaches, seed = 1111, refresh = 0 # suppresses all output as of v2.18.1 of rstan ) ## ----print-pois--------------------------------------------------------------- print(fit_poisson) ## ----roaches-model-nb, message=FALSE------------------------------------------ fit_nb <- update(fit_poisson, family = "neg_binomial_2") ## ----print-nb----------------------------------------------------------------- print(fit_nb) ## ----y------------------------------------------------------------------------ y <- roaches$y ## ----yrep--------------------------------------------------------------------- yrep_poisson <- posterior_predict(fit_poisson, draws = 500) yrep_nb <- posterior_predict(fit_nb, draws = 500) dim(yrep_poisson) dim(yrep_nb) ## ----ppc_dens_overlay--------------------------------------------------------- color_scheme_set("brightblue") ppc_dens_overlay(y, yrep_poisson[1:50, ]) ## ----ppc_dens_overlay-2, message=FALSE, warning=FALSE------------------------- ppc_dens_overlay(y, yrep_poisson[1:50, ]) + xlim(0, 150) ## ----ppc_hist, message=FALSE-------------------------------------------------- ppc_hist(y, yrep_poisson[1:5, ]) ## ----ppc_hist-nb, message=FALSE----------------------------------------------- ppc_hist(y, yrep_nb[1:5, ]) ## ----ppc_hist-nb-2, message=FALSE--------------------------------------------- ppc_hist(y, yrep_nb[1:5, ], binwidth = 20) + coord_cartesian(xlim = c(-1, 300)) ## ----prop_zero---------------------------------------------------------------- prop_zero <- function(x) mean(x == 0) prop_zero(y) # check proportion of zeros in y ## ----ppc_stat, message=FALSE-------------------------------------------------- ppc_stat(y, yrep_poisson, stat = "prop_zero", binwidth = 0.005) ## ----ppc_stat-nb, message=FALSE----------------------------------------------- ppc_stat(y, yrep_nb, stat = "prop_zero") ## ----ppc_stat-max, message=FALSE---------------------------------------------- ppc_stat(y, yrep_poisson, stat = "max") ppc_stat(y, yrep_nb, stat = "max") ppc_stat(y, yrep_nb, stat = "max", binwidth = 100) + coord_cartesian(xlim = c(-1, 5000)) ## ----available_ppc------------------------------------------------------------ available_ppc() ## ----available_ppc-grouped---------------------------------------------------- available_ppc(pattern = "_grouped") ## ----ppc_stat_grouped, message=FALSE------------------------------------------ ppc_stat_grouped(y, yrep_nb, group = roaches$treatment, stat = "prop_zero") ## ----pp_check.foo------------------------------------------------------------- # @param object An object of class "foo". # @param type The type of plot. # @param ... Optional arguments passed on to the bayesplot plotting function. pp_check.foo <- function(object, type = c("multiple", "overlaid"), ...) { type <- match.arg(type) y <- object[["y"]] yrep <- object[["yrep"]] stopifnot(nrow(yrep) >= 50) samp <- sample(nrow(yrep), size = ifelse(type == "overlaid", 50, 5)) yrep <- yrep[samp, ] if (type == "overlaid") { ppc_dens_overlay(y, yrep, ...) } else { ppc_hist(y, yrep, ...) } } ## ----foo-object--------------------------------------------------------------- x <- list(y = rnorm(200), yrep = matrix(rnorm(1e5), nrow = 500, ncol = 200)) class(x) <- "foo" ## ----pp_check-1, message=FALSE------------------------------------------------ color_scheme_set("purple") pp_check(x, type = "multiple", binwidth = 0.3) ## ----pp_check-2--------------------------------------------------------------- color_scheme_set("darkgray") pp_check(x, type = "overlaid") bayesplot/inst/doc/plotting-mcmc-draws.html0000644000176200001440000526040014563223030020564 0ustar liggesusers Plotting MCMC draws using the bayesplot package

    Plotting MCMC draws using the bayesplot package

    Jonah Gabry

    2024-02-14

    Introduction

    This vignette focuses on plotting parameter estimates from MCMC draws. MCMC diagnostic plots are covered in the separate vignette Visual MCMC diagnostics, and graphical posterior predictive model checking is covered in the vignette Graphical posterior predictive checks.

    Setup

    In addition to bayesplot we’ll load the following packages:

    • ggplot2, in case we want to customize the ggplot objects created by bayesplot
    • rstanarm, for fitting the example models used throughout the vignette
    library("bayesplot")
library("ggplot2")
library("rstanarm")

    Example model

    The bayesplot package provides various plotting functions for visualizing Markov chain Monte Carlo (MCMC) draws from the posterior distribution of the parameters of a Bayesian model. In this vignette we demonstrate a few of these functions. Example usage of the functions not demonstrated here can be found in the package documentation.

    For demonstration we will use draws obtained using the stan_glm function in the rstanarm package (Gabry and Goodrich, 2017), but MCMC draws from using any package can be used with the functions in the bayesplot package. See, for example, brms, which, like rstanarm, calls the rstan package internally to use Stan’s MCMC sampler.

    head(mtcars) # see help("mtcars")
                       mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
    # linear regression model using stan_glm
# using '~ .' to include all variables
fit <- stan_glm(mpg ~ ., data = mtcars, seed = 1111)
print(fit)
    stan_glm
 family:       gaussian [identity]
 formula:      mpg ~ .
 observations: 32
 predictors:   11
------
            Median MAD_SD
(Intercept) 12.0   19.3  
cyl         -0.1    1.1  
disp         0.0    0.0  
hp           0.0    0.0  
drat         0.8    1.6  
wt          -3.7    1.9  
qsec         0.8    0.8  
vs           0.3    2.1  
am           2.6    2.1  
gear         0.6    1.5  
carb        -0.2    0.9  

Auxiliary parameter(s):
      Median MAD_SD
sigma 2.7    0.4   

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

    To use the posterior draws with the functions in the bayesplot package we’ll extract them from the fitted model object:

    posterior <- as.array(fit)
dim(posterior)
    [1] 1000    4   12
    dimnames(posterior)
    $iterations
NULL

$chains
[1] "chain:1" "chain:2" "chain:3" "chain:4"

$parameters
 [1] "(Intercept)" "cyl"         "disp"        "hp"          "drat"       
 [6] "wt"          "qsec"        "vs"          "am"          "gear"       
[11] "carb"        "sigma"

    We’ve used as.array above (as opposed to as.matrix) because it keeps the Markov chains separate (stan_glm runs four chains by default). Most of the plots don’t actually need the chains to be separate, but for a few of the plots we make in this vignette we’ll want to show the chains individually.


    Posterior uncertainty intervals

    For models fit using MCMC we can compute posterior uncertainty intervals (sometimes called “credible intervals”) in various ways. bayesplot currently provides plots of central intervals based on quantiles, although additional options may be provided in future releases (e.g., HDIs, which can be useful in particular cases).

    Documentation:

    mcmc_intervals, mcmc_areas

    Central posterior uncertainty intervals can be plotted using the mcmc_intervals function.

    color_scheme_set("red")
mcmc_intervals(posterior, pars = c("cyl", "drat", "am", "sigma"))

    The default is to show 50% intervals (the thick segments) and 90% intervals (the thinner outer lines). These defaults can be changed using the prob and prob_outer arguments, respectively. The points in the above plot are posterior medians. The point_est argument can be used to select posterior means instead or to omit the point estimates.

    To show the uncertainty intervals as shaded areas under the estimated posterior density curves we can use the mcmc_areas function.

    mcmc_areas(
  posterior, 
  pars = c("cyl", "drat", "am", "sigma"),
  prob = 0.8, # 80% intervals
  prob_outer = 0.99, # 99%
  point_est = "mean"
)


    Univariate marginal posterior distributions

    bayesplot provides functions for looking at histograms or kernel density estimates of marginal posterior distributions, either with all Markov chains combined or with the chains separate.

    Documentation:

    mcmc_hist

    The mcmc_hist function plots marginal posterior distributions (combining all chains):

    color_scheme_set("green")
mcmc_hist(posterior, pars = c("wt", "sigma"))

    If we want to plot log(sigma) rather than sigma we can either transform the draws in advance or use the transformations argument.

    color_scheme_set("blue")
mcmc_hist(posterior, pars = c("wt", "sigma"),
          transformations = list("sigma" = "log"))

    Most of the other functions for plotting MCMC draws also have a transformations argument.

    mcmc_hist_by_chain

    To view separate histograms of each of the four Markov chains we can use mcmc_hist_by_chain, which plots each chain in a separate facet in the plot.

    color_scheme_set("brightblue")
mcmc_hist_by_chain(posterior, pars = c("wt", "sigma"))

    mcmc_dens

    The mcmc_dens function is similar to mcmc_hist but plots kernel density estimates instead of histograms.

    color_scheme_set("purple")
mcmc_dens(posterior, pars = c("wt", "sigma"))

    mcmc_dens_overlay

    Like mcmc_hist_by_chain, the mcmc_dens_overlay function separates the Markov chains. But instead of plotting each chain individually, the density estimates are overlaid.

    mcmc_dens_overlay(posterior, pars = c("wt", "sigma"))

    mcmc_violin

    The mcmc_violin function plots the density estimates of each chain as violins and draws horizontal line segments at user-specified quantiles.

    color_scheme_set("teal")
mcmc_violin(posterior, pars = c("wt", "sigma"), probs = c(0.1, 0.5, 0.9))


    Bivariate plots

    Various functions are available for plotting bivariate marginal posterior distributions. Some of these functions also take optional arguments for adding MCMC diagnostic information to the plots. That additional functionality is discussed in the separate Visual MCMC diagnostics vignette.

    Documentation:

    mcmc_scatter

    The mcmc_scatter function creates a simple scatterplot of two parameters.

    color_scheme_set("gray")
mcmc_scatter(posterior, pars = c("(Intercept)", "wt"), 
             size = 1.5, alpha = 0.5)

    mcmc_hex

    The mcmc_hex function creates a similar plot but using hexagonal binning, which can be useful to avoid overplotting.

    # requires hexbin package
if (requireNamespace("hexbin", quietly = TRUE)) {
  mcmc_hex(posterior, pars = c("(Intercept)", "wt"))
}

    mcmc_pairs

    In addition to mcmc_scatter and mcmc_hex, bayesplot now provides an mcmc_pairs function for creating pairs plots with more than two parameters.

    color_scheme_set("pink")
mcmc_pairs(posterior, pars = c("(Intercept)", "wt", "sigma"),
           off_diag_args = list(size = 1.5))

    The univariate marginal posteriors are shown along the diagonal as histograms, but this can be changed to densities by setting diag_fun="dens". Bivariate plots are displayed above and below the diagonal as scatterplots, but it is also possible to use hex plots by setting off_diag_fun="hex". By default, mcmc_pairs shows some of the Markov chains (half, if an even number of chains) above the diagonal and the others below. There are many more options for controlling how the draws should be split between the plots above and below the diagonal (see the documentation for the condition argument), but they are more useful when MCMC diagnostic information is included. This is discussed in the Visual MCMC diagnostics vignette.


    Trace plots

    Trace plots are time series plots of Markov chains. In this vignette we show the standard trace plots that bayesplot can make. For models fit using any Stan interface (or Hamiltonian Monte Carlo in general), the Visual MCMC diagnostics vignette provides an example of also adding information about divergences to trace plots.

    Documentation:

    mcmc_trace

    The mcmc_trace function creates standard trace plots:

    color_scheme_set("blue")
mcmc_trace(posterior, pars = c("wt", "sigma"))

    If it’s hard to see the difference between the chains we can change to a mixed color scheme, for example:

    color_scheme_set("mix-blue-red")
mcmc_trace(posterior, pars = c("wt", "sigma"), 
           facet_args = list(ncol = 1, strip.position = "left"))

    The code above also illustrates the use of the facet_args argument, which is a list of parameters passed to facet_wrap in ggplot2. Specifying ncol=1 means the trace plots will be stacked in a single column rather than placed side by side, and strip.position="left" moves the facet labels to the y-axis (instead of above each facet).

