shinystan/0000755000176200001440000000000013271757566012315 5ustar liggesusersshinystan/inst/0000755000176200001440000000000013271714530013252 5ustar liggesusersshinystan/inst/ShinyStan/0000755000176200001440000000000013137732221015170 5ustar liggesusersshinystan/inst/ShinyStan/ui.R0000644000176200001440000000300713035516434015733 0ustar liggesuserssource("global_utils.R", local = TRUE) source("ui_utils.R", local = TRUE) # Begin shinyUI ----------------------------------------------------------- # _________________________________________________________________________ tagList( tags$noscript( style = "color: orange; font-size: 30px; text-align: center;", "Please enable JavaScript to use ShinyStan." ), shinyjs::useShinyjs(), includeCSS("css/ShinyStan.css"), navbarPage( save_and_close_button(), # title = NULL id = "nav", position = "fixed-top", collapsible = TRUE, theme = shinythemes::shinytheme("flatly"), windowTitle = "ShinyStan", #### HOME #### tabPanel( title = strong(style = "color: #B2011D;", "ShinyStan"), value = "home", source_ui("PAGE_home.R") ), #### DIAGNOSE #### tabPanel( title = "Diagnose", icon = icon("medkit"), source_ui("PAGE_diagnose.R") ), #### ESTIMATE #### tabPanel( title = "Estimate", icon = icon("stats", lib = "glyphicon"), withMathJax(), source_ui("PAGE_estimate.R") ), #### EXPLORE #### tabPanel( title = "Explore", icon = icon("eye-open", lib = "glyphicon"), source_ui("PAGE_explore.R") ), #### More #### source_ui("PAGE_more_menu.R") ) # End navbarPage ) # End tagList # End shinyUI ------------------------------------------------------------- # ------------------------------------------------------------------------- shinystan/inst/ShinyStan/server.R0000644000176200001440000000744513035516434016636 0ustar liggesusersif (exists(".SHINYSTAN_OBJECT")) { object <- .SHINYSTAN_OBJECT } else { object <- get(".SHINYSTAN_OBJECT", envir = shinystan:::.sso_env) } path_to_extract_sso <- file.path("server_files","utilities","extract_sso.R") server_files <- list.files("server_files", full.names = TRUE, recursive = TRUE) SERVER_FILES <- server_files[!server_files %in% path_to_extract_sso] source("global_utils.R", local = TRUE) source("server_utils.R", local = TRUE) source(path_to_extract_sso, local = TRUE) # BEGIN server ------------------------------------------------------ # ___________________________________________________________________ function(input, output, session) { # If not running on server then automatically stop app whenever browser tab # (or any session) is closed if (!nzchar(Sys.getenv("SHINY_PORT"))) { session$onSessionEnded(function() stopApp(object)) } # Stop the app when "Save & Close" button is clicked observeEvent( input$save_and_close_button, stopApp(object) ) # Source all files from server_files directory and subdirectories for (f in SERVER_FILES) source(f, local = TRUE) # Link to pages from home page table of contents toc_entries <- c("Estimate", "Diagnose", "Explore", "Model Code") observe({ local({ lapply(toc_entries, function(x) { id <- paste0("toc_", if (x == "Model Code") "more" else tolower(x)) shinyjs::onclick(id, updateTabsetPanel(session, "nav", selected = x)) }) }) }) # Toggle options dropdowns options_trigger_ids <- c("table", "multiparam", "autocorr", "rhat_warnings", "bivariate", "trivariate", "density", "hist") observe({ lapply(seq_along(options_trigger_ids), function(j) { shinyjs::onclick( id = paste0(options_trigger_ids[j], "_options_show"), shinyjs::toggle( id = paste0(options_trigger_ids[j], "_options"), anim = TRUE, animType = "slide", time = 0.4 ) ) }) }) # Enable/disable individual options density_trigger_ids <- c("point_est", "ci", "x_breaks", "fill_color", "line_color") observe({ lapply(seq_along(density_trigger_ids), function(j) { shinyjs::toggleState( id = paste0("dens_", density_trigger_ids[j]), condition = input$dens_chain_split == "Together" ) }) shinyjs::toggleState(id = "ac_flip", condition = input$ac_combine == FALSE) }) # Links to glossary observe({ shinyjs::onclick( "open_glossary_from_table", updateTabsetPanel(session, "nav", selected = "Glossary") ) shinyjs::onclick( "open_glossary_from_nuts_table", updateTabsetPanel(session, "nav", selected = "Glossary") ) }) # Enable/disable diagnostic plots diagnostic_trigger_ids <- paste0("diagnostic_", c("param", "param_transform", "param_transform_go")) observe({ diag_nav <- input$diagnostics_navlist local({ if (diag_nav != 'By model parameter') lapply(diagnostic_trigger_ids, function(x) shinyjs::disable(id = x)) else lapply(diagnostic_trigger_ids, function(x) shinyjs::enable(id = x)) }) }) # Links to quick definitions observeEvent( input$open_quick_rhat, shinyjs::info(includeText("text/quick_rhat.txt")) ) observeEvent( input$open_quick_neff, shinyjs::info(includeText("text/quick_neff.txt")) ) observeEvent( input$open_quick_mcse, shinyjs::info(includeText("text/quick_mcse.txt")) ) # Show/hide citation observeEvent( input$shinystan_citation_show, shinyjs::toggle( id = "citation_div", anim = TRUE, animType = "fade" ) ) } # END server ------------------------------------------------------ # _________________________________________________________________ shinystan/inst/ShinyStan/server_files/0000755000176200001440000000000013271714530017662 5ustar liggesusersshinystan/inst/ShinyStan/server_files/tooltips/0000755000176200001440000000000013035516434021540 5ustar liggesusersshinystan/inst/ShinyStan/server_files/tooltips/tooltips.R0000644000176200001440000000364113035516434023544 0ustar liggesusers# # This file is part of shinyStan # # Copyright (C) 2015 Jonah Sol Gabry & Stan Development Team # # # # shinyStan is free software; you can redistribute it and/or modify it under the # # terms of the GNU General Public License as published by the Free Software # # Foundation; either version 3 of the License, or (at your option) any later # # version. # # # # shinyStan is distributed in the hope that it will be useful, but WITHOUT ANY # # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License along with # # this program; if not, see . # # tooltip_ids <- c( # "download_multiview", # "dynamic_trace_stack", # "download_all_summary", # "tex_options", # "dens_transform_x", "hist_transform_x", "bivariate_transform_x", "trivariate_transform_x", # "bivariate_transform_y", "trivariate_transform_y", # "trivariate_transform_z" # ) # # tooltip_msgs <- c( # "Will be a list with three elements corresponding the the ggplot2 objects for the three plots.", # "If 'Stacked' is selected, the chains will be stacked on top of one another rather than drawing them independently. The first series specified in the input data will wind up on top of the chart and the last will be on bottom. Note that the y-axis values no longer correspond to the true values when this option is enabled.", # "Save as data.frame (.RData)", # "Print latex table to R console", # rep("A function of x, e.g. log(x), sqrt(x), x^2, 1/x, etc. Should be a valid R expression.", 4), # rep("A function of y, e.g. log(y), sqrt(y), y^2, 1/y, etc. Should be a valid R expression.", 2), # "A function of z, e.g. log(z), sqrt(z), z^2, 1/z, etc. Should be a valid R expression." # ) # tooltip_placements <- c(rep("right", 