    The "viridis" color scheme is also useful for trace plots because it is comprised of very distinct colors:

    color_scheme_set("viridis")
mcmc_trace(posterior, pars = "(Intercept)")

    mcmc_trace_highlight

    The mcmc_trace_highlight function uses points instead of lines and reduces the opacity of all but a single chain (which is specified using the highlight argument).

    mcmc_trace_highlight(posterior, pars = "sigma", highlight = 3)


    References

    Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.15.3. https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm

    Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. :10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)

    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC Press, London, third edition.

    Stan Development Team. (2017). Stan Modeling Language Users Guide and Reference Manual. https://mc-stan.org/users/documentation/

    bayesplot/inst/doc/plotting-mcmc-draws.R0000644000176200001440000001000014563223027020007 0ustar liggesusersparams <- list(EVAL = TRUE) ## ----SETTINGS-knitr, include=FALSE-------------------------------------------- stopifnot(require("knitr")) library("bayesplot") knitr::opts_chunk$set( dev = "png", dpi = 150, fig.asp = 0.618, fig.width = 5, out.width = "60%", fig.align = "center", comment = NA, eval = if (isTRUE(exists("params"))) params$EVAL else FALSE ) ## ----pkgs, include=FALSE------------------------------------------------------ library("ggplot2") library("rstanarm") ## ----eval=FALSE--------------------------------------------------------------- # library("bayesplot") # library("ggplot2") # library("rstanarm") ## ----mtcars------------------------------------------------------------------- head(mtcars) # see help("mtcars") ## ----eval=FALSE--------------------------------------------------------------- # # linear regression model using stan_glm # # using '~ .' to include all variables # fit <- stan_glm(mpg ~ ., data = mtcars, seed = 1111) # print(fit) ## ----stan_glm, include=FALSE-------------------------------------------------- fit <- stan_glm(mpg ~ ., data = mtcars, QR = TRUE, seed = 1111) ## ----print-fit, echo=FALSE---------------------------------------------------- print(fit) ## ----get-draws---------------------------------------------------------------- posterior <- as.array(fit) dim(posterior) dimnames(posterior) ## ----mcmc_intervals----------------------------------------------------------- color_scheme_set("red") mcmc_intervals(posterior, pars = c("cyl", "drat", "am", "sigma")) ## ----mcmc_areas--------------------------------------------------------------- mcmc_areas( posterior, pars = c("cyl", "drat", "am", "sigma"), prob = 0.8, # 80% intervals prob_outer = 0.99, # 99% point_est = "mean" ) ## ----mcmc_hist, message=FALSE------------------------------------------------- color_scheme_set("green") mcmc_hist(posterior, pars = c("wt", "sigma")) ## ----mcmc_hist-transform, message=FALSE--------------------------------------- color_scheme_set("blue") mcmc_hist(posterior, pars = c("wt", "sigma"), transformations = list("sigma" = "log")) ## ----mcmc_hist_by_chain, message=FALSE---------------------------------------- color_scheme_set("brightblue") mcmc_hist_by_chain(posterior, pars = c("wt", "sigma")) ## ----mcmc_dens, message=FALSE------------------------------------------------- color_scheme_set("purple") mcmc_dens(posterior, pars = c("wt", "sigma")) ## ----mcmc_dens_overlay, message=FALSE----------------------------------------- mcmc_dens_overlay(posterior, pars = c("wt", "sigma")) ## ----mcmc_violin-------------------------------------------------------------- color_scheme_set("teal") mcmc_violin(posterior, pars = c("wt", "sigma"), probs = c(0.1, 0.5, 0.9)) ## ----mcmc_scatter------------------------------------------------------------- color_scheme_set("gray") mcmc_scatter(posterior, pars = c("(Intercept)", "wt"), size = 1.5, alpha = 0.5) ## ----mcmc_hex----------------------------------------------------------------- # requires hexbin package if (requireNamespace("hexbin", quietly = TRUE)) { mcmc_hex(posterior, pars = c("(Intercept)", "wt")) } ## ----mcmc_pairs, message=FALSE------------------------------------------------ color_scheme_set("pink") mcmc_pairs(posterior, pars = c("(Intercept)", "wt", "sigma"), off_diag_args = list(size = 1.5)) ## ----mcmc_trace--------------------------------------------------------------- color_scheme_set("blue") mcmc_trace(posterior, pars = c("wt", "sigma")) ## ----change-scheme------------------------------------------------------------ color_scheme_set("mix-blue-red") mcmc_trace(posterior, pars = c("wt", "sigma"), facet_args = list(ncol = 1, strip.position = "left")) ## ----viridis-scheme, eval=FALSE----------------------------------------------- # color_scheme_set("viridis") # mcmc_trace(posterior, pars = "(Intercept)") ## ----mcmc_trace_highlight----------------------------------------------------- mcmc_trace_highlight(posterior, pars = "sigma", highlight = 3) bayesplot/inst/doc/visual-mcmc-diagnostics.html0000644000176200001440000743250314563223213021430 0ustar liggesusers Visual MCMC diagnostics using the bayesplot package

    Visual MCMC diagnostics using the bayesplot package

    Jonah Gabry and Martin Modrák

    2024-02-14

    Introduction

    This vignette focuses on MCMC diagnostic plots, in particular on diagnosing divergent transitions and on the n_eff and Rhat statistics that help you determine that the chains have mixed well. Plots of parameter estimates from MCMC draws are covered in the separate vignette Plotting MCMC draws, and graphical posterior predictive model checking is covered in the Graphical posterior predictive checks vignette.

    Note that most of these plots can also be browsed interactively using the shinystan package.

    Setup

    In addition to bayesplot we’ll load the following packages:

    • ggplot2, in case we want to customize the ggplot objects created by bayesplot
    • rstan, for fitting the example models used throughout the vignette
    library("bayesplot")
library("ggplot2")
library("rstan")

    Example model

    Before we delve into the actual plotting we need to fit a model to have something to work with. In this vignette we’ll use the eight schools example, which is discussed in many places, including Rubin (1981), Gelman et al. (2013), and the RStan Getting Started wiki. This is a simple hierarchical meta-analysis model with data consisting of point estimates y and standard errors sigma from analyses of test prep programs in J=8 schools. Ideally we would have the full data from each of the previous studies, but in this case we only have the these estimates.

    schools_dat <- list(
  J = 8, 
  y = c(28,  8, -3,  7, -1,  1, 18, 12),
  sigma = c(15, 10, 16, 11,  9, 11, 10, 18)
)

    The model is: \[ \begin{align*} y_j &\sim {\rm Normal}(\theta_j, \sigma_j), \quad j = 1,\dots,J \\ \theta_j &\sim {\rm Normal}(\mu, \tau), \quad j = 1, \dots, J \\ \mu &\sim {\rm Normal}(0, 10) \\ \tau &\sim {\rm half-Cauchy}(0, 10), \end{align*} \] with the normal distribution parameterized by the mean and standard deviation, not the variance or precision. In Stan code:

    // Saved in 'schools_mod_cp.stan'
data {
  int<lower=0> J;
  vector[J] y;
  vector<lower=0>[J] sigma;
}
parameters {
  real mu;
  real<lower=0> tau;
  vector[J] theta;
}
model {
  mu ~ normal(0, 10);
  tau ~ cauchy(0, 10);
  theta ~ normal(mu, tau);
  y ~ normal(theta, sigma);
}

    This parameterization of the model is referred to as the centered parameterization (CP). We’ll also fit the same statistical model but using the so-called non-centered parameterization (NCP), which replaces the vector \(\theta\) with a vector \(\eta\) of a priori i.i.d. standard normal parameters and then constructs \(\theta\) deterministically from \(\eta\) by scaling by \(\tau\) and shifting by \(\mu\): \[ \begin{align*} \theta_j &= \mu + \tau \,\eta_j, \quad j = 1,\dots,J \\ \eta_j &\sim N(0,1), \quad j = 1,\dots,J. \end{align*} \] The Stan code for this model is:

    // Saved in 'schools_mod_ncp.stan'
data {
  int<lower=0> J;
  vector[J] y;
  vector<lower=0>[J] sigma;
}
parameters {
  real mu;
  real<lower=0> tau;
  vector[J] eta;
}
transformed parameters {
  vector[J] theta;
  theta = mu + tau * eta;
}
model {
  mu ~ normal(0, 10);
  tau ~ cauchy(0, 10);
  eta ~ normal(0, 1); // implies theta ~ normal(mu, tau)
  y ~ normal(theta, sigma);
}

    The centered and non-centered are two parameterizations of the same statistical model, but they have very different practical implications for MCMC. Using the bayesplot diagnostic plots, we’ll see that, for this data, the NCP is required in order to properly explore the posterior distribution.

    To fit both models we first translate the Stan code to C++ and compile it using the stan_model function.

    schools_mod_cp <- stan_model("schools_mod_cp.stan")
schools_mod_ncp <- stan_model("schools_mod_ncp.stan")

    We then fit the model by calling Stan’s MCMC algorithm using the sampling function (the increased adapt_delta param is to make the sampler a bit more “careful” and avoid false positive divergences),

    fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214055, control = list(adapt_delta = 0.9))
    Warning: There were 44 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them.
    Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
https://mc-stan.org/misc/warnings.html#bfmi-low
    Warning: Examine the pairs() plot to diagnose sampling problems
    Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess
    Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess
    fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9))

    and extract a iterations x chains x parameters array of posterior draws with as.array,

    # Extract posterior draws for later use
posterior_cp <- as.array(fit_cp)
posterior_ncp <- as.array(fit_ncp)

    You may have noticed the warnings about divergent transitions for the centered parameterization fit. Those are serious business and in most cases indicate that something is wrong with the model and the results should not be trusted. For an explanation of these warnings see Divergent transitions after warmup. We’ll have a look at diagnosing the source of the divergences first and then dive into some diagnostics that should be checked even if there are no warnings from the sampler.


    Diagnostics for the No-U-Turn Sampler

    The No-U-Turn Sampler (NUTS, Hoffman and Gelman, 2014) is the variant of Hamiltonian Monte Carlo (HMC) used by Stan and the various R packages that depend on Stan for fitting Bayesian models. The bayesplot package has special functions for visualizing some of the unique diagnostics permitted by HMC, and NUTS in particular. See Betancourt (2017), Betancourt and Girolami (2013), and Stan Development Team (2017) for more details on the concepts.

    Documentation:

    The special bayesplot functions for NUTS diagnostics are

    available_mcmc(pattern = "_nuts_")
    bayesplot MCMC module:
(matching pattern '_nuts_') 
  mcmc_nuts_acceptance
  mcmc_nuts_divergence
  mcmc_nuts_energy
  mcmc_nuts_stepsize
  mcmc_nuts_treedepth

    Those functions require more information than simply the posterior draws, in particular the log of the posterior density for each draw and some NUTS-specific diagnostic values may be needed. The bayesplot package provides generic functions log_posterior and nuts_params for extracting this information from fitted model objects. Currently methods are provided for models fit using the rstan, rstanarm and brms packages, although it is not difficult to define additional methods for the objects returned by other R packages. For the Stan models we fit above we can use the log_posterior and nuts_params methods for stanfit objects:

    lp_cp <- log_posterior(fit_cp)
head(lp_cp)
      Chain Iteration     Value
1     1         1 -22.24092
2     1         2 -19.47006
3     1         3 -20.53399
4     1         4 -24.14739
5     1         5 -20.18378
6     1         6 -16.20084
    np_cp <- nuts_params(fit_cp)
head(np_cp)
      Chain Iteration     Parameter     Value
1     1         1 accept_stat__ 0.8270966
2     1         2 accept_stat__ 0.9643772
3     1         3 accept_stat__ 0.9787898
4     1         4 accept_stat__ 0.9956166
5     1         5 accept_stat__ 0.9668557
6     1         6 accept_stat__ 0.9912499
    # for the second model
lp_ncp <- log_posterior(fit_ncp)
np_ncp <- nuts_params(fit_ncp)

    In addition to the NUTS-specific plotting functions, some of the general MCMC plotting functions demonstrated in the Plotting MCMC draws vignette also take optional arguments that can be used to display important HMC/NUTS diagnostic information. We’ll see examples of this in the next section on divergent transitions.

    Divergent transitions

    When running the Stan models above, there were warnings about divergent transitions. Here we’ll look at diagnosing the source of divergences through visualizations.

    mcmc_parcoord

    The mcmc_parcoord plot shows one line per iteration, connecting the parameter values at this iteration. This lets you see global patterns in the divergences.

    This function works in general without including information about the divergences, but if the optional np argument is used to pass NUTS parameter information, then divergences will be colored in the plot (by default in red).

    color_scheme_set("darkgray")
mcmc_parcoord(posterior_cp, np = np_cp)

    Here, you may notice that divergences in the centered parameterization happen exclusively when tau, the hierarchical standard deviation, goes near zero and the values of the thetas are essentially fixed. This makes tau immediately suspect. See Gabry et al. (2019) for another example of the parallel coordinates plot.

    mcmc_pairs

    The mcmc_pairs function can also be used to look at multiple parameters at once, but unlike mcmc_parcoord (which works well even when including several dozen parameters) mcmc_pairs is more useful for up to ~8 parameters. It shows univariate histograms and bivariate scatter plots for selected parameters and is especially useful in identifying collinearity between variables (which manifests as narrow bivariate plots) as well as the presence of multiplicative non-identifiabilities (banana-like shapes).

    Let’s look at how tau interacts with other variables, using only one of the thetas to keep the plot readable:

    mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"),
           off_diag_args = list(size = 0.75))

    Note that each bivariate plot is present twice – by default each of those contain half of the chains, so you also get to see if the chains produced similar results (see the documentation for the condition argument for other options). Here, the interaction of tau and theta[1] seems most interesting, as it concentrates the divergences into a tight region.

    Further examples of pairs plots and instructions for using the various optional arguments to mcmc_pairs are provided via help("mcmc_pairs").

    mcmc_scatter

    Using the mcmc_scatter function (with optional argument np) we can look at a single bivariate plot to investigate it more closely. For hierarchical models, a good place to start is to plot a “local” parameter (theta[j]) against a “global” scale parameter on which it depends (tau).

    We will also use the transformations argument to look at the log of tau, as this is what Stan is doing under the hood for parameters like tau that have a lower bound of zero. That is, even though the draws for tau returned from Stan are all positive, the parameter space that the Markov chains actual explore is unconstrained. Transforming tau is not strictly necessary for the plot (often the plot is still useful without it) but plotting in the unconstrained is often even more informative.

    First the plot for the centered parameterization:

    # assign to an object so we can reuse later
scatter_theta_cp <- mcmc_scatter(
  posterior_cp, 
  pars = c("theta[1]", "tau"), 
  transform = list(tau = "log"), # can abbrev. 'transformations'
  np = np_cp, 
  size = 1
)
scatter_theta_cp

    The shape of this bivariate distribution resembles a funnel (or tornado). This one in particular is essentially the same as an example referred to as Neal’s funnel (details in the Stan manual) and it is a clear indication that the Markov chains are struggling to explore the tip of the funnel, which is narrower than the rest of the space.

    The main problem is that large steps are required to explore the less narrow regions efficiently, but those steps become too large for navigating the narrow region. The required step size is connected to the value of tau. When tau is large it allows for large variation in theta (and requires large steps) while small tau requires small steps in theta.

    The non-centered parameterization avoids this by sampling the eta parameter which, unlike theta, is a priori independent of tau. Then theta is computed deterministically from the parameters eta, mu and tau afterwards. Here’s the same plot as above, but with eta[1] from non-centered parameterization instead of theta[1] from the centered parameterization:

    scatter_eta_ncp <- mcmc_scatter(
  posterior_ncp, 
  pars = c("eta[1]", "tau"), 
  transform = list(tau = "log"), 
  np = np_ncp, 
  size = 1
)
scatter_eta_ncp

    We can see that the funnel/tornado shape is replaced by a somewhat Gaussian blob/cloud and the divergences go away. Gabry et al. (2019) has further discussion of this example.

    Ultimately we only care about eta insofar as it enables the Markov chains to better explore the posterior, so let’s directly examine how much more exploration was possible after the reparameterization. For the non-centered parameterization we can make the same scatterplot but use the values of theta[1] = mu + eta[1] * tau instead of eta[1]. Below is a side by side comparison with the scatterplot of theta[1] vs log(tau) from the centered parameterization that we made above. We will also force the plots to have the same \(y\)-axis limits, which will make the most important difference much more apparent:

    # A function we'll use several times to plot comparisons of the centered 
# parameterization (cp) and the non-centered parameterization (ncp). See
# help("bayesplot_grid") for details on the bayesplot_grid function used here.
compare_cp_ncp <- function(cp_plot, ncp_plot, ncol = 2, ...) {
  bayesplot_grid(
    cp_plot, ncp_plot, 
    grid_args = list(ncol = ncol),
    subtitles = c("Centered parameterization", 
                  "Non-centered parameterization"),
    ...
  )
}


scatter_theta_ncp <- mcmc_scatter(
  posterior_ncp, 
  pars = c("theta[1]", "tau"), 
  transform = list(tau = "log"), 
  np = np_ncp, 
  size = 1
)

compare_cp_ncp(scatter_theta_cp, scatter_theta_ncp, ylim = c(-8, 4))

    Once we transform the eta values into theta values we actually see an even more pronounced funnel/tornado shape than we have with the centered parameterization. But this is precisely what we want! The non-centered parameterization allowed us to obtain draws from the funnel distribution without having to directly navigate the curvature of the funnel. With the centered parameterization the chains never could make it into the neck of funnel and we see a clustering of divergences and no draws in the tail of the distribution.

    mcmc_trace

    Another useful diagnostic plot is the trace plot, which is a time series plot of the Markov chains. That is, a trace plot shows the evolution of parameter vector over the iterations of one or many Markov chains. The np argument to the mcmc_trace function can be used to add a rug plot of the divergences to a trace plot of parameter draws. Typically we can see that at least one of the chains is getting stuck wherever there is a cluster of many red marks.

    Here is the trace plot for the tau parameter from the centered parameterization:

    color_scheme_set("mix-brightblue-gray")
mcmc_trace(posterior_cp, pars = "tau", np = np_cp) + 
  xlab("Post-warmup iteration")

    The first thing to note is that all chains seem to be exploring the same region of parameter values, which is a good sign. But the plot is too crowded to help us diagnose divergences. We may however zoom in to investigate, using the window argument:

    mcmc_trace(posterior_cp, pars = "tau", np = np_cp, window = c(300,500)) + 
  xlab("Post-warmup iteration")

    What we see here is that chains can get stuck as tau approaches zero and spend substantial time in the same region of the parameter space. This is just another indication that there is problematic geometry at \(\tau \simeq 0\) – healthy chains jump up and down frequently.

    mcmc_nuts_divergence

    To understand how the divergences interact with the model globally, we can use the mcmc_nuts_divergence function:

    color_scheme_set("red")
mcmc_nuts_divergence(np_cp, lp_cp)

    In the top panel we see the distribution of the log-posterior when there was no divergence vs the distribution when there was a divergence. Divergences often indicate that some part of the posterior isn’t being explored and the plot confirms that lp|Divergence indeed has lighter tails than lp|No divergence.

    The bottom panel shows the same thing but instead of the log-posterior the NUTS acceptance statistic is shown.

    Specifying the optional chain argument will overlay the plot just for a particular Markov chain on the plot for all chains combined:

    mcmc_nuts_divergence(np_cp, lp_cp, chain = 4)

    For the non-centered parameterization we may get a few warnings about divergences but if we do we’ll have far fewer of them to worry about.

    mcmc_nuts_divergence(np_ncp, lp_ncp)

    If there are only a few divergences we can often get rid of them by increasing the target acceptance rate (adapt_delta, the upper limit is 1), which has the effect of lowering the step size used by the sampler and allowing the Markov chains to explore more complicated curvature in the target distribution.

    fit_cp_2 <- sampling(schools_mod_cp, data = schools_dat,
                     control = list(adapt_delta = 0.999), seed = 978245244)
    Warning: There were 18 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them.
    Warning: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See
https://mc-stan.org/misc/warnings.html#bfmi-low
    Warning: Examine the pairs() plot to diagnose sampling problems
    Warning: The largest R-hat is 1.06, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat
    Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess
    Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess
    fit_ncp_2 <- sampling(schools_mod_ncp, data = schools_dat,
                      control = list(adapt_delta = 0.999), seed = 843256842)

    For the first model and this particular data, increasing adapt_delta will not solve the problem and a reparameterization is required.

    mcmc_nuts_divergence(nuts_params(fit_cp_2), log_posterior(fit_cp_2))

    mcmc_nuts_divergence(nuts_params(fit_ncp_2), log_posterior(fit_ncp_2))

    Energy and Bayesian fraction of missing information

    The mcmc_nuts_energy function creates plots similar to those presented in Betancourt (2017). While mcmcm_nuts_divergence can identify light tails and incomplete exploration of the target distribution, the mcmc_nuts_energy function can identify overly heavy tails that are also challenging for sampling. Informally, the energy diagnostic for HMC (and the related energy-based Bayesian fraction of missing information) quantifies the heaviness of the tails of the posterior distribution.

    mcmc_nuts_energy

    The plot created by mcmc_nuts_energy shows overlaid histograms of the (centered) marginal energy distribution \(\pi_E\) and the first-differenced distribution \(\pi_{\Delta E}\),

    color_scheme_set("red")
mcmc_nuts_energy(np_cp)

    The two histograms ideally look the same (Betancourt, 2017), which is only the case for the non-centered parameterization (right):

    compare_cp_ncp(
  mcmc_nuts_energy(np_cp, binwidth = 1/2),
  mcmc_nuts_energy(np_ncp, binwidth = 1/2)
)

    The difference between the parameterizations is even more apparent if we force the step size to a smaller value and help the chains explore more of the posterior:

    np_cp_2 <- nuts_params(fit_cp_2)
np_ncp_2 <- nuts_params(fit_ncp_2)

compare_cp_ncp(
  mcmc_nuts_energy(np_cp_2), 
  mcmc_nuts_energy(np_ncp_2)
)

    See Betancourt (2017) for more on this particular example as well as the general theory behind the energy plots.


    General MCMC diagnostics

    A Markov chain generates draws from the target distribution only after it has converged to an equilibrium. Unfortunately, this is only guaranteed in the limit in theory. In practice, diagnostics must be applied to monitor whether the Markov chain(s) have converged. The bayesplot package provides various plotting functions for visualizing Markov chain Monte Carlo (MCMC) diagnostics after fitting a Bayesian model. MCMC draws from any package can be used, although there are a few diagnostic plots that we will see later in this vignette that are specifically intended to be used for Stan models (or models fit using the same algorithms as Stan).

    Documentation:

    Rhat: potential scale reduction statistic

    One way to monitor whether a chain has converged to the equilibrium distribution is to compare its behavior to other randomly initialized chains. This is the motivation for the potential scale reduction statistic, split-\(\hat{R}\). The split-\(\hat{R}\) statistic measures the ratio of the average variance of draws within each chain to the variance of the pooled draws across chains; if all chains are at equilibrium, these will be the same and \(\hat{R}\) will be one. If the chains have not converged to a common distribution, the \(\hat{R}\) statistic will be greater than one (see Gelman et al. 2013, Stan Development Team 2018).

    The bayesplot package provides the functions mcmc_rhat and mcmc_rhat_hist for visualizing \(\hat{R}\) estimates.

    First we’ll quickly fit one of the models above again, this time intentionally using too few MCMC iterations and allowing more dispersed initial values. This should lead to some high \(\hat{R}\) values.

    fit_cp_bad_rhat <- sampling(schools_mod_cp, data = schools_dat, 
                            iter = 50, init_r = 10, seed = 671254821)
    Warning: There were 48 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
    Warning: Examine the pairs() plot to diagnose sampling problems
    Warning: The largest R-hat is 1.73, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat
    Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess
    Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess

    bayesplot provides a generic rhat extractor function, currently with methods defined for models fit using the rstan, rstanarm and brms packages. But regardless of how you fit your model, all bayesplot needs is a vector of \(\hat{R}\) values.

    rhats <- rhat(fit_cp_bad_rhat)
print(rhats)
          mu      tau theta[1] theta[2] theta[3] theta[4] theta[5] theta[6] 
1.195701 1.652152 1.299397 1.396105 1.255962 1.123406 1.154050 1.424646 
theta[7] theta[8]     lp__ 
1.133948 1.577158 1.839991

    mcmc_rhat, mcmc_rhat_hist

    We can visualize the \(\hat{R}\) values with the mcmc_rhat function:

    color_scheme_set("brightblue") # see help("color_scheme_set")
mcmc_rhat(rhats)

    In the plot, the points representing the \(\hat{R}\) values are colored based on whether they are less than \(1.05\), between \(1.05\) and \(1.1\), or greater than \(1.1\). There is no theoretical reason to trichotomize \(\hat{R}\) values using these cutoffs, so keep in mind that this is just a heuristic.