4), rep("top", 7)) shinystan/inst/ShinyStan/server_files/debounce.R0000644000176200001440000000334613035516434021600 0ustar liggesusers# From: https://gist.github.com/jcheng5/6141ea7066e62cafb31c # Returns a reactive that debounces the given expression by the given time in # milliseconds. # # This is not a true debounce in that it will not prevent \code{expr} from being # called many times (in fact it may be called more times than usual), but # rather, the reactive invalidation signal that is produced by expr is debounced # instead. This means that this function should be used when \code{expr} is # cheap but the things it will trigger (outputs and reactives that use # \code{expr}) are expensive. debounce <- function(expr, millis, env = parent.frame(), quoted = FALSE, domain = getDefaultReactiveDomain()) { force(millis) f <- exprToFunction(expr, env, quoted) label <- sprintf("debounce(%s)", paste(deparse(body(f)), collapse = "\n")) v <- reactiveValues( trigger = NULL, when = NULL # the deadline for the timer to fire; NULL if not scheduled ) # Responsible for tracking when f() changes. observeEvent(f(), { # The value changed. Start or reset the timer. v$when <- Sys.time() + millis / 1000 }, ignoreNULL = FALSE) # This observer is the timer. It rests until v$when elapses, then touches # v$trigger. observe({ if (is.null(v$when)) return() now <- Sys.time() if (now >= v$when) { v$trigger <- runif(1) v$when <- NULL } else { invalidateLater((v$when - now) * 1000, domain) } }) # This is the actual reactive that is returned to the user. It returns the # value of f(), but only invalidates/updates when v$trigger is touched. eventReactive(v$trigger, { f() }, ignoreNULL = FALSE) } shinystan/inst/ShinyStan/server_files/pages/0000755000176200001440000000000013035516434020762 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/explore/0000755000176200001440000000000013035516434022440 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/explore/ui/0000755000176200001440000000000013035516434023055 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/explore/ui/ui_trivariate_select_x.R0000644000176200001440000000033013035516434027731 0ustar liggesusersoutput$ui_trivariate_select_x <- renderUI({ selectizeInput( "trivariate_param_x", label = strong_bl("x-axis"), choices = .make_param_list(object), selected = input$param, multiple = FALSE ) })shinystan/inst/ShinyStan/server_files/pages/explore/server/0000755000176200001440000000000013035516434023746 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/explore/server/multiview.R0000644000176200001440000000417513137733210026121 0ustar liggesusersmultiview_samps <- reactive({ validate( need(input$param, message = FALSE), need(!is.null(input$multiview_warmup), message = "Loading...") ) if (!input$multiview_warmup) par_samps_post_warmup() else par_samps_all() }) dynamic_trace_plot_multiview <- reactive({ if (input$param == "") return() stack <- FALSE chain <- 0 do.call( ".param_trace_dynamic", args = list( param_samps = multiview_samps(), chain = chain, stack = stack, warmup_val = N_WARMUP, warmup_shade = isTRUE(input$multiview_warmup) && N_WARMUP > 0, x_lab = "Iteration", y_lab = input$param ) ) }) autocorr_plot_multiview <- reactive({ lags <- min(25, round((N_ITER - N_WARMUP) / 2)) do.call( ".autocorr_single_plot", args = list( samps = multiview_samps(), lags = lags ) ) }) density_plot_multiview <- reactive({ do.call( ".param_dens", args = list( param = input$param, dat = multiview_samps(), chain = 0, chain_split = FALSE, fill_color = base_fill, line_color = vline_base_clr, point_est = "None", CI = "None", x_breaks = "Some", title = FALSE ) ) }) output$multiview_param_name <- renderUI(strong(style = "font-size: 250%; color: #f9dd67;", input$param)) output$multiview_trace_out <- dygraphs::renderDygraph(dynamic_trace_plot_multiview()) output$multiview_density_out <- renderPlot(density_plot_multiview(), bg = "transparent") output$multiview_autocorr_out <- renderPlot(autocorr_plot_multiview(), bg = "transparent") # download multiview plot # output$download_multiview <- downloadHandler( # filename = 'shinystan_multiview.RData', # content = function(file) { # param_name <- input$param # shinystan_multiview <- list() # shinystan_multiview[[paste0("trace_", param_name)]] <- trace_plot_multiview() # shinystan_multiview[[paste0("density", param_name)]] <- density_plot_multiview() # shinystan_multiview[[paste0("ac_", param_name)]] <- autocorr_plot_multiview() # save(shinystan_multiview, file = file) # } # ) shinystan/inst/ShinyStan/server_files/pages/explore/server/histogram.R0000644000176200001440000000233113035516434026065 0ustar liggesusers# histogram hist_transform_x <- eventReactive(input$hist_transform_x_go > 0, input$hist_transform_x) histogram_plot <- reactive({ validate( need(input$param, message = FALSE), need(!is.null(input$hist_chain), message = FALSE) ) chain <- input$hist_chain if (is.na(chain)) chain <- 0 binwd <- input$hist_binwd if (is.na(binwd)) binwd <- 0 do.call( ".param_hist", args = list( param = input$param, dat = par_samps_post_warmup(), chain = chain, binwd = binwd, fill_color = input$hist_fill_color, line_color = input$hist_line_color, transform_x = hist_transform_x() ) ) }) output$hist_plot_out <- renderPlot({ x <- histogram_plot() suppress_and_print(x) }, bg = "transparent") # download plot output$download_histogram <- downloadHandler( filename = 'shinystan-histogram-gg.RData', content = function(file) { shinystan_histogram_gg <- histogram_plot() save(shinystan_histogram_gg, file = file) } ) output$save_pdf_histogram = downloadHandler( filename = "shinstan-histogram.pdf", content = function(file) { ggsave(file, plot = histogram_plot(), device = pdf) } ) shinystan/inst/ShinyStan/server_files/pages/explore/server/density.R0000644000176200001440000000610213035516434025547 0ustar liggesusers# kernel density plot dens_transform_x <- eventReactive( input$dens_transform_x_go > 0, input$dens_transform_x ) user_xlim <- function(lim) { xz <- strsplit(lim, split = "c(", fixed = TRUE)[[1L]][2] xz <- strsplit(xz, split = ",", fixed = TRUE)[[1L]] if (identical(xz, NA_character_)) return(FALSE) x_lim <- unlist(strsplit(xz, split = ")", fixed = TRUE)) x_lim <- gsub(" ", "", x_lim) if (x_lim[1L] == "min") x_lim[1L] <- NA if (x_lim[2L] == "max") x_lim[2L] <- NA as.numeric(x_lim) } density_plot <- reactive({ xzoom <- input$dens_xzoom if (xzoom == "") return(last_plot()) validate( need(input$param, message = FALSE), need(!is.null(input$dens_chain), message = FALSE), need(xzoom, message = FALSE) ) x_lim <- if (xzoom == "c(min, max)") { NULL } else { check <- try(user_xlim(xzoom)) validate(need(check, message = "Invalid input")) check } chain <- input$dens_chain if (is.na(chain)) chain <- 0 prior_fam <- input$dens_prior prior_params <- if (prior_fam == "None") { NULL } else if (prior_fam == "Normal") { list( mean = input$dens_prior_normal_mu, sd = input$dens_prior_normal_sigma ) } else if (prior_fam == "t") { list( df = input$dens_prior_t_df, location = input$dens_prior_t_mu, scale = input$dens_prior_t_sigma ) } else if (prior_fam == "Cauchy") { list( location = input$dens_prior_cauchy_mu, scale = input$dens_prior_cauchy_sigma ) } else if (prior_fam == "Beta") { list( shape1 = input$dens_prior_beta_shape1, shape2 = input$dens_prior_beta_shape2 ) } else if (prior_fam == "Exponential") { list(rate = input$dens_prior_expo_rate) } else if (prior_fam == "Gamma") { list( shape = input$dens_prior_gamma_shape, rate = input$dens_prior_gamma_rate ) } else if (prior_fam == "Inverse Gamma") { list( shape = input$dens_prior_inversegamma_shape, scale = input$dens_prior_inversegamma_scale ) } else { NULL } do.call( ".param_dens", args = list( param = input$param, dat = par_samps_post_warmup(), chain = chain, chain_split = input$dens_chain_split == "Separate", fill_color = input$dens_fill_color, line_color = input$dens_line_color, point_est = input$dens_point_est, CI = input$dens_ci, # y_breaks = input$dens_y_breaks, x_breaks = input$dens_x_breaks, x_lim = x_lim, prior_fam = prior_fam, prior_params = prior_params, transform_x = dens_transform_x() ) ) }) output$density_plot_out <- renderPlot({ suppress_and_print(density_plot()) }, bg = "transparent") # download plot output$download_density <- downloadHandler( filename = 'shinystan-density-gg.RData', content = function(file) { shinystan_density_gg <- density_plot() save(shinystan_density_gg, file = file) } ) output$save_pdf_density = downloadHandler( filename = "shinstan-density.pdf", content = function(file) { ggsave(file, plot = density_plot(), device = pdf) } ) shinystan/inst/ShinyStan/server_files/pages/explore/server/bivariate.R0000644000176200001440000000522013035516434026036 0ustar liggesusers# bivariate scatterplot bivariate_transform_x <- eventReactive(input$bivariate_transform_go > 0, input$bivariate_transform_x) bivariate_transform_y <- eventReactive(input$bivariate_transform_go > 0, input$bivariate_transform_y) bivariate_plot <- reactive({ validate( need(input$param, message = FALSE), need(input$bivariate_ellipse_lev, message = FALSE), need(input$bivariate_param_y, message = FALSE) ) if (!is.null(input$bivariate_ellipse_lev)) { validate( need(is.numeric(input$bivariate_pt_size), message = "Point size must be numeric"), need(is.numeric(input$bivariate_pt_shape), message = "Point shape must be numeric") ) if (input$bivariate_ellipse_lev != "None") { validate( need( input$param != input$bivariate_param_y, "For this option the x and y can't be the same parameter." ), need( is.numeric(input$bivariate_ellipse_lwd), message = "Ellipse size must be numeric" ), need( is.numeric(input$bivariate_ellipse_lty), message = "Ellipse shape must be numeric" ) ) } } do.call( ".bivariate_plot", args = list( samps = SAMPS_post_warmup, sp = if (!identical(SAMPLER_PARAMS_post_warmup, FALSE) && STAN_ALGORITHM == "NUTS") SAMPLER_PARAMS_post_warmup else NULL, max_td = if ("max_td" %in% names(MISC)) MISC$max_td else NULL, param = input$param, param2 = input$bivariate_param_y, pt_alpha = input$bivariate_pt_alpha, pt_size = input$bivariate_pt_size, pt_shape = input$bivariate_pt_shape, pt_color = input$bivariate_pt_color, ellipse_lev = input$bivariate_ellipse_lev, ellipse_color = input$bivariate_ellipse_color, ellipse_lty = input$bivariate_ellipse_lty, ellipse_lwd = input$bivariate_ellipse_lwd, ellipse_alpha = input$bivariate_ellipse_alpha, lines = input$bivariate_lines, lines_color = input$bivariate_lines_color, lines_alpha = input$bivariate_lines_alpha, transform_x = bivariate_transform_x(), transform_y = bivariate_transform_y() ) ) }) output$bivariate_plot_out <- renderPlot({ x <- bivariate_plot() suppressWarnings(print(x)) }, bg = "transparent") # download output$download_bivariate <- downloadHandler( filename = 'shinystan-bivariate-gg.RData', content = function(file) { shinystan_bivariate_gg <- bivariate_plot() save(shinystan_bivariate_gg, file = file) } ) output$save_pdf_bivariate = downloadHandler( filename = "shinstan-bivariate.pdf", content = function(file) { ggsave(file, plot = bivariate_plot(), device = pdf) } ) shinystan/inst/ShinyStan/server_files/pages/explore/server/summary_stats_param.R0000644000176200001440000000155113035516434030166 0ustar liggesusers# posterior summary statistics for a single parameter parameter_summary <- reactive({ validate(need(input$param != "", message = FALSE)) do.call( ".param_summary", args = list( param = input$param, summary = SUMMARY ) ) }) output$param_name <- renderText({ input$param }) output$parameter_summary_out <- DT::renderDataTable({ DT::datatable({ as.data.frame(round(parameter_summary(), 2)) }, rownames = FALSE, options = list( paging = FALSE, searching = FALSE, info = FALSE, ordering = FALSE, autoWidth = TRUE, columnDefs = list(list(sClass="alignRight", targets ="_all")), initComplete = htmlwidgets::JS( # change background color of table header 'function(settings, json) { $(this.api().table().header()).css({"background-color": "transparent", "color": "black"}); }') )) }) shinystan/inst/ShinyStan/server_files/pages/explore/server/trivariate.R0000644000176200001440000000250313035516434026243 0ustar liggesusers# trivariate scatterplot trivariate_transform_x <- eventReactive(input$trivariate_transform_go > 0, input$trivariate_transform_x) trivariate_transform_y <- eventReactive(input$trivariate_transform_go > 0, input$trivariate_transform_y) trivariate_transform_z <- eventReactive(input$trivariate_transform_go > 0, input$trivariate_transform_z) trivariate_plot <- reactive({ validate( need(input$trivariate_flip, message = "Loading..."), need(input$trivariate_param_x, message = "Waiting for x ..."), need(input$trivariate_param_y, message = "Waiting for y ..."), need(input$trivariate_param_z, message = "Waiting for z ...") ) x <- input$trivariate_param_x y <- input$trivariate_param_y z <- input$trivariate_param_z samps <- SAMPS_post_warmup do.call( ".param_trivariate", args = list( params = c(x, y, z), samps = samps, pt_color = input$trivariate_pt_color, pt_size = input$trivariate_pt_size, show_grid = input$trivariate_grid == "show", flip_y = input$trivariate_flip == "flip", transform_x = trivariate_transform_x(), transform_y = trivariate_transform_y(), transform_z = trivariate_transform_z() ) ) }) output$trivariate_plot_out <- threejs::renderScatterplotThree({ trivariate_plot() }) shinystan/inst/ShinyStan/server_files/pages/more/0000755000176200001440000000000013035516434021724 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/more/notes_and_code.R0000644000176200001440000000153713035516434025021 0ustar liggesusers# output$hamiltonian_gif <- renderImage({ # list(src="www/ham-sim-stepsize-ok.gif") # }, deleteFile = FALSE) output$user_text_saved <- renderText({ if (input$save_user_model_info > 0) { paste("Saved", format(Sys.time(), "%a %b %d %Y %X")) } }) output$user_code_saved <- renderText({ if (input$save_user_model_code > 0) { paste("Saved", format(Sys.time(), "%a %b %d %Y %X")) } }) observeEvent(input$save_user_model_info, handlerExpr = { model_info <- input$user_model_info if (model_info == "") model_info <- "Use this space to store notes about your model" slot(object, "user_model_info") <<- model_info }) observeEvent(input$save_user_model_code, handlerExpr = { model_code <- input$user_model_code if (model_code == "") model_code <- "Use this space to store your model