    The \(y\)-axis text is off by default for this plot because it’s only possible to see the labels clearly for models with very few parameters. We can see the names of the parameters with the concerning \(\hat{R}\) values using the yaxis_text convenience function (which passes arguments like hjust to ggplot2::element_text):

    mcmc_rhat(rhats) + yaxis_text(hjust = 1)

    If we look at the same model fit using longer Markov chains we should see all \(\hat{R} < 1.1\), and all points in the plot the same (light) color:

    mcmc_rhat(rhat = rhat(fit_cp)) + yaxis_text(hjust = 0)

    We can see the same information shown by mcmc_rhat but in histogram form using the mcmc_rhat_hist function. See the Examples section in help("mcmc_rhat_hist") for examples.

    Effective sample size

    The effective sample size is an estimate of the number of independent draws from the posterior distribution of the estimand of interest. The \(n_{eff}\) metric used in Stan is based on the ability of the draws to estimate the true mean value of the parameter, which is related to (but not necessarily equivalent to) estimating other functions of the draws. Because the draws within a Markov chain are not independent if there is autocorrelation, the effective sample size, \(n_{eff}\), is usually smaller than the total sample size, \(N\) (although it may be larger in some cases1). The larger the ratio of \(n_{eff}\) to \(N\) the better (see Gelman et al. 2013, Stan Development Team 2018 for more details) .

    The bayesplot package provides a generic neff_ratio extractor function, currently with methods defined for models fit using the rstan, rstanarm and brms packages. But regardless of how you fit your model, all bayesplot needs is a vector of \(n_{eff}/N\) values. The mcmc_neff and mcmc_neff_hist can then be used to plot the ratios.

    mcmc_neff, mcmc_neff_hist

    ratios_cp <- neff_ratio(fit_cp)
print(ratios_cp)
            mu        tau   theta[1]   theta[2]   theta[3]   theta[4]   theta[5] 
0.10870912 0.07431729 0.15594301 0.18650716 0.29205327 0.24055708 0.18942798 
  theta[6]   theta[7]   theta[8]       lp__ 
0.23979849 0.10096636 0.33114694 0.03549358 
    mcmc_neff(ratios_cp, size = 2)

    In the plot, the points representing the values of \(n_{eff}/N\) are colored based on whether they are less than \(0.1\), between \(0.1\) and \(0.5\), or greater than \(0.5\). These particular values are arbitrary in that they have no particular theoretical meaning, but a useful heuristic is to worry about any \(n_{eff}/N\) less than \(0.1\).

    One important thing to keep in mind is that these ratios will depend not only on the model being fit but also on the particular MCMC algorithm used. One reason why we have such high ratios of \(n_{eff}\) to \(N\) is that the No-U-Turn sampler used by rstan generally produces draws from the posterior distribution with much lower autocorrelations compared to draws obtained using other MCMC algorithms (e.g., Gibbs).

    Even for models fit using rstan the parameterization can make a big difference. Here are the \(n_{eff}/N\) plots for fit_cp and fit_ncp side by side.

    neff_cp <- neff_ratio(fit_cp, pars = c("theta", "mu", "tau"))
neff_ncp <- neff_ratio(fit_ncp, pars = c("theta", "mu", "tau"))
compare_cp_ncp(mcmc_neff(neff_cp), mcmc_neff(neff_ncp), ncol = 1)

    Because of the difference in parameterization, the effective sample sizes are much better for the second model, the non-centered parameterization.

    Autocorrelation

    As mentioned above, \(n_{eff}/N\) decreases as autocorrelation becomes more extreme. We can visualize the autocorrelation using the mcmc_acf (line plot) or mcmc_acf_bar (bar plot) functions. For the selected parameters, these functions show the autocorrelation for each Markov chain separately up to a user-specified number of lags. Positive autocorrelation is bad (it means the chain tends to stay in the same area between iterations) and you want it to drop quickly to zero with increasing lag. Negative autocorrelation is possible and it is useful as it indicates fast convergence of sample mean towards true mean.

    mcmc_acf, mcmc_acf_bar

    Here we can again see a difference when comparing the two parameterizations of the same model. For model 1, \(\theta_1\) is the primitive parameter for school 1, whereas for the non-centered parameterization in model 2 the primitive parameter is \(\eta_1\) (and \(\theta_1\) is later constructed from \(\eta_1\), \(\mu\), and \(\tau\)):

    compare_cp_ncp(
  mcmc_acf(posterior_cp, pars = "theta[1]", lags = 10),
  mcmc_acf(posterior_ncp, pars = "eta[1]", lags = 10)
)


    References

    Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/abs/1701.02434

    Betancourt, M. (2016). Diagnosing suboptimal cotangent disintegrations in Hamiltonian Monte Carlo. https://arxiv.org/abs/1604.00695

    Betancourt, M. and Girolami, M. (2013). Hamiltonian Monte Carlo for hierarchical models. https://arxiv.org/abs/1312.0906

    Gabry, J., and Goodrich, B. (2018). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.17.4. https://mc-stan.org/rstanarm/

    Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. :10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)

    Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science. 7(4): 457–472.

    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC Press, London, third edition.

    Hoffman, M. D. and Gelman, A. (2014). The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. 15:1593–1623.

    Rubin, D. B. (1981). Estimation in Parallel Randomized Experiments. Journal of Educational and Behavioral Statistics. 6:377–401.

    Stan Development Team. Stan Modeling Language Users Guide and Reference Manual. https://mc-stan.org/users/documentation/

    Stan Development Team. (2018). RStan: the R interface to Stan. R package version 2.17.3. https://mc-stan.org/rstan/

    1. \(n_{eff} > N\) indicates that the mean estimate of the parameter computed from Stan draws approaches the true mean faster than the mean estimate computed from independent samples from the true posterior (the estimate from Stan has smaller variance). This is possible when the draws are anticorrelated - draws above the mean tend to be well matched with draws below the mean. Other functions computed from draws (quantiles, posterior intervals, tail probabilities) may not necessarily approach the true posterior faster. Google “antithetic sampling” or visit the relevant forum thread for some further explanation.↩︎

    bayesplot/inst/doc/visual-mcmc-diagnostics.Rmd0000644000176200001440000007745514556270424021222 0ustar liggesusers--- title: "Visual MCMC diagnostics using the bayesplot package" author: "Jonah Gabry and Martin Modrák" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 3 params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") && !(isTRUE(as.logical(Sys.getenv("CI"))) && .Platform$OS.type == "windows") vignette: > %\VignetteIndexEntry{Visual MCMC diagnostics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, child="children/SETTINGS-knitr.txt"} ``` ```{r pkgs, include=FALSE} library("bayesplot") library("ggplot2") library("rstan") library("dplyr") #Used only for consistency checks rstan_options(auto_write = TRUE) #Helpful throughout development bayesplot_theme_set() ``` ## Introduction This vignette focuses on MCMC diagnostic plots, in particular on diagnosing divergent transitions and on the `n_eff` and `Rhat` statistics that help you determine that the chains have mixed well. Plots of parameter estimates from MCMC draws are covered in the separate vignette [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html), and graphical posterior predictive model checking is covered in the [_Graphical posterior predictive checks_](https://mc-stan.org/bayesplot/articles/graphical-ppcs.html) vignette. Note that most of these plots can also be browsed interactively using the [shinystan](https://mc-stan.org/shinystan/) package. ### Setup In addition to __bayesplot__ we'll load the following packages: * __ggplot2__, in case we want to customize the ggplot objects created by __bayesplot__ * __rstan__, for fitting the example models used throughout the vignette ```{r setup, eval=FALSE} library("bayesplot") library("ggplot2") library("rstan") ``` ### Example model Before we delve into the actual plotting we need to fit a model to have something to work with. In this vignette we'll use the eight schools example, which is discussed in many places, including Rubin (1981), Gelman et al. (2013), and the [RStan Getting Started](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started#how-to-use-rstan) wiki. This is a simple hierarchical meta-analysis model with data consisting of point estimates `y` and standard errors `sigma` from analyses of test prep programs in `J=8` schools. Ideally we would have the full data from each of the previous studies, but in this case we only have the these estimates. ```{r schools_dat} schools_dat <- list( J = 8, y = c(28, 8, -3, 7, -1, 1, 18, 12), sigma = c(15, 10, 16, 11, 9, 11, 10, 18) ) ``` The model is: $$ \begin{align*} y_j &\sim {\rm Normal}(\theta_j, \sigma_j), \quad j = 1,\dots,J \\ \theta_j &\sim {\rm Normal}(\mu, \tau), \quad j = 1, \dots, J \\ \mu &\sim {\rm Normal}(0, 10) \\ \tau &\sim {\rm half-Cauchy}(0, 10), \end{align*} $$ with the normal distribution parameterized by the mean and standard deviation, not the variance or precision. In Stan code: ```{stan stancode1, output.var = "schools_mod_cp"} // Saved in 'schools_mod_cp.stan' data { int J; vector[J] y; vector[J] sigma; } parameters { real mu; real tau; vector[J] theta; } model { mu ~ normal(0, 10); tau ~ cauchy(0, 10); theta ~ normal(mu, tau); y ~ normal(theta, sigma); } ``` This parameterization of the model is referred to as the centered parameterization (CP). We'll also fit the same statistical model but using the so-called non-centered parameterization (NCP), which replaces the vector $\theta$ with a vector $\eta$ of a priori _i.i.d._ standard normal parameters and then constructs $\theta$ deterministically from $\eta$ by scaling by $\tau$ and shifting by $\mu$: $$ \begin{align*} \theta_j &= \mu + \tau \,\eta_j, \quad j = 1,\dots,J \\ \eta_j &\sim N(0,1), \quad j = 1,\dots,J. \end{align*} $$ The Stan code for this model is: ```{stan, stancode2, output.var = "schools_mod_ncp"} // Saved in 'schools_mod_ncp.stan' data { int J; vector[J] y; vector[J] sigma; } parameters { real mu; real tau; vector[J] eta; } transformed parameters { vector[J] theta; theta = mu + tau * eta; } model { mu ~ normal(0, 10); tau ~ cauchy(0, 10); eta ~ normal(0, 1); // implies theta ~ normal(mu, tau) y ~ normal(theta, sigma); } ``` The centered and non-centered are two parameterizations of the same statistical model, but they have very different practical implications for MCMC. Using the __bayesplot__ diagnostic plots, we'll see that, for this data, the NCP is required in order to properly explore the posterior distribution. To fit both models we first translate the Stan code to C++ and compile it using the `stan_model` function. ```{r compile-models, eval=FALSE} schools_mod_cp <- stan_model("schools_mod_cp.stan") schools_mod_ncp <- stan_model("schools_mod_ncp.stan") ``` We then fit the model by calling Stan's MCMC algorithm using the `sampling` function (the increased `adapt_delta` param is to make the sampler a bit more "careful" and avoid false positive divergences), ```{r fit-models-hidden, results='hide', message=FALSE} fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214055, control = list(adapt_delta = 0.9)) fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9)) ``` and extract a `iterations x chains x parameters` array of posterior draws with `as.array`, ```{r extract-draws} # Extract posterior draws for later use posterior_cp <- as.array(fit_cp) posterior_ncp <- as.array(fit_ncp) ``` You may have noticed the warnings about divergent transitions for the centered parameterization fit. Those are serious business and in most cases indicate that something is wrong with the model and the results should not be trusted. For an explanation of these warnings see [Divergent transitions after warmup](https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup). We'll have a look at diagnosing the source of the divergences first and then dive into some diagnostics that should be checked even if there are no warnings from the sampler.
    ## Diagnostics for the No-U-Turn Sampler The No-U-Turn Sampler (NUTS, Hoffman and Gelman, 2014) is the variant of Hamiltonian Monte Carlo (HMC) used by [Stan](https://mc-stan.org/) and the various R packages that depend on Stan for fitting Bayesian models. The **bayesplot** package has special functions for visualizing some of the unique diagnostics permitted by HMC, and NUTS in particular. See Betancourt (2017), Betancourt and Girolami (2013), and Stan Development Team (2017) for more details on the concepts. **Documentation:** * `help("MCMC-nuts")` * [mc-stan.org/bayesplot/reference/MCMC-nuts](https://mc-stan.org/bayesplot/reference/MCMC-nuts.html) ------ The special **bayesplot** functions for NUTS diagnostics are ```{r available_mcmc-nuts} available_mcmc(pattern = "_nuts_") ``` Those functions require more information than simply the posterior draws, in particular the log of the posterior density for each draw and some NUTS-specific diagnostic values may be needed. The **bayesplot** package provides generic functions `log_posterior` and `nuts_params` for extracting this information from fitted model objects. Currently methods are provided for models fit using the **rstan**, **rstanarm** and **brms** packages, although it is not difficult to define additional methods for the objects returned by other R packages. For the Stan models we fit above we can use the `log_posterior` and `nuts_params` methods for stanfit objects: ```{r extract-nuts-info} lp_cp <- log_posterior(fit_cp) head(lp_cp) np_cp <- nuts_params(fit_cp) head(np_cp) # for the second model lp_ncp <- log_posterior(fit_ncp) np_ncp <- nuts_params(fit_ncp) ``` ```{r echo=FALSE, warning=FALSE} # On rare occasions, the fits may not be illustrative. Currently the seed is # fixed, but if something in Stan changes and the fixed seeds produce unexpected # results (which should be rare), we want to know. n_divergent_cp <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() n_divergent_ncp <- np_ncp %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() if(n_divergent_cp < 10 || n_divergent_cp > 2000) { stop("Unexpected number of divergences in the CP model. Change seed?") } if(n_divergent_ncp > 0) { stop("Divergences in the NCP model. Fix a bug / change seed?") } ``` In addition to the NUTS-specific plotting functions, some of the general MCMC plotting functions demonstrated in the [_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html) vignette also take optional arguments that can be used to display important HMC/NUTS diagnostic information. We'll see examples of this in the next section on divergent transitions. ### Divergent transitions When running the Stan models above, there were warnings about divergent transitions. Here we'll look at diagnosing the source of divergences through visualizations. #### mcmc_parcoord The `mcmc_parcoord` plot shows one line per iteration, connecting the parameter values at this iteration. This lets you see global patterns in the divergences. This function works in general without including information about the divergences, but if the optional `np` argument is used to pass NUTS parameter information, then divergences will be colored in the plot (by default in red). ```{r mcmc_parcoord-1} color_scheme_set("darkgray") mcmc_parcoord(posterior_cp, np = np_cp) ``` Here, you may notice that divergences in the centered parameterization happen exclusively when `tau`, the hierarchical standard deviation, goes near zero and the values of the `theta`s are essentially fixed. This makes `tau` immediately suspect. See [Gabry et al. (2019)](#gabry2019) for another example of the parallel coordinates plot. #### mcmc_pairs The `mcmc_pairs` function can also be used to look at multiple parameters at once, but unlike `mcmc_parcoord` (which works well even when including several dozen parameters) `mcmc_pairs` is more useful for up to ~8 parameters. It shows univariate histograms and bivariate scatter plots for selected parameters and is especially useful in identifying collinearity between variables (which manifests as narrow bivariate plots) as well as the presence of multiplicative non-identifiabilities (banana-like shapes). Let's look at how `tau` interacts with other variables, using only one of the `theta`s to keep the plot readable: ```{r mcmc_pairs} mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"), off_diag_args = list(size = 0.75)) ``` Note that each bivariate plot is present twice -- by default each of those contain half of the chains, so you also get to see if the chains produced similar results (see the documentation for the `condition` argument for other options). Here, the interaction of `tau` and `theta[1]` seems most interesting, as it concentrates the divergences into a tight region. Further examples of pairs plots and instructions for using the various optional arguments to `mcmc_pairs` are provided via `help("mcmc_pairs")`. #### mcmc_scatter Using the `mcmc_scatter` function (with optional argument `np`) we can look at a single bivariate plot to investigate it more closely. For hierarchical models, a good place to start is to plot a "local" parameter (`theta[j]`) against a "global" scale parameter on which it depends (`tau`). We will also use the `transformations` argument to look at the log of `tau`, as this is what Stan is doing under the hood for parameters like `tau` that have a lower bound of zero. That is, even though the draws for `tau` returned from Stan are all positive, the parameter space that the Markov chains actual explore is unconstrained. Transforming `tau` is not strictly necessary for the plot (often the plot is still useful without it) but plotting in the unconstrained is often even more informative. First the plot for the centered parameterization: ```{r mcmc_scatter-1} # assign to an object so we can reuse later scatter_theta_cp <- mcmc_scatter( posterior_cp, pars = c("theta[1]", "tau"), transform = list(tau = "log"), # can abbrev. 'transformations' np = np_cp, size = 1 ) scatter_theta_cp ``` The shape of this bivariate distribution resembles a funnel (or tornado). This one in particular is essentially the same as an example referred to as Neal's funnel (details in the Stan manual) and it is a clear indication that the Markov chains are struggling to explore the tip of the funnel, which is narrower than the rest of the space. The main problem is that large steps are required to explore the less narrow regions efficiently, but those steps become too large for navigating the narrow region. The required step size is connected to the value of `tau`. When `tau` is large it allows for large variation in `theta` (and requires large steps) while small `tau` requires small steps in `theta`. The non-centered parameterization avoids this by sampling the `eta` parameter which, unlike `theta`, is _a priori independent_ of `tau`. Then `theta` is computed deterministically from the parameters `eta`, `mu` and `tau` afterwards. Here's the same plot as above, but with `eta[1]` from non-centered parameterization instead of `theta[1]` from the centered parameterization: ```{r mcmc_scatter-2} scatter_eta_ncp <- mcmc_scatter( posterior_ncp, pars = c("eta[1]", "tau"), transform = list(tau = "log"), np = np_ncp, size = 1 ) scatter_eta_ncp ``` We can see that the funnel/tornado shape is replaced by a somewhat Gaussian blob/cloud and the divergences go away. [Gabry et al. (2019)](#gabry2019) has further discussion of this example. Ultimately we only care about `eta` insofar as it enables the Markov chains to better explore the posterior, so let's directly examine how much more exploration was possible after the reparameterization. For the non-centered parameterization we can make the same scatterplot but use the values of `theta[1] = mu + eta[1] * tau` instead of `eta[1]`. Below is a side by side comparison with the scatterplot of `theta[1]` vs `log(tau)` from the centered parameterization that we made above. We will also force the plots to have the same $y$-axis limits, which will make the most important difference much more apparent: ```{r mcmc_scatter-3} # A function we'll use several times to plot comparisons of the centered # parameterization (cp) and the non-centered parameterization (ncp). See # help("bayesplot_grid") for details on the bayesplot_grid function used here. compare_cp_ncp <- function(cp_plot, ncp_plot, ncol = 2, ...) { bayesplot_grid( cp_plot, ncp_plot, grid_args = list(ncol = ncol), subtitles = c("Centered parameterization", "Non-centered parameterization"), ... ) } scatter_theta_ncp <- mcmc_scatter( posterior_ncp, pars = c("theta[1]", "tau"), transform = list(tau = "log"), np = np_ncp, size = 1 ) compare_cp_ncp(scatter_theta_cp, scatter_theta_ncp, ylim = c(-8, 4)) ``` Once we transform the `eta` values into `theta` values we actually see an even more pronounced funnel/tornado shape than we have with the centered parameterization. But this is precisely what we want! The non-centered parameterization allowed us to obtain draws from the funnel distribution without having to directly navigate the curvature of the funnel. With the centered parameterization the chains never could make it into the neck of funnel and we see a clustering of divergences and no draws in the tail of the distribution. #### mcmc_trace Another useful diagnostic plot is the trace plot, which is a time series plot of the Markov chains. That is, a trace plot shows the evolution of parameter vector over the iterations of one or many Markov chains. The `np` argument to the `mcmc_trace` function can be used to add a rug plot of the divergences to a trace plot of parameter draws. Typically we can see that at least one of the chains is getting stuck wherever there is a cluster of many red marks. Here is the trace plot for the `tau` parameter from the centered parameterization: ```{r mcmc_trace} color_scheme_set("mix-brightblue-gray") mcmc_trace(posterior_cp, pars = "tau", np = np_cp) + xlab("Post-warmup iteration") ``` The first thing to note is that all chains seem to be exploring the same region of parameter values, which is a good sign. But the plot is too crowded to help us diagnose divergences. We may however zoom in to investigate, using the `window` argument: ```{r echo=FALSE} #A check that the chosen window still relevant n_divergent_in_window <- np_cp %>% filter(Parameter == "divergent__" & Value == 1 & Iteration >= 300 & Iteration <= 500) %>% nrow() if(n_divergent_in_window < 6) { divergences <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% select(Iteration) %>% get("Iteration", .) %>% sort() %>% paste(collapse = ",") stop(paste("Too few divergences in the selected window for traceplot zoom. Change the window or the random seed.\nDivergences happened at: ", divergences)) } ``` ```{r mcmc_trace_zoom} mcmc_trace(posterior_cp, pars = "tau", np = np_cp, window = c(300,500)) + xlab("Post-warmup iteration") ``` What we see here is that chains can get stuck as `tau` approaches zero and spend substantial time in the same region of the parameter space. This is just another indication that there is problematic geometry at $\tau \simeq 0$ -- healthy chains jump up and down frequently. #### mcmc_nuts_divergence To understand how the divergences interact with the model globally, we can use the `mcmc_nuts_divergence` function: ```{r mcmc_nuts_divergence} color_scheme_set("red") mcmc_nuts_divergence(np_cp, lp_cp) ``` In the top panel we see the distribution of the log-posterior when there was no divergence vs the distribution when there was a divergence. Divergences often indicate that some part of the posterior isn't being explored and the plot confirms that `lp|Divergence` indeed has lighter tails than `lp|No divergence`. The bottom panel shows the same thing but instead of the log-posterior the NUTS acceptance statistic is shown. Specifying the optional `chain` argument will overlay the plot just for a particular Markov chain on the plot for all chains combined: ```{r mcmc_nuts_divergence-chain} mcmc_nuts_divergence(np_cp, lp_cp, chain = 4) ``` For the non-centered parameterization we may get a few warnings about divergences but if we do we'll have far fewer of them to worry about. ```{r mcmc_nuts_divergence-2} mcmc_nuts_divergence(np_ncp, lp_ncp) ``` If there are only a few divergences we can often get rid of them by increasing the target acceptance rate (`adapt_delta`, the upper limit is 1), which has the effect of lowering the step size used by the sampler and allowing the Markov chains to explore more complicated curvature in the target distribution. ```{r fit-adapt-delta, results='hide', message=FALSE} fit_cp_2 <- sampling(schools_mod_cp, data = schools_dat, control = list(adapt_delta = 0.999), seed = 978245244) fit_ncp_2 <- sampling(schools_mod_ncp, data = schools_dat, control = list(adapt_delta = 0.999), seed = 843256842) ``` ```{r echo=FALSE, warning=FALSE} # On rare occasions, the fits may not be illustrative. Currently the seed is fixed, but if something in Stan changes and the fixed seeds produce unexpected results (which should be rare), we want to know. n_divergent_cp_2 <- fit_cp_2 %>% nuts_params() %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() n_divergent_ncp_2 <- fit_ncp_2 %>% nuts_params() %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() if(n_divergent_cp_2 <= 0) { stop("No divergences in CP with increased adapt.delta. Change seed?") } if(n_divergent_ncp_2 > 0) { stop("Divergences in the NCP model. Fix a bug / change seed?") } ``` For the first model and this particular data, increasing `adapt_delta` will not solve the problem and a reparameterization is required. ```{r mcmc_nuts_divergence-3} mcmc_nuts_divergence(nuts_params(fit_cp_2), log_posterior(fit_cp_2)) mcmc_nuts_divergence(nuts_params(fit_ncp_2), log_posterior(fit_ncp_2)) ``` ### Energy and Bayesian fraction of missing information The `mcmc_nuts_energy` function creates plots similar to those presented in Betancourt (2017). While `mcmcm_nuts_divergence` can identify light tails and incomplete exploration of the target distribution, the `mcmc_nuts_energy` function can identify overly heavy tails that are also challenging for sampling. Informally, the energy diagnostic for HMC (and the related energy-based Bayesian fraction of missing information) quantifies the heaviness of the tails of the posterior distribution. #### mcmc_nuts_energy The plot created by `mcmc_nuts_energy` shows overlaid histograms of the (centered) marginal energy distribution $\pi_E$ and the first-differenced distribution $\pi_{\Delta E}$, ```{r mcmc_nuts_energy-1, message=FALSE} color_scheme_set("red") mcmc_nuts_energy(np_cp) ``` The two histograms ideally look the same (Betancourt, 2017), which is only the case for the non-centered parameterization (right): ```{r mcmc_nuts_energy-3, message=FALSE, fig.width=8} compare_cp_ncp( mcmc_nuts_energy(np_cp, binwidth = 1/2), mcmc_nuts_energy(np_ncp, binwidth = 1/2) ) ``` The difference between the parameterizations is even more apparent if we force the step size to a smaller value and help the chains explore more of the posterior: ```{r mcmc_nuts_energy-4, message=FALSE, fig.width=8} np_cp_2 <- nuts_params(fit_cp_2) np_ncp_2 <- nuts_params(fit_ncp_2) compare_cp_ncp( mcmc_nuts_energy(np_cp_2), mcmc_nuts_energy(np_ncp_2) ) ``` See Betancourt (2017) for more on this particular example as well as the general theory behind the energy plots.