code" slot(object, "model_code") <<- model_code }) shinystan/inst/ShinyStan/server_files/pages/diagnose/0000755000176200001440000000000013035516434022553 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/diagnose/ui/0000755000176200001440000000000013035516434023170 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/diagnose/ui/multitrace_customize.R0000644000176200001440000000467613035516434027603 0ustar liggesusers# # output$ui_multitrace_customize <- renderUI({ # my_palette <- "Default" # my_rect <- "Warmup" # my_rect_alpha <- 0.15 # my_layout <- "Long" # # absolutePanel(id = "controls_multitrace", # class = "draggable_controls", # fixed = TRUE, # # top = 175, right = 20, width = 270, # top = 300, right = 20, width = 270, # draggable = TRUE, # shinyjs::hidden( # div(id = "multitrace_options", # wellPanel( # class = "optionswell", # strongBig("Parameter estimates"), # hr(class = "hroptions"), # selectInput("multitrace_options_display", label = strongBig("Control"), # choices = c("Options", "Aesthetics"), # selected = "Options", width = "100%"), # conditionalPanel(condition = "input.multitrace_options_display == 'Options'", # numericInput("multitrace_chain", label = "Chain (0 = all chains)", min = 0, max = object@nChains, step = 1, value = 0), # radioButtons("multitrace_layout", label = "Layout", # choices = c("Long", "Grid"), selected = my_layout, inline = TRUE), # downloadButton("download_multitrace", "Save as ggplot2 object") # ), # conditionalPanel(condition = "input.multitrace_options_display == 'Aesthetics'", # selectizeInput("multitrace_palette", "Color palette", choices = c("Default", "Brewer (spectral)", "Rainbow", "Gray"), selected = my_palette), # # selectInput("multitrace_rect", label = "Shading", choices = c("None", "Samples", "Warmup"), selected = my_rect, size = 2, selectize = FALSE), # radioButtons("multitrace_rect", label = "Shading", choices = c("None", "Samples", "Warmup"), selected = my_rect, inline = TRUE), # sliderInput("multitrace_rect_alpha", "Shading opacity", value = my_rect_alpha, min = 0, max = 1, step = 0.01) # ) # ) # ) # ) # ) # }) shinystan/inst/ShinyStan/server_files/pages/diagnose/server/0000755000176200001440000000000013137732214024060 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/diagnose/server/summary_stats_sampler.R0000644000176200001440000000164313044376075030653 0ustar liggesusers# summary statistics for sampler parameters ------------------------------- summary_stats_sampler <- reactive({ validate( need(STAN_ALGORITHM %in% c("NUTS", "HMC"), message = "Only available for algorithm = NUTS or HMC"), need(input$sampler_warmup, message = "Loading...") ) sp <- if (input$sampler_warmup == "include") SAMPLER_PARAMS else SAMPLER_PARAMS_post_warmup do.call( ".sampler_summary", args = list( sampler_params = sp, warmup_val = N_WARMUP, report = input$sampler_report, digits = input$sampler_digits ) ) }) output$sampler_summary <- DT::renderDataTable({ DT::datatable({ summary_stats_sampler() }, options = list( # rownames = FALSE, processing = TRUE, deferRender = TRUE, scrollX = TRUE, scrollY = "200px", scrollCollapse = TRUE, paging = FALSE, searching = FALSE, info = FALSE )) }) shinystan/inst/ShinyStan/server_files/pages/diagnose/server/autocorr.R0000644000176200001440000000257713035516434026055 0ustar liggesuserscalc_height_autocorr_plot <- reactive({ params <- input$ac_params params <- .update_params_with_groups(params, PARAM_NAMES) LL <- length(params) LL <- ifelse(LL < 8, 8, LL) round(60 * LL) }) autocorr_plot <- reactive({ validate( need(input$ac_lags, message = "Loading..."), need(!is.null(input$ac_warmup), message = "Loading...") ) samps <- if (!input$ac_warmup) SAMPS_post_warmup else SAMPS_all params <- .update_params_with_groups(input$ac_params, PARAM_NAMES) if (!length(params)) params <- dimnames(samps)$parameters[1] # default to first parameter params <- unique(params) samps <- samps[, , params, drop = FALSE] do.call( ".autocorr_plot", args = list( samps = samps, lags = input$ac_lags, flip = input$ac_flip, combine_chains = input$ac_combine, partial = input$ac_partial ) ) }) output$autocorr_plot_out <- renderPlot({ autocorr_plot() }, bg = "transparent") # download the plot output$download_autocorr <- downloadHandler( filename = paste0('shinystan-autocorr-gg.RData'), content = function(file) { shinystan_autocorr_gg <- autocorr_plot() save(shinystan_autocorr_gg, file = file) }) output$save_pdf_autocorr = downloadHandler( filename = "shinstan-autocorr.pdf", content = function(file) { ggsave(file, plot = autocorr_plot(), device = pdf) }) shinystan/inst/ShinyStan/server_files/pages/diagnose/server/diagnostics.R0000644000176200001440000003744013137732214026522 0ustar liggesuserslp_name <- "log-posterior" lp_lab <- "Log Posterior" metrop_lab <- "Mean Metrop. Acceptance" stepsize_lab <- "Sampled Step Size" treedepth_lab <- "Treedepth" ndivergent_lab <- "Divergent" sp_nuts_check <- reactive({ validate( need(STAN_ALGORITHM == "NUTS", message = "Only available for algorithm = NUTS"), need(input$diagnostic_chain, message = "Loading...") ) }) lp_check <- reactive({ validate( need(lp_name %in% dimnames(SAMPS_post_warmup)[[3]], message = "Plot not displayed\n(Draws for 'lp__' or 'log-posterior' not found)") ) }) diagnostic_chain <- reactive({ validate(need(input$diagnostic_chain, message = "Waiting for chain (0 for all)")) input$diagnostic_chain }) diagnostic_param <- reactive({ validate(need(input$diagnostic_param, message = "Waiting for parameter")) input$diagnostic_param }) diagnostic_param_transform <- eventReactive(input$diagnostic_param_transform_go > 0, input$diagnostic_param_transform) selected_range <- debounce({ panel <- input$diagnostics_navlist nm <- switch( panel, "By model parameter" = "parameter", "Sample information" = "lp", "Treedepth information" = "treedepth", "Step size information" = "stepsize", "Divergence information" = "divergent" ) input_nm <- paste0("dynamic_trace_diagnostic_", nm, "_out_date_window") validate(need(input[[input_nm]], "Updating selected range")) sel <- input[[input_nm]] high <- as.integer(strsplit(sel[[2]], "[-]")[[1]][1]) low <- as.integer(if (is.nan(sel[[1]])) "1" else strsplit(sel[[1]], "[-]")[[1]][1]) low:high }, millis = 125) # stepsize ---------------------------------------------------------------- dynamic_trace_diagnostic_stepsize <- reactive({ sp_nuts_check() chain <- diagnostic_chain() samps <- .stepsize_pw[,-1] lab <- "Sampled Step Size" stack <- FALSE `%>%` <- dygraphs::`%>%` graph <- do.call( ".dynamic_trace_diagnostics", args = list( param_samps = samps, param_name = lab, chain = chain, stack = stack, group = "stepsize_information" ) ) graph %>% dygraphs::dyAxis("y", pixelsPerLabel = 40) }) stepsize_vs_lp <- reactive({ sp_nuts_check() lp_check() chain <- diagnostic_chain() sel <- selected_range() stepsize <- .stepsize_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] # drop iterations column lp <- SAMPS_post_warmup[if (!is.null(sel)) sel, , lp_name] .sampler_param_vs_param( p = lp, sp = stepsize, p_lab = lp_lab, sp_lab = stepsize_lab, chain = chain, violin = TRUE ) }) stepsize_vs_accept_stat <- reactive({ sp_nuts_check() chain <- diagnostic_chain() sel <- selected_range() df_ss <- .stepsize_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] # drop iterations column df_as <- .accept_stat_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] .sampler_param_vs_sampler_param_violin( round(df_ss, 4), df_as, lab_x = stepsize_lab, lab_y = metrop_lab, chain = chain ) }) # sample (accept_stat, lp) ------------------------------------------------ dynamic_trace_diagnostic_lp <- reactive({ sp_nuts_check() lp_check() chain <- diagnostic_chain() samps <- SAMPS_post_warmup[, , lp_name] lab <- "Log Posterior" stack <- FALSE do.call( ".dynamic_trace_diagnostics", args = list( param_samps = samps, param_name = lab, chain = chain, stack = stack, group = "sample_information" ) ) }) dynamic_trace_diagnostic_accept_stat <- reactive({ sp_nuts_check() chain <- diagnostic_chain() samps <- .accept_stat_pw[,-1] stack <- FALSE do.call( ".dynamic_trace_diagnostics", args = list( param_samps = samps, param_name = metrop_lab, chain = chain, stack = stack, group = "sample_information" ) ) }) lp_hist <- reactive({ sp_nuts_check() lp_check() chain <- diagnostic_chain() sel <- selected_range() lp <- SAMPS_post_warmup[if (!is.null(sel)) sel,, lp_name] df <- as.data.frame(cbind(iterations = 1:NROW(lp), lp)) .p_hist(df, lab = lp_lab, chain) }) accept_stat_hist <- reactive({ sp_nuts_check() sel <- selected_range() df <- .accept_stat_pw[if (!is.null(sel)) sel,, drop=FALSE] chain <- diagnostic_chain() .p_hist(df, lab = metrop_lab, chain) + xlim(0,1) }) accept_stat_vs_lp <- reactive({ sp_nuts_check() lp_check() sel <- selected_range() metrop <- .accept_stat_pw[if (!is.null(sel)) sel, -1L, drop = FALSE] # drop iterations column lp <- SAMPS_post_warmup[if (!is.null(sel)) sel, , lp_name] chain <- input$diagnostic_chain divergent <- .ndivergent_pw[if (!is.null(sel)) sel, -1L, drop = FALSE] td <- .treedepth_pw[if (!is.null(sel)) sel, -1L, drop = FALSE] hit_max_td <- apply(td, 2L, function(y) as.numeric(y == MISC$max_td)) .sampler_param_vs_param( p = lp, sp = metrop, divergent = divergent, hit_max_td = as.data.frame(hit_max_td), p_lab = lp_lab, sp_lab = metrop_lab, chain = chain ) }) # treedepth --------------------------------------------------------------- dynamic_trace_diagnostic_treedepth <- reactive({ sp_nuts_check() chain <- diagnostic_chain() samps <- .treedepth_pw[, -1] max_td <- MISC$max_td lab <- treedepth_lab stack <- FALSE graph <- do.call( ".dynamic_trace_diagnostics", args = list( param_samps = samps, param_name = lab, chain = chain, stack = stack, group = "treedepth_information" ) ) `%>%` <- dygraphs::`%>%` graph %>% dygraphs::dyLimit( limit = max_td, label = "max_treedepth", color = "black", labelLoc = "right", strokePattern = "solid" ) %>% dygraphs::dyAxis( "y", valueRange = c(0, max_td * 8 / 7), pixelsPerLabel = 20, drawGrid = FALSE ) }) treedepth_ndivergent_hist <- reactive({ sp_nuts_check() chain <- diagnostic_chain() sel <- selected_range() df_td <- .treedepth_pw[if (!is.null(sel)) sel, , drop=FALSE] df_nd <- .ndivergent_pw[if (!is.null(sel)) sel, , drop=FALSE] .treedepth_ndivergent_hist(df_td, df_nd, chain = chain, divergent = "All") }) treedepth_ndivergent0_hist <- reactive({ sp_nuts_check() chain <- diagnostic_chain() sel <- selected_range() df_td <- .treedepth_pw[if (!is.null(sel)) sel, , drop=FALSE] df_nd <- .ndivergent_pw[if (!is.null(sel)) sel, , drop=FALSE] .treedepth_ndivergent_hist(df_td, df_nd, chain = chain, divergent = 0) }) treedepth_ndivergent1_hist <- reactive({ sp_nuts_check() chain <- diagnostic_chain() sel <- selected_range() df_td <- .treedepth_pw[if (!is.null(sel)) sel, , drop=FALSE] df_nd <- .ndivergent_pw[if (!is.null(sel)) sel, , drop=FALSE] .treedepth_ndivergent_hist(df_td, df_nd, chain = chain, divergent = 1) }) treedepth_vs_lp <- reactive({ sp_nuts_check() lp_check() chain <- diagnostic_chain() sel <- selected_range() treedepth <- .treedepth_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] # drop iterations column lp <- SAMPS_post_warmup[if (!is.null(sel)) sel, , lp_name] .sampler_param_vs_param( p = lp, sp = treedepth, p_lab = lp_lab, sp_lab = treedepth_lab, chain = chain, violin = TRUE ) }) treedepth_vs_accept_stat <- reactive({ sp_nuts_check() chain <- diagnostic_chain() sel <- selected_range() df_td <- .treedepth_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] # drop iterations column df_as <- .accept_stat_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] .sampler_param_vs_sampler_param_violin( df_td, df_as, lab_x = treedepth_lab, lab_y = metrop_lab, chain = chain ) }) # N divergent ------------------------------------------------------------- dynamic_trace_diagnostic_ndivergent <- reactive({ sp_nuts_check() chain <- diagnostic_chain() samps <- .ndivergent_pw[,-1] stack <- FALSE graph <- do.call( ".dynamic_trace_diagnostics", args = list( param_samps = samps, param_name = ndivergent_lab, chain = chain, stack = stack, group = "ndivergent_information" ) ) `%>%` <- dygraphs::`%>%` graph %>% dygraphs::dyAxis( "y", valueRange = c(0, 1.1), pixelsPerLabel = 1e4, drawGrid = FALSE ) }) ndivergent_vs_lp <- reactive({ sp_nuts_check() lp_check() chain <- diagnostic_chain() sel <- selected_range() ndivergent <- .ndivergent_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] # drop iterations column lp <- SAMPS_post_warmup[if (!is.null(sel)) sel, , lp_name] .sampler_param_vs_param( p = lp, sp = ndivergent, p_lab = lp_lab, sp_lab = ndivergent_lab, chain = chain, violin = TRUE ) }) ndivergent_vs_accept_stat <- reactive({ sp_nuts_check() chain <- diagnostic_chain() sel <- selected_range() df_nd <- .ndivergent_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] # drop iterations column df_as <- .accept_stat_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] .sampler_param_vs_sampler_param_violin( df_nd, df_as, lab_x = ndivergent_lab, lab_y = metrop_lab, chain = chain ) }) # energy ------------------------------------------------------------------ energy_hist <- reactive({ sp_nuts_check() chain <- diagnostic_chain() np <- bayesplot::nuts_params(SAMPLER_PARAMS_post_warmup, pars = "energy__") if (chain != 0) np <- subset(np, Chain == chain) schm <- unlist(bayesplot::color_scheme_get("brightblue")) schm["light"] <- base_fill schm["mid"] <- overlay_fill schm["light_highlight"] <- vline_base_clr schm["mid_highlight"] <- pt_outline_clr bayesplot::color_scheme_set(unname(schm)) bayesplot::mcmc_nuts_energy(np, merge_chains = isTRUE(chain != 0)) + ggplot2::facet_wrap(~ Chain, labeller = "label_both") + thm_no_yaxs + bayesplot::facet_bg(FALSE) + bayesplot::facet_text(size = rel(1)) + bayesplot::legend_move("right") + theme(legend.text.align = 0, legend.text = element_text(size = rel(1.5))) }) # model parameter --------------------------------------------------------- dynamic_trace_diagnostic_parameter <- reactive({ sp_nuts_check() chain <- diagnostic_chain() param <- diagnostic_param() transform_x <- diagnostic_param_transform() samps <- SAMPS_post_warmup[, , param] lab <- param if (transform_x != "identity") { t_x <- get(transform_x) samps <- t_x(samps) lab <- paste0(transform_x, "(", param, ")") } stack <- FALSE do.call( ".dynamic_trace_diagnostics", args = list( param_samps = samps, param_name = paste("Parameter:", lab), chain = chain, stack = stack ) ) }) param_vs_lp <- reactive({ sp_nuts_check() lp_check() param <- diagnostic_param() chain <- diagnostic_chain() sel <- selected_range() lp <- SAMPS_post_warmup[if (!is.null(sel)) sel, , lp_name] transform_x <- diagnostic_param_transform() samps <- SAMPS_post_warmup[if (!is.null(sel)) sel, , param, drop = FALSE] divergent <- .ndivergent_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] td <- .treedepth_pw[if (!is.null(sel)) sel,-1L, drop = FALSE] hit_max_td <- apply(td, 2L, function(y) as.numeric(y == MISC$max_td)) lab <- param if (transform_x != "identity") { t_x <- get(transform_x) samps <- t_x(samps) lab <- paste0(transform_x, "(", param, ")") } samps <- as.data.frame(samps) .sampler_param_vs_param( p = lp, sp = samps, divergent = divergent, hit_max_td = as.data.frame(hit_max_td), p_lab = lp_lab, sp_lab = lab, chain = chain, violin = FALSE ) }) param_vs_accept_stat <- reactive({ sp_nuts_check() chain <- diagnostic_chain() param <- diagnostic_param() sel <- selected_range() metrop <- .accept_stat_pw[if (!is.null(sel)) sel, -1L, drop=FALSE] # drop iterations column transform_x <- diagnostic_param_transform() samps <- SAMPS_post_warmup[if (!is.null(sel)) sel,, param] divergent <- .ndivergent_pw[if (!is.null(sel)) sel, -1L, drop=FALSE] td <- .treedepth_pw[if (!is.null(sel)) sel, -1L, drop=FALSE] hit_max_td <- apply(td, 2L, function(y) as.numeric(y == MISC$max_td)) lab <- param if (transform_x != "identity") { t_x <- get(transform_x) samps <- t_x(samps) lab <- paste0(transform_x, "(", param, ")") } graph <- .sampler_param_vs_param( p = samps, sp = metrop, divergent = divergent, hit_max_td = as.data.frame(hit_max_td), chain = chain, p_lab = lab, sp_lab = metrop_lab ) graph + coord_flip() }) param_vs_stepsize <- reactive({ sp_nuts_check() chain <- diagnostic_chain() param <- diagnostic_param() sel <- selected_range() stepsize <- .stepsize_pw[if (!is.null(sel)) sel, -1L, drop=FALSE] # drop iterations column transform_x <- diagnostic_param_transform() samps <- SAMPS_post_warmup[if (!is.null(sel)) sel, , param] lab <- param if (transform_x != "identity") { t_x <- get(transform_x) samps <- t_x(samps) lab <- paste0(transform_x, "(", param, ")") } .sampler_param_vs_param( p = samps, sp = stepsize, p_lab = lab, sp_lab = stepsize_lab, chain = chain, violin = TRUE ) }) param_vs_treedepth <- reactive({ sp_nuts_check() chain <- diagnostic_chain() param <- diagnostic_param() sel <- selected_range() treedepth <- .treedepth_pw[if (!is.null(sel)) sel, -1L, drop=FALSE] # drop iterations column transform_x <- diagnostic_param_transform() samps <- SAMPS_post_warmup[if (!is.null(sel)) sel, , param] lab <- param if (transform_x != "identity") { t_x <- get(transform_x) samps <- t_x(samps) lab <- paste0(transform_x, "(", param, ")") } .sampler_param_vs_param( p = samps, sp = treedepth, p_lab = lab, sp_lab = treedepth_lab, chain = chain, violin = TRUE ) }) p_hist <- reactive({ sp_nuts_check() chain <- diagnostic_chain() param <- diagnostic_param() sel <- selected_range() transform_x <- diagnostic_param_transform() samps <- SAMPS_post_warmup[if (!is.null(sel)) sel,, param] lab <- param if (transform_x != "identity") { t_x <- get(transform_x) samps <- t_x(samps) lab <- paste0(transform_x, "(", param, ")") } df <- as.data.frame(cbind(iterations = 1:NROW(samps), samps)) .p_hist(df, lab = lab, chain = chain) }) # outputs --------------------------------------------------- trace_nms <- c("parameter", "lp", "accept_stat", "treedepth", "stepsize", "ndivergent") hmc_plots <- c("accept_stat_trace", "accept_stat_hist","accept_stat_vs_lp", "lp_trace", "lp_hist", "ndivergent_trace", "treedepth_trace", "treedepth_ndivergent_hist","treedepth_ndivergent0_hist", "treedepth_ndivergent1_hist", "treedepth_vs_lp", "ndivergent_vs_lp", "treedepth_vs_accept_stat", "ndivergent_vs_accept_stat", "stepsize_vs_lp", "stepsize_vs_accept_stat", "stepsize_trace", "param_vs_lp", "param_vs_accept_stat", "param_vs_stepsize", "param_vs_treedepth", "p_trace", "p_hist", "energy_hist") for (j in seq_along(trace_nms)) { local({ fn <- paste0("dynamic_trace_diagnostic_", trace_nms[j]) output[[paste0(fn,"_out")]] <- dygraphs::renderDygraph(do.call(fn, list())) }) } for (i in seq_along(hmc_plots)) { local({ fn <- hmc_plots[i] output[[paste0(fn, "_out")]] <- renderPlot({ x <- suppressMessages(do.call(fn, list())) suppress_and_print(x) }) }) } output$diagnostic_chain_text <- renderText({ chain <- diagnostic_chain() if (chain == 0) return("All chains") paste("Chain", chain) }) output$diagnostics_warnings_text <- renderText({ sp_nuts_check() divs <- sum(.ndivergent_pw[, -1]) hits <- sum(.treedepth_pw[, -1] == MISC$max_td) d <- divs > 0 h <- hits > 0 if (d && h) { msg <- paste( "WARNINGS -- Diverging error:", divs, "iterations.", "Maximum treedepth reached:", hits, "iterations." ) } else if (d && !h) { msg <- paste("WARNINGS -- Diverging error:", divs, "iterations.") } else if (!d && h) { msg <- paste("WARNINGS -- Maximum treedepth reached:", hits, "iterations.") } else { msg <- NULL } msg }) shinystan/inst/ShinyStan/server_files/pages/diagnose/server/rhat_neff_mcse.R0000644000176200001440000000451713035516434027156 0ustar liggesusersvb_check <- function() { validate( need( STAN_METHOD != "variational", message = "Not available for variational inference" ) ) } n_eff_plot <- reactive({ vb_check() dat <- SUMMARY[, "n_eff"] N <- prod(dim(SAMPS_post_warmup)[1:2]) dat <- data.frame(parameter = names(dat), x = dat / N) do.call(".rhat_neff_mcse_hist", args = list( dat = dat, N = nrow(SAMPS_post_warmup), which = "n_eff" )) }) rhat_plot <- reactive({ vb_check() dat <- SUMMARY[, "Rhat"] dat <- data.frame(parameter = names(dat), x = dat) do.call(".rhat_neff_mcse_hist", args = list(dat = dat, which = "rhat")) }) mcse_over_sd_plot <- reactive({ vb_check() dat <- SUMMARY[, c("se_mean", "sd")] dat <- dat[, 1] / dat[, 2] dat <- data.frame(parameter = names(dat), x = dat) do.call(".rhat_neff_mcse_hist", args = list(dat = dat, which = "mcse")) }) n_eff_warnings <- reactive({ vb_check() paste( .n_eff_warnings( SUMMARY, threshold = input$n_eff_threshold, N_total = length(SAMPS_post_warmup[, , 1L]) ), collapse = "\n" ) }) rhat_warnings <- reactive({ vb_check() paste(.rhat_warnings(SUMMARY, threshold = input$rhat_threshold), collapse = "\n") }) mcse_over_sd_warnings <- reactive({ vb_check() paste(.mcse_over_sd_warnings(SUMMARY, threshold = input$mcse_threshold), collapse = "\n") }) output$n_eff_warnings_title <- renderText({ paste0( "The following parameters have an effective sample size less than ", input$n_eff_threshold, "% of the total sample size: " ) }) output$rhat_warnings_title <- renderText({ paste0( "The following parameters have an Rhat value above ", input$rhat_threshold, ": " ) }) output$mcse_over_sd_warnings_title <- renderText({ paste0( "The following parameters have a Monte Carlo standard error greater than ", input$mcse_threshold, "% of the posterior standard deviation:" ) }) rhat_neff_mcse <- c("rhat", "n_eff", "mcse_over_sd") for (i in seq_along(rhat_neff_mcse)) { local({ fn <- paste0(rhat_neff_mcse[i], "_plot") output[[paste0(fn, "_out")]] <- renderPlot({ x <- do.call(fn, list()) suppress_and_print(x) }, bg = "transparent") }) local({ fn <- paste0(rhat_neff_mcse[i], "_warnings") output[[fn]] <- renderText(do.call(fn, list())) }) } shinystan/inst/ShinyStan/server_files/pages/diagnose/server/multitrace.R0000644000176200001440000000337213035516434026362 0ustar liggesusers# # # multiparameter traceplots ----------------------------------------------- # calc_height_trace_plot <- reactive({ # params <- input$multitrace_params # grid <- FALSE # if (!is.null(input$multitrace_layout)) { # if (input$multitrace_layout == "Grid") grid <- TRUE # } # params <- .update_params_with_groups(params, param_names) # LL <- length(params) # if (LL == 0) LL <- 4 # if (LL == 1) LL <- 2 # if (grid) { # if (LL > 5) return(30*LL) # if (LL < 5) return(60*LL) # } # round(100*LL) # }) # # # multitrace_plot # multitrace_plot <- reactive({ # validate(need(!is.null(input$multitrace_rect), message = "Loading...")) # x1 <- input$multi_xzoom[1] # x2 <- input$multi_xzoom[2] # dat <- samps_all[x1:x2,,,drop=FALSE] # # zoom <- "On" # do.call(".param_trace_multi", args = list( # params = input$multitrace_params, # all_param_names = param_names, # dat = dat, # chain = input$multitrace_chain, # warmup_val = warmup_val, # palette = input$multitrace_palette , # rect = input$multitrace_rect, # rect_color = "skyblue", # rect_alpha = input$multitrace_rect_alpha, # layout = input$multitrace_layout, # x1 = x1, # x2 = x2 # )) # }) # # output$multitrace_plot_out <- renderPlot({ # x <- multitrace_plot() # suppressWarnings(print(x)) # this avoids warnings about removing rows when using tracezoom feature # }, height = calc_height_trace_plot, bg = "transparent") # # # download the plot # output$download_multitrace <- downloadHandler( # filename = paste0('shinystan_multitrace.RData'), # content = function(file) { # shinystan_multitrace <- multitrace_plot() # save(shinystan_multitrace, file = file) # } # ) shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/0000755000176200001440000000000013035516434024170 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/ui/0000755000176200001440000000000013035516434024605 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/ui/pp_get_y_and_yrep.R0000644000176200001440000000254613035516434030426 0ustar liggesusersoutput$ui_pp_get_y <- renderUI({ if (is.null(pp_y)) { div( h4( withMathJax("Select \\(\\mathbf{y}\\) (vector of observations)") ), selectizeInput( "y_name", label = "Object from global environment", choices = c("", objects(envir = .GlobalEnv)), options = list(placeholder = "Select an object"), width = "50%" ) ) } else { helpText("All set: y found in shinystan object.") } }) output$ui_pp_get_yrep <- renderUI({ if (is.null(pp_yrep)) { choices <- PARAM_NAMES choices <- strsplit(choices, split = "[", fixed = TRUE) choices <- lapply(choices, function(i) return(i[1])) choices <- unique(unlist(choices)) div(h4( withMathJax( "Select \\(\\mathbf{y^{rep}}\\) (posterior predictive replications)" ) ), flowLayout( selectizeInput( "yrep_name", label = "Parameter/generated quantity from model", choices = c("", choices), options = list(placeholder = "Select a parameter name") ), selectizeInput( "yrep_name2", label = "Or object from global environment", choices = c("", objects(envir = .GlobalEnv)), options = list(placeholder = "Select an object") ) )) } else { helpText("All set: yrep found in shinystan object. Select a plot to view.") } }) shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/0000755000176200001440000000000013035516434025476 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/hists_test_statistics.R0000644000176200001440000000537713035516434032300 0ustar liggesuserspp_hists_test_statistics_mean <- reactive({ pp_tests() y <- get_y() yrep <- get_yrep() mean_y <- mean(y) mean_yrep <- apply(yrep, 1, mean) do.call(".pp_hists_test_statistics", args = list( stat_y = mean_y, stat_yrep = mean_yrep, which = "mean", geom = input$pp_hists_test_statistics_type )) }) pp_hists_test_statistics_sd <- reactive({ pp_tests() y <- get_y() yrep <- get_yrep() sd_y <- sd(y) sd_yrep <- apply(yrep, 1, sd) do.call(".pp_hists_test_statistics", args = list( stat_y = sd_y, stat_yrep = sd_yrep, which = "sd", geom = input$pp_hists_test_statistics_type )) }) pp_hists_test_statistics_min <- reactive({ pp_tests() y <- get_y() yrep <- get_yrep() min_y <- min(y) min_yrep <- apply(yrep, 1, min) do.call(".pp_hists_test_statistics", args = list( stat_y = min_y, stat_yrep = min_yrep, which = "min", geom = input$pp_hists_test_statistics_type )) }) pp_hists_test_statistics_max <- reactive({ pp_tests() y <- get_y() yrep <- get_yrep() max_y <- max(y) max_yrep <- apply(yrep, 1, max) do.call(".pp_hists_test_statistics", args = list( stat_y = max_y, stat_yrep = max_yrep, which = "max", geom = input$pp_hists_test_statistics_type )) }) pp_test_stats <- c("mean", "sd", "min", "max") for (i in seq_along(pp_test_stats)) { local({ fn <- paste0("pp_hists_test_statistics_", pp_test_stats[i]) output[[paste0(fn,"_out")]] <- renderPlot({ x <- suppressMessages(do.call(fn, list())) suppress_and_print(x) }, bg = "transparent") }) } # pp_hists_test_statistics_custom1 <- reactive({ # tests() # validate(need(input$pp_test_statistics_fun1, message = "")) # y <- get_y() # yrep <- get_yrep() # # fun <- input$pp_test_statistics_fun1 # if (grepl("function", fun)) { # f <- eval(parse(text = fun)) # stat_y <- f(y) # stat_yrep <- apply(yrep, 1, FUN = f) # } else { # stat_y <- do.call(fun, args = list(y)) # stat_yrep <- apply(yrep, 1, paste(fun)) # } # # do.call(".pp_hists_test_statistics", args = list( # stat_y = stat_y, # stat_yrep = stat_yrep, # which = "f", # geom = input$pp_hists_test_statistics_type # )) # }) # pp_hists_test_statistics_custom2 <- reactive({ # tests() # if (is.null(input$pp_test_statistics_fun2) | is.na(input$pp_test_statistics_fun2)) { # return(last_plot()) # } # y <- get_y() # yrep <- get_yrep() # stat_y <- do.call(input$pp_test_statistics_fun2, args = list(y)) # stat_yrep <- apply(yrep, 1, paste(input$pp_test_statistics_fun2)) # # do.call(".pp_hists_test_statistics", args = list( # stat_y = stat_y, # stat_yrep = stat_yrep, # which = paste(input$pp_test_statistics_fun2), # geom = input$pp_hists_test_statistics_type # )) # })shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/hists_rep_vs_obs.R0000644000176200001440000000146013035516434031175 0ustar liggesuserspp_hists_rep_vs_obs <- reactive({ pp_tests() validate(need(input$pp_hists_rep_vs_obs_type, message = "Loading...")) y <- get_y() yrep <- get_yrep() sample_ids <- sample_ids_for_hist() yrep_samp <- yrep[sample_ids, ] rownames(yrep_samp) <- paste("yrep", sample_ids) geom <- input$pp_hists_rep_vs_obs_type if (geom == "density" & input$pp_hists_rep_vs_obs_overlay == TRUE) { x_lim <- range(c(y, yrep)) out <- do.call(".pp_dens_rep_vs_obs", args = list( y = y, yrep_samp = yrep_samp, x_lim = x_lim )) return(out) } graphs <- .pp_hists_rep_vs_obs(y = y, yrep_samp = yrep_samp, geom = geom) suppressMessages(do.call(gridExtra::grid.arrange, c(graphs, ncol = 3))) }) output$pp_hists_rep_vs_obs_out <- renderPlot({ pp_hists_rep_vs_obs() }, bg = "transparent") shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/y_vs_avg_rep.R0000644000176200001440000000050113035516434030300 