    ## General MCMC diagnostics A Markov chain generates draws from the target distribution only after it has converged to an equilibrium. Unfortunately, this is only guaranteed in the limit in theory. In practice, diagnostics must be applied to monitor whether the Markov chain(s) have converged. The __bayesplot__ package provides various plotting functions for visualizing Markov chain Monte Carlo (MCMC) diagnostics after fitting a Bayesian model. MCMC draws from any package can be used, although there are a few diagnostic plots that we will see later in this vignette that are specifically intended to be used for [Stan](https://mc-stan.org/) models (or models fit using the same algorithms as Stan). **Documentation:** * `help("MCMC-diagnostics")` * [mc-stan.org/bayesplot/reference/MCMC-diagnostics](https://mc-stan.org/bayesplot/reference/MCMC-diagnostics.html) ------ ### Rhat: potential scale reduction statistic One way to monitor whether a chain has converged to the equilibrium distribution is to compare its behavior to other randomly initialized chains. This is the motivation for the potential scale reduction statistic, split-$\hat{R}$. The split-$\hat{R}$ statistic measures the ratio of the average variance of draws within each chain to the variance of the pooled draws across chains; if all chains are at equilibrium, these will be the same and $\hat{R}$ will be one. If the chains have not converged to a common distribution, the $\hat{R}$ statistic will be greater than one (see Gelman et al. 2013, Stan Development Team 2018). The **bayesplot** package provides the functions `mcmc_rhat` and `mcmc_rhat_hist` for visualizing $\hat{R}$ estimates. First we'll quickly fit one of the models above again, this time intentionally using too few MCMC iterations and allowing more dispersed initial values. This should lead to some high $\hat{R}$ values. ```{r fit_cp_bad_rhat, results='hide'} fit_cp_bad_rhat <- sampling(schools_mod_cp, data = schools_dat, iter = 50, init_r = 10, seed = 671254821) ``` **bayesplot** provides a generic `rhat` extractor function, currently with methods defined for models fit using the **rstan**, **rstanarm** and **brms** packages. But regardless of how you fit your model, all **bayesplot** needs is a vector of $\hat{R}$ values. ```{r print-rhats} rhats <- rhat(fit_cp_bad_rhat) print(rhats) ``` #### mcmc_rhat, mcmc_rhat_hist We can visualize the $\hat{R}$ values with the `mcmc_rhat` function: ```{r echo=FALSE} #Check that the fit we got is a sensible example if(all(rhats < 1.3)) { stop("All rhats for the short chain run are low. Change seed?") } ``` ```{r mcmc_rhat-1} color_scheme_set("brightblue") # see help("color_scheme_set") mcmc_rhat(rhats) ``` In the plot, the points representing the $\hat{R}$ values are colored based on whether they are less than $1.05$, between $1.05$ and $1.1$, or greater than $1.1$. There is no theoretical reason to trichotomize $\hat{R}$ values using these cutoffs, so keep in mind that this is just a heuristic. The $y$-axis text is off by default for this plot because it's only possible to see the labels clearly for models with very few parameters. We can see the names of the parameters with the concerning $\hat{R}$ values using the `yaxis_text` convenience function (which passes arguments like `hjust` to `ggplot2::element_text`): ```{r mcmc_rhat-2} mcmc_rhat(rhats) + yaxis_text(hjust = 1) ``` If we look at the same model fit using longer Markov chains we should see all $\hat{R} < 1.1$, and all points in the plot the same (light) color: ```{r mcmc_rhat-3} mcmc_rhat(rhat = rhat(fit_cp)) + yaxis_text(hjust = 0) ``` We can see the same information shown by `mcmc_rhat` but in histogram form using the `mcmc_rhat_hist` function. See the **Examples** section in `help("mcmc_rhat_hist")` for examples. ### Effective sample size The effective sample size is an estimate of the number of independent draws from the posterior distribution of the estimand of interest. The $n_{eff}$ metric used in Stan is based on the ability of the draws to estimate the true mean value of the parameter, which is related to (but not necessarily equivalent to) estimating other functions of the draws. Because the draws within a Markov chain are _not_ independent if there is autocorrelation, the effective sample size, $n_{eff}$, is usually smaller than the total sample size, $N$ (although it may be larger in some cases[^1]). The larger the ratio of $n_{eff}$ to $N$ the better (see Gelman et al. 2013, Stan Development Team 2018 for more details) . [^1]: $n_{eff} > N$ indicates that the mean estimate of the parameter computed from Stan draws approaches the true mean faster than the mean estimate computed from independent samples from the true posterior (the estimate from Stan has smaller variance). This is possible when the draws are anticorrelated - draws above the mean tend to be well matched with draws below the mean. Other functions computed from draws (quantiles, posterior intervals, tail probabilities) may not necessarily approach the true posterior faster. Google "antithetic sampling" or visit [the relevant forum thread](https://discourse.mc-stan.org/t/n-eff-bda3-vs-stan/2608/19) for some further explanation. The **bayesplot** package provides a generic `neff_ratio` extractor function, currently with methods defined for models fit using the **rstan**, **rstanarm** and **brms** packages. But regardless of how you fit your model, all **bayesplot** needs is a vector of $n_{eff}/N$ values. The `mcmc_neff` and `mcmc_neff_hist` can then be used to plot the ratios. #### mcmc_neff, mcmc_neff_hist ```{r print-neff-ratios} ratios_cp <- neff_ratio(fit_cp) print(ratios_cp) mcmc_neff(ratios_cp, size = 2) ``` In the plot, the points representing the values of $n_{eff}/N$ are colored based on whether they are less than $0.1$, between $0.1$ and $0.5$, or greater than $0.5$. These particular values are arbitrary in that they have no particular theoretical meaning, but a useful heuristic is to worry about any $n_{eff}/N$ less than $0.1$. One important thing to keep in mind is that these ratios will depend not only on the model being fit but also on the particular MCMC algorithm used. One reason why we have such high ratios of $n_{eff}$ to $N$ is that the No-U-Turn sampler used by **rstan** generally produces draws from the posterior distribution with much lower autocorrelations compared to draws obtained using other MCMC algorithms (e.g., Gibbs). Even for models fit using **rstan** the parameterization can make a big difference. Here are the $n_{eff}/N$ plots for `fit_cp` and `fit_ncp` side by side. ```{r mcmc_neff-compare} neff_cp <- neff_ratio(fit_cp, pars = c("theta", "mu", "tau")) neff_ncp <- neff_ratio(fit_ncp, pars = c("theta", "mu", "tau")) compare_cp_ncp(mcmc_neff(neff_cp), mcmc_neff(neff_ncp), ncol = 1) ``` Because of the difference in parameterization, the effective sample sizes are much better for the second model, the non-centered parameterization. ### Autocorrelation As mentioned above, $n_{eff}/N$ decreases as autocorrelation becomes more extreme. We can visualize the autocorrelation using the `mcmc_acf` (line plot) or `mcmc_acf_bar` (bar plot) functions. For the selected parameters, these functions show the autocorrelation for each Markov chain separately up to a user-specified number of lags. Positive autocorrelation is bad (it means the chain tends to stay in the same area between iterations) and you want it to drop quickly to zero with increasing lag. Negative autocorrelation is possible and it is useful as it indicates fast convergence of sample mean towards true mean. #### `mcmc_acf`, `mcmc_acf_bar` Here we can again see a difference when comparing the two parameterizations of the same model. For model 1, $\theta_1$ is the primitive parameter for school 1, whereas for the non-centered parameterization in model 2 the primitive parameter is $\eta_1$ (and $\theta_1$ is later constructed from $\eta_1$, $\mu$, and $\tau$): ```{r mcmc_acf, out.width = "70%"} compare_cp_ncp( mcmc_acf(posterior_cp, pars = "theta[1]", lags = 10), mcmc_acf(posterior_ncp, pars = "eta[1]", lags = 10) ) ```
    ## References Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/abs/1701.02434 Betancourt, M. (2016). Diagnosing suboptimal cotangent disintegrations in Hamiltonian Monte Carlo. https://arxiv.org/abs/1604.00695 Betancourt, M. and Girolami, M. (2013). Hamiltonian Monte Carlo for hierarchical models. https://arxiv.org/abs/1312.0906 Gabry, J., and Goodrich, B. (2018). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.17.4. https://mc-stan.org/rstanarm/ Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. \doi:10.1111/rssa.12378. ([journal version](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssa.12378), [arXiv preprint](https://arxiv.org/abs/1709.01449), [code on GitHub](https://github.com/jgabry/bayes-vis-paper)) Gelman, A. and Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. *Statistical Science*. 7(4): 457--472. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third edition. Hoffman, M. D. and Gelman, A. (2014). The No-U-Turn Sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. *Journal of Machine Learning Research*. 15:1593--1623. Rubin, D. B. (1981). Estimation in Parallel Randomized Experiments. *Journal of Educational and Behavioral Statistics*. 6:377--401. Stan Development Team. _Stan Modeling Language Users Guide and Reference Manual_. https://mc-stan.org/users/documentation/ Stan Development Team. (2018). RStan: the R interface to Stan. R package version 2.17.3. https://mc-stan.org/rstan/ bayesplot/inst/doc/visual-mcmc-diagnostics.R0000644000176200001440000002210614563223212020647 0ustar liggesusersparams <- list(EVAL = TRUE) ## ----SETTINGS-knitr, include=FALSE-------------------------------------------- stopifnot(require("knitr")) library("bayesplot") knitr::opts_chunk$set( dev = "png", dpi = 150, fig.asp = 0.618, fig.width = 5, out.width = "60%", fig.align = "center", comment = NA, eval = if (isTRUE(exists("params"))) params$EVAL else FALSE ) ## ----pkgs, include=FALSE------------------------------------------------------ library("bayesplot") library("ggplot2") library("rstan") library("dplyr") #Used only for consistency checks rstan_options(auto_write = TRUE) #Helpful throughout development bayesplot_theme_set() ## ----setup, eval=FALSE-------------------------------------------------------- # library("bayesplot") # library("ggplot2") # library("rstan") ## ----schools_dat-------------------------------------------------------------- schools_dat <- list( J = 8, y = c(28, 8, -3, 7, -1, 1, 18, 12), sigma = c(15, 10, 16, 11, 9, 11, 10, 18) ) ## ----compile-models, eval=FALSE----------------------------------------------- # schools_mod_cp <- stan_model("schools_mod_cp.stan") # schools_mod_ncp <- stan_model("schools_mod_ncp.stan") ## ----fit-models-hidden, results='hide', message=FALSE------------------------- fit_cp <- sampling(schools_mod_cp, data = schools_dat, seed = 803214055, control = list(adapt_delta = 0.9)) fit_ncp <- sampling(schools_mod_ncp, data = schools_dat, seed = 457721433, control = list(adapt_delta = 0.9)) ## ----extract-draws------------------------------------------------------------ # Extract posterior draws for later use posterior_cp <- as.array(fit_cp) posterior_ncp <- as.array(fit_ncp) ## ----available_mcmc-nuts------------------------------------------------------ available_mcmc(pattern = "_nuts_") ## ----extract-nuts-info-------------------------------------------------------- lp_cp <- log_posterior(fit_cp) head(lp_cp) np_cp <- nuts_params(fit_cp) head(np_cp) # for the second model lp_ncp <- log_posterior(fit_ncp) np_ncp <- nuts_params(fit_ncp) ## ----echo=FALSE, warning=FALSE------------------------------------------------ # On rare occasions, the fits may not be illustrative. Currently the seed is # fixed, but if something in Stan changes and the fixed seeds produce unexpected # results (which should be rare), we want to know. n_divergent_cp <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() n_divergent_ncp <- np_ncp %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() if(n_divergent_cp < 10 || n_divergent_cp > 2000) { stop("Unexpected number of divergences in the CP model. Change seed?") } if(n_divergent_ncp > 0) { stop("Divergences in the NCP model. Fix a bug / change seed?") } ## ----mcmc_parcoord-1---------------------------------------------------------- color_scheme_set("darkgray") mcmc_parcoord(posterior_cp, np = np_cp) ## ----mcmc_pairs--------------------------------------------------------------- mcmc_pairs(posterior_cp, np = np_cp, pars = c("mu","tau","theta[1]"), off_diag_args = list(size = 0.75)) ## ----mcmc_scatter-1----------------------------------------------------------- # assign to an object so we can reuse later scatter_theta_cp <- mcmc_scatter( posterior_cp, pars = c("theta[1]", "tau"), transform = list(tau = "log"), # can abbrev. 'transformations' np = np_cp, size = 1 ) scatter_theta_cp ## ----mcmc_scatter-2----------------------------------------------------------- scatter_eta_ncp <- mcmc_scatter( posterior_ncp, pars = c("eta[1]", "tau"), transform = list(tau = "log"), np = np_ncp, size = 1 ) scatter_eta_ncp ## ----mcmc_scatter-3----------------------------------------------------------- # A function we'll use several times to plot comparisons of the centered # parameterization (cp) and the non-centered parameterization (ncp). See # help("bayesplot_grid") for details on the bayesplot_grid function used here. compare_cp_ncp <- function(cp_plot, ncp_plot, ncol = 2, ...) { bayesplot_grid( cp_plot, ncp_plot, grid_args = list(ncol = ncol), subtitles = c("Centered parameterization", "Non-centered parameterization"), ... ) } scatter_theta_ncp <- mcmc_scatter( posterior_ncp, pars = c("theta[1]", "tau"), transform = list(tau = "log"), np = np_ncp, size = 1 ) compare_cp_ncp(scatter_theta_cp, scatter_theta_ncp, ylim = c(-8, 4)) ## ----mcmc_trace--------------------------------------------------------------- color_scheme_set("mix-brightblue-gray") mcmc_trace(posterior_cp, pars = "tau", np = np_cp) + xlab("Post-warmup iteration") ## ----echo=FALSE--------------------------------------------------------------- #A check that the chosen window still relevant n_divergent_in_window <- np_cp %>% filter(Parameter == "divergent__" & Value == 1 & Iteration >= 300 & Iteration <= 500) %>% nrow() if(n_divergent_in_window < 6) { divergences <- np_cp %>% filter(Parameter == "divergent__" & Value == 1) %>% select(Iteration) %>% get("Iteration", .) %>% sort() %>% paste(collapse = ",") stop(paste("Too few divergences in the selected window for traceplot zoom. Change the window or the random seed.\nDivergences happened at: ", divergences)) } ## ----mcmc_trace_zoom---------------------------------------------------------- mcmc_trace(posterior_cp, pars = "tau", np = np_cp, window = c(300,500)) + xlab("Post-warmup iteration") ## ----mcmc_nuts_divergence----------------------------------------------------- color_scheme_set("red") mcmc_nuts_divergence(np_cp, lp_cp) ## ----mcmc_nuts_divergence-chain----------------------------------------------- mcmc_nuts_divergence(np_cp, lp_cp, chain = 4) ## ----mcmc_nuts_divergence-2--------------------------------------------------- mcmc_nuts_divergence(np_ncp, lp_ncp) ## ----fit-adapt-delta, results='hide', message=FALSE--------------------------- fit_cp_2 <- sampling(schools_mod_cp, data = schools_dat, control = list(adapt_delta = 0.999), seed = 978245244) fit_ncp_2 <- sampling(schools_mod_ncp, data = schools_dat, control = list(adapt_delta = 0.999), seed = 843256842) ## ----echo=FALSE, warning=FALSE------------------------------------------------ # On rare occasions, the fits may not be illustrative. Currently the seed is fixed, but if something in Stan changes and the fixed seeds produce unexpected results (which should be rare), we want to know. n_divergent_cp_2 <- fit_cp_2 %>% nuts_params() %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() n_divergent_ncp_2 <- fit_ncp_2 %>% nuts_params() %>% filter(Parameter == "divergent__" & Value == 1) %>% nrow() if(n_divergent_cp_2 <= 0) { stop("No divergences in CP with increased adapt.delta. Change seed?") } if(n_divergent_ncp_2 > 0) { stop("Divergences in the NCP model. Fix a bug / change seed?") } ## ----mcmc_nuts_divergence-3--------------------------------------------------- mcmc_nuts_divergence(nuts_params(fit_cp_2), log_posterior(fit_cp_2)) mcmc_nuts_divergence(nuts_params(fit_ncp_2), log_posterior(fit_ncp_2)) ## ----mcmc_nuts_energy-1, message=FALSE---------------------------------------- color_scheme_set("red") mcmc_nuts_energy(np_cp) ## ----mcmc_nuts_energy-3, message=FALSE, fig.width=8--------------------------- compare_cp_ncp( mcmc_nuts_energy(np_cp, binwidth = 1/2), mcmc_nuts_energy(np_ncp, binwidth = 1/2) ) ## ----mcmc_nuts_energy-4, message=FALSE, fig.width=8-------------------------- np_cp_2 <- nuts_params(fit_cp_2) np_ncp_2 <- nuts_params(fit_ncp_2) compare_cp_ncp( mcmc_nuts_energy(np_cp_2), mcmc_nuts_energy(np_ncp_2) ) ## ----fit_cp_bad_rhat, results='hide'------------------------------------------ fit_cp_bad_rhat <- sampling(schools_mod_cp, data = schools_dat, iter = 50, init_r = 10, seed = 671254821) ## ----print-rhats-------------------------------------------------------------- rhats <- rhat(fit_cp_bad_rhat) print(rhats) ## ----echo=FALSE--------------------------------------------------------------- #Check that the fit we got is a sensible example if(all(rhats < 1.3)) { stop("All rhats for the short chain run are low. Change seed?") } ## ----mcmc_rhat-1-------------------------------------------------------------- color_scheme_set("brightblue") # see help("color_scheme_set") mcmc_rhat(rhats) ## ----mcmc_rhat-2-------------------------------------------------------------- mcmc_rhat(rhats) + yaxis_text(hjust = 1) ## ----mcmc_rhat-3-------------------------------------------------------------- mcmc_rhat(rhat = rhat(fit_cp)) + yaxis_text(hjust = 0) ## ----print-neff-ratios-------------------------------------------------------- ratios_cp <- neff_ratio(fit_cp) print(ratios_cp) mcmc_neff(ratios_cp, size = 2) ## ----mcmc_neff-compare-------------------------------------------------------- neff_cp <- neff_ratio(fit_cp, pars = c("theta", "mu", "tau")) neff_ncp <- neff_ratio(fit_ncp, pars = c("theta", "mu", "tau")) compare_cp_ncp(mcmc_neff(neff_cp), mcmc_neff(neff_ncp), ncol = 1) ## ----mcmc_acf, out.width = "70%"---------------------------------------------- compare_cp_ncp( mcmc_acf(posterior_cp, pars = "theta[1]", lags = 10), mcmc_acf(posterior_ncp, pars = "eta[1]", lags = 10) ) bayesplot/inst/doc/graphical-ppcs.html0000644000176200001440000201176314563223020017570 0ustar liggesusers Graphical posterior predictive checks using the bayesplot package

    Graphical posterior predictive checks using the bayesplot package

    Jonah Gabry

    2024-02-14

    Introduction

    This vignette focuses on graphical posterior predictive checks (PPC). Plots of parameter estimates from MCMC draws are covered in the separate vignette Plotting MCMC draws, and MCMC diagnostics are covered in the Visual MCMC diagnostics vignette.

    Graphical posterior predictive checks (PPCs)

    The bayesplot package provides various plotting functions for graphical posterior predictive checking, that is, creating graphical displays comparing observed data to simulated data from the posterior predictive distribution (Gabry et al, 2019).

    The idea behind posterior predictive checking is simple: if a model is a good fit then we should be able to use it to generate data that looks a lot like the data we observed. To generate the data used for posterior predictive checks (PPCs) we simulate from the posterior predictive distribution. This is the distribution of the outcome variable implied by a model after using the observed data \(y\) (a vector of \(N\) outcome values) to update our beliefs about unknown model parameters \(\theta\). The posterior predictive distribution for observation \(\widetilde{y}\) can be written as \[p(\widetilde{y} \,|\, y) = \int p(\widetilde{y} \,|\, \theta) \, p(\theta \,|\, y) \, d\theta.\] Typically we will also condition on \(X\) (a matrix of predictor variables).

    For each draw (simulation) \(s = 1, \ldots, S\) of the parameters from the posterior distribution, \(\theta^{(s)} \sim p(\theta \,|\, y)\), we draw an entire vector of \(N\) outcomes \(\widetilde{y}^{(s)}\) from the posterior predictive distribution by simulating from the data model conditional on parameters \(\theta^{(s)}\). The result is an \(S \times N\) matrix of draws \(\widetilde{y}\).

    When simulating from the posterior predictive distribution we can use either the same values of the predictors \(X\) that we used when fitting the model or new observations of those predictors. When we use the same values of \(X\) we denote the resulting simulations by \(y^{rep}\), as they can be thought of as replications of the outcome \(y\) rather than predictions for future observations (\(\widetilde{y}\) using predictors \(\widetilde{X}\)). This corresponds to the notation from Gelman et al. (2013) and is the notation used throughout the package documentation.

    Using the replicated datasets drawn from the posterior predictive distribution, the functions in the bayesplot package create various graphical displays comparing the observed data \(y\) to the replications. The names of the bayesplot plotting functions for posterior predictive checking all have the prefix ppc_.

    Setup

    In addition to bayesplot we’ll load the following packages:

    • ggplot2, in case we want to customize the ggplot objects created by bayesplot
    • rstanarm, for fitting the example models used throughout the vignette
    library("bayesplot")
library("ggplot2")
library("rstanarm")

    Example models

    To demonstrate some of the various PPCs that can be created with the bayesplot package we’ll use an example of comparing Poisson and Negative binomial regression models from one of the rstanarm package vignettes (Gabry and Goodrich, 2017).

    We want to make inferences about the efficacy of a certain pest management system at reducing the number of roaches in urban apartments. […] The regression predictors for the model are the pre-treatment number of roaches roach1, the treatment indicator treatment, and a variable senior indicating whether the apartment is in a building restricted to elderly residents. Because the number of days for which the roach traps were used is not the same for all apartments in the sample, we include it as an exposure […].

    First we fit a Poisson regression model with outcome variable y representing the roach count in each apartment at the end of the experiment.

    head(roaches) # see help("rstanarm-datasets")
        y roach1 treatment senior exposure2
1 153 308.00         1      0  0.800000
2 127 331.25         1      0  0.600000
3   7   1.67         1      0  1.000000
4   7   3.00         1      0  1.000000
5   0   2.00         1      0  1.142857
6   0   0.00         1      0  1.000000
    roaches$roach100 <- roaches$roach1 / 100 # pre-treatment number of roaches (in 100s)
    # using rstanarm's default priors. For details see the section on default
# weakly informative priors at https://mc-stan.org/rstanarm/articles/priors.html
fit_poisson <- stan_glm(
  y ~ roach100 + treatment + senior,
  offset = log(exposure2),
  family = poisson(link = "log"),
  data = roaches,
  seed = 1111, 
  refresh = 0 # suppresses all output as of v2.18.1 of rstan
)
    print(fit_poisson)
    stan_glm
 family:       poisson [log]
 formula:      y ~ roach100 + treatment + senior
 observations: 262
 predictors:   4
------
            Median MAD_SD
(Intercept)  3.1    0.0  
roach100     0.7    0.0  
treatment   -0.5    0.0  
senior      -0.4    0.0  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

    We’ll also fit the negative binomial model that we’ll compare to the Poisson:

    fit_nb <- update(fit_poisson, family = "neg_binomial_2")
    print(fit_nb)
    stan_glm
 family:       neg_binomial_2 [log]
 formula:      y ~ roach100 + treatment + senior
 observations: 262
 predictors:   4
------
            Median MAD_SD
(Intercept)  2.8    0.2  
roach100     1.3    0.3  
treatment   -0.8    0.2  
senior      -0.3    0.3  

Auxiliary parameter(s):
                      Median MAD_SD
reciprocal_dispersion 0.3    0.0   

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

    Defining y and yrep

    In order to use the PPC functions from the bayesplot package we need a vector y of outcome values,

    y <- roaches$y

    and a matrix yrep of draws from the posterior predictive distribution,

    yrep_poisson <- posterior_predict(fit_poisson, draws = 500)
yrep_nb <- posterior_predict(fit_nb, draws = 500)
dim(yrep_poisson)
    [1] 500 262
    dim(yrep_nb)
    [1] 500 262

    Each row of the matrix is a draw from the posterior predictive distribution, i.e. a vector with one element for each of the data points in y.

    Since we fit the models using rstanarm we used its special posterior_predict function, but if we were using a model fit with the rstan package we could create yrep in the generated quantities block of the Stan program or by doing simulations in R after fitting the model. Draws from the posterior predictive distribution can be used with bayesplot regardless of whether or not the model was fit using an interface to Stan. bayesplot just requires a yrep matrix that has number_of_draws rows and number_of_observations columns.


    Histograms and density estimates

    ppc_dens_overlay

    The first PPC we’ll look at is a comparison of the distribution of y and the distributions of some of the simulated datasets (rows) in the yrep matrix.

    color_scheme_set("brightblue")
ppc_dens_overlay(y, yrep_poisson[1:50, ])

    In the plot above, the dark line is the distribution of the observed outcomes y and each of the 50 lighter lines is the kernel density estimate of one of the replications of y from the posterior predictive distribution (i.e., one of the rows in yrep). This plot makes it easy to see that this model fails to account for the large proportion of zeros in y. That is, the model predicts fewer zeros than were actually observed.

    To see the discrepancy at the lower values of more clearly we can use the xlim function from ggplot2 to restrict the range of the x-axis:

    ppc_dens_overlay(y, yrep_poisson[1:50, ]) + xlim(0, 150)

    See Figure 6 in Gabry et al. (2019) for another example of using ppc_dens_overlay.

    ppc_hist

    We could see the same thing from a different perspective by looking at separate histograms of y and some of the yrep datasets using the ppc_hist function:

    ppc_hist(y, yrep_poisson[1:5, ])

    The same plot for the negative binomial model looks much different:

    ppc_hist(y, yrep_nb[1:5, ])

    The negative binomial model does better handling the number of zeros in the data, but it occasionally predicts values that are way too large, which is why the x-axes extend to such high values in the plot and make it difficult to read. To see the predictions for the smaller values more clearly we can zoom in:

    ppc_hist(y, yrep_nb[1:5, ], binwidth = 20) + 
  coord_cartesian(xlim = c(-1, 300))


    Distributions of test statistics

    Another way to see that the Poisson model predicts too few zeros is to look at the distribution of the proportion of zeros over the replicated datasets from the posterior predictive distribution in yrep and compare to the proportion of observed zeros in y.

    ppc_stat

    First we define a function that takes a vector as input and returns the proportion of zeros:

    prop_zero <- function(x) mean(x == 0)
prop_zero(y) # check proportion of zeros in y
    [1] 0.3587786

    The stat argument to ppc_stat accepts a function or the name of a function for computing a test statistic from a vector of data. In our case we can specify stat = "prop_zero" since we’ve already defined the prop_zero function, but we also could have used stat = function(x) mean(x == 0).

    ppc_stat(y, yrep_poisson, stat = "prop_zero", binwidth = 0.005)

    The dark line is at the value \(T(y)\), i.e. the value of the test statistic computed from the observed \(y\), in this case prop_zero(y). The lighter area on the left is actually a histogram of the proportion of zeros in in the yrep simulations, but it can be hard to see because almost none of the simulated datasets in yrep have any zeros.

    Here’s the same plot for the negative binomial model:

    ppc_stat(y, yrep_nb, stat = "prop_zero")

    Again we see that the negative binomial model does a much better job predicting the proportion of observed zeros than the Poisson.

    However, if we look instead at the distribution of the maximum value in the replications, we can see that the Poisson model makes more realistic predictions than the negative binomial:

    ppc_stat(y, yrep_poisson, stat = "max")

    ppc_stat(y, yrep_nb, stat = "max")

    ppc_stat(y, yrep_nb, stat = "max", binwidth = 100) + 
  coord_cartesian(xlim = c(-1, 5000))

    See Figure 7 in Gabry et al. (2019) for another example of using ppc_stat.


    Other PPCs and PPCs by group

    There are many additional PPCs available, including plots of predictive intervals, distributions of predictive errors, and more. For links to the documentation for all of the various PPC plots see help("PPC-overview") from R or the online documentation on the Stan website.

    The available_ppc function can also be used to list the names of all PPC plotting functions:

    available_ppc()
    bayesplot PPC module:
  ppc_bars
  ppc_bars_grouped
  ppc_boxplot
  ppc_dens
  ppc_dens_overlay
  ppc_dens_overlay_grouped
  ppc_ecdf_overlay
  ppc_ecdf_overlay_grouped
  ppc_error_binned
  ppc_error_hist
  ppc_error_hist_grouped
  ppc_error_scatter
  ppc_error_scatter_avg
  ppc_error_scatter_avg_grouped
  ppc_error_scatter_avg_vs_x
  ppc_freqpoly
  ppc_freqpoly_grouped
  ppc_hist
  ppc_intervals
  ppc_intervals_grouped
  ppc_km_overlay
  ppc_km_overlay_grouped
  ppc_loo_intervals
  ppc_loo_pit
  ppc_loo_pit_overlay
  ppc_loo_pit_qq
  ppc_loo_ribbon
  ppc_pit_ecdf
  ppc_pit_ecdf_grouped
  ppc_ribbon
  ppc_ribbon_grouped
  ppc_rootogram
  ppc_scatter
  ppc_scatter_avg
  ppc_scatter_avg_grouped
  ppc_stat
  ppc_stat_2d
  ppc_stat_freqpoly
  ppc_stat_freqpoly_grouped
  ppc_stat_grouped
  ppc_violin_grouped

    Many of the available PPCs can also be carried out within levels of a grouping variable. Any function for PPCs by group will have a name ending in _grouped and will accept an additional argument group. The full list of currently available _grouped functions is:

    available_ppc(pattern = "_grouped")
    bayesplot PPC module:
(matching pattern '_grouped') 
  ppc_bars_grouped
  ppc_dens_overlay_grouped
  ppc_ecdf_overlay_grouped
  ppc_error_hist_grouped
  ppc_error_scatter_avg_grouped
  ppc_freqpoly_grouped
  ppc_intervals_grouped
  ppc_km_overlay_grouped
  ppc_pit_ecdf_grouped
  ppc_ribbon_grouped
  ppc_scatter_avg_grouped
  ppc_stat_freqpoly_grouped
  ppc_stat_grouped
  ppc_violin_grouped

    ppc_stat_grouped

    For example, ppc_stat_grouped is the same as ppc_stat except that the test statistic is computed within levels of the grouping variable and a separate plot is made for each level:

    ppc_stat_grouped(y, yrep_nb, group = roaches$treatment, stat = "prop_zero")

    See Figure 8 in Gabry et al. (2019) for another example of using ppc_stat_grouped.


    Providing an interface to bayesplot PPCs from another package

    The bayesplot package provides the S3 generic function pp_check. Authors of R packages for Bayesian inference are encouraged to define methods for the fitted model objects created by their packages. This will hopefully be convenient for both users and developers and contribute to the use of the same naming conventions across many of the R packages for Bayesian data analysis.

    To provide an interface to bayesplot from your package, you can very easily define a pp_check method (or multiple pp_check methods) for the fitted model objects created by your package. All a pp_check method needs to do is provide the y vector and yrep matrix arguments to the various plotting functions included in bayesplot.

    Defining a pp_check method

    Here is an example for how to define a simple pp_check method in a package that creates fitted model objects of class "foo". We will define a method pp_check.foo that extracts the data y and the draws from the posterior predictive distribution yrep from an object of class "foo" and then calls one of the plotting functions from bayesplot.

    Suppose that objects of class "foo" are lists with named components, two of which are y and yrep. Here’s a simple method pp_check.foo that offers the user the option of two different plots:

    # @param object An object of class "foo".
# @param type The type of plot.
# @param ... Optional arguments passed on to the bayesplot plotting function.
pp_check.foo <- function(object, type = c("multiple", "overlaid"), ...) {
  type <- match.arg(type)
  y <- object[["y"]]
  yrep <- object[["yrep"]]
  stopifnot(nrow(yrep) >= 50)
  samp <- sample(nrow(yrep), size = ifelse(type == "overlaid", 50, 5))
  yrep <- yrep[samp, ]
  
  if (type == "overlaid") {
    ppc_dens_overlay(y, yrep, ...) 
  } else {
    ppc_hist(y, yrep, ...)
  }
}

    To try out pp_check.foo we can just make a list with y and yrep components and give it class foo:

    x <- list(y = rnorm(200), yrep = matrix(rnorm(1e5), nrow = 500, ncol = 200))
class(x) <- "foo"
    color_scheme_set("purple")
pp_check(x, type = "multiple", binwidth = 0.3)

    color_scheme_set("darkgray")
pp_check(x, type = "overlaid")

    Examples of pp_check methods in other packages

    Several packages currently use this approach to provide an interface to bayesplot’s graphical posterior predictive checks. See, for example, the pp_check methods in the rstanarm and brms packages.


    References

    Buerkner, P. (2017). brms: Bayesian Regression Models using Stan. R package version 1.7.0. https://CRAN.R-project.org/package=brms

    Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression Modeling via Stan. R package version 2.15.3. https://mc-stan.org/rstanarm/, https://CRAN.R-project.org/package=rstanarm

    Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. :10.1111/rssa.12378. (journal version, arXiv preprint, code on GitHub)

    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC Press, London, third edition.

    Stan Development Team. Stan Modeling Language Users Guide and Reference Manual. https://mc-stan.org/users/documentation/

    bayesplot/inst/CITATION0000644000176200001440000000201014127610043014355 0ustar liggesusersyear <- sub("-.*", "", meta$Date) note <- sprintf("R package version %s", meta$Version) bibentry(bibtype = "Misc", title = "bayesplot: Plotting for Bayesian Models", author = c(person("Jonah", "Gabry"), person("Tristan", "Mahr") ), year = year, note = note, url = c("https://mc-stan.org/bayesplot/"), header = "To cite the bayesplot R package:" ) bibentry(bibtype = "Article", title = "Visualization in Bayesian workflow", author = c(person("Jonah", "Gabry"), person("Daniel", "Simpson"), person("Aki", "Vehtari"), person("Michael", "Betancourt"), person("Andrew", "Gelman")), year = "2019", journal = "J. R. Stat. Soc. A", volume = 182, issue = 2, pages = "389-402", doi = "10.1111/rssa.12378", header = "To cite the bayesplot paper 'Visualization in Bayesian workflow':" )

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