0ustar liggesuserspp_y_vs_avg_rep <- reactive({ pp_tests() y <- get_y() yrep <- get_yrep() zoom <- input$pp_zoom_to_zero do.call(".pp_y_vs_avg_rep", args = list( y = y, colMeans_yrep = colMeans(yrep), zoom_to_zero = zoom )) }) output$pp_y_vs_avg_rep_out <- renderPlot({ pp_y_vs_avg_rep() }, bg = "transparent") shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/rep_vs_resid_rep.R0000644000176200001440000000063413035516434031156 0ustar liggesuserspp_avg_rep_vs_avg_resid_rep <- reactive({ pp_tests() y <- get_y() yrep <- get_yrep() rowMeans_resids <- rowMeans(y - yrep) rowMeans_yrep <- rowMeans(yrep) do.call(".pp_avg_rep_vs_avg_resid_rep", args = list( rowMeans_yrep = rowMeans_yrep, rowMeans_resids = rowMeans_resids )) }) output$pp_avg_rep_vs_avg_resid_rep_out <- renderPlot({ pp_avg_rep_vs_avg_resid_rep() }, bg = "transparent") shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/rstanarm.R0000644000176200001440000000205713035516434027454 0ustar liggesusersoutput$pp_rep_vs_obs_out_rstanarm <- renderPlot({ overlay <- input$pp_rep_vs_obs_overlay_rstanarm == "density" if (overlay) print(PPC_plots[["pp_check_dens"]]) else suppress_and_print(PPC_plots[["pp_check_hist"]]) }, bg = "transparent") output$pp_hists_test_statistics_mean_out_rstanarm <- renderPlot({ suppress_and_print(PPC_plots[["pp_check_stat_mean"]]) }, bg = "transparent") output$pp_hists_test_statistics_sd_out_rstanarm <- renderPlot({ suppress_and_print(PPC_plots[["pp_check_stat_sd"]]) }, bg = "transparent") output$pp_hists_test_statistics_min_out_rstanarm <- renderPlot({ suppress_and_print(PPC_plots[["pp_check_stat_min"]]) }, bg = "transparent") output$pp_hists_test_statistics_max_out_rstanarm <- renderPlot({ suppress_and_print(PPC_plots[["pp_check_stat_max"]]) }, bg = "transparent") output$pp_y_vs_avg_rep_out_rstanarm <- renderPlot({ print(PPC_plots[["pp_check_scatter"]]) }, bg = "transparent") output$pp_hist_resids_out_rstanarm <- renderPlot({ suppress_and_print(PPC_plots[["pp_check_resid"]]) }, bg = "transparent") shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/pp_utils.R0000644000176200001440000000453513035516434027467 0ustar liggesusers # validate input tests ---------------------------------------------------- pp_tests <- reactive({ validate( need( get_y(), message = "Waiting for y \n" ), need( get_yrep(), message = "Waiting for y_rep \n" ) ) }) # y ------------------------------------------------------------------- get_y <- reactive({ if (!is.null(pp_y)) { return(pp_y) } else { validate(need(input$y_name, message = "Waiting for y")) y <- get(input$y_name) validate( need( !isTRUE(length(dim(y)) > 1), message = "Error: y should be a vector" ), need( is.numeric(y), message = "Error: y should be a numeric vector" ) ) return(y) } }) # y_rep ------------------------------------------------------------------- has_yrep_name <- reactive({ a <- input$yrep_name # name selected from model parameters / generated quantities b <- input$yrep_name2 # name of object in global environment validate(need(a != "" || b != "", message = "Waiting for y_rep")) if (a != "" && b != "") validate(need(FALSE, message = "y_rep can only be specified once")) return(TRUE) }) get_yrep <- reactive({ if (!is.null(pp_yrep)) { return(pp_yrep) } else { validate(need(has_yrep_name(), message = "Waiting for y_rep")) if (input$yrep_name2 != "") { return(get(input$yrep_name2)) } else { yreps <- grep(paste0("^", input$yrep_name, "\\["), PARAM_NAMES) out <- SAMPS_post_warmup[, , yreps] dd <- dim(out) validate(need( dd[3] == length(as.vector(get_y())), message = "ncol(y_rep) should equal length(y)" )) out <- array(out, dim = c(prod(dd[1:2]), dd[3])) return(out) } } }) # sample_ids_for_hist ------------------------------------------------------ nrow_yrep <- reactive({ nrow(get_yrep()) }) sample_ids_for_hist <- reactive({ go <- input$resample_hist_go isolate(sample(nrow_yrep(), 8)) }) # sample_ids_for_dens ------------------------------------------------------ sample_ids_for_dens <- reactive({ go <- input$resample_dens_go isolate(sample(nrow_yrep(), min(nrow_yrep(), 50))) }) # sample_id_for_resids ------------------------------------------------------ sample_id_for_resids <- reactive({ go <- input$resample_resids_go isolate(sample(nrow_yrep(), 1)) }) shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/server/hists_resids.R0000644000176200001440000000054113035516434030324 0ustar liggesuserspp_hist_resids <- reactive({ pp_tests() s <- sample_id_for_resids() resids <- get_y() - get_yrep()[s,] names(resids) <- paste0("resids(yrep_", s, ")") do.call(".pp_hist_resids", args = list(resids = resids)) }) output$pp_hist_resids_out <- renderPlot({ x <- suppressMessages(pp_hist_resids()) suppress_and_print(x) }, bg = "transparent") shinystan/inst/ShinyStan/server_files/pages/diagnose/ppcheck/ppcheck_helpers.R0000644000176200001440000000737413035516434027465 0ustar liggesuserspp_yrep_clr <- "#487575" pp_yrep_fill <- "#6B8E8E" .pp_hists_rep_vs_obs <- function(y, yrep_samp, geom = "histogram") { thm <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_yaxs + no_lgnd) graphs <- lapply(1:(1 + nrow(yrep_samp)), function(i) { if (i == 1) g <- qplot(x = y, geom = geom, color = I(vline_base_clr), size = I(0.2), fill = I(base_fill)) + labs(y = "", x = "y") else g <- qplot(x = yrep_samp[i-1, ], geom = geom, color = I(pp_yrep_clr), fill = I(pp_yrep_fill), size = I(0.2)) + labs(y = "", x = rownames(yrep_samp)[i-1]) g + thm }) graphs } .pp_dens_rep_vs_obs <- function(y, yrep_samp, x_lim) { dat <- data.frame(t(yrep_samp)) dat <- cbind(y = y, dat) mdat <- suppressMessages(reshape2::melt(dat)) mdat$which <- "yrep" mdat$which[mdat$variable == "y"] <- "y" graph <- ggplot(mdat, aes(x = value, group = variable, fill = which, color = which, alpha = which, size = which)) graph <- graph + geom_density() + scale_color_manual(values = c(vline_base_clr, pp_yrep_clr)) + scale_fill_manual(values = c(base_fill, pp_yrep_fill)) + scale_alpha_manual(values = c(3/4, 0)) + scale_size_manual(values = c(1/3, 1/2)) + scale_x_continuous(limits = x_lim) graph + labs(x = "", y = "") + theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_yaxs + no_lgnd) } .pp_hists_test_statistics <- function(stat_y, stat_yrep, which, geom = "histogram") { thm <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_yaxs) graph <- ggplot(data.frame(x = stat_yrep), aes(x = x)) if (geom == "histogram") { graph <- graph + stat_bin(aes(y=..count../sum(..count..)), color = pp_yrep_clr, fill = pp_yrep_fill, size = 0.2) } if (geom == "density") { graph <- graph + geom_density(color = pp_yrep_clr, fill = pp_yrep_fill, size = 0.2) } graph + geom_vline(xintercept = stat_y, color = vline_base_clr, size = 1.5, alpha = 1) + labs(y = "", x = paste0(which, "(yrep)")) + thm } .pp_hist_resids <- function(resids) { thm <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_yaxs + no_lgnd) graph <- ggplot(data.frame(x = resids), aes(x = x)) + stat_bin(aes(y=..count../sum(..count..)), color = vline_base_clr, fill = base_fill, size = 0.2) graph + thm + labs(y = "", x = names(resids)) } .pp_avg_rep_vs_avg_resid_rep <- function(rowMeans_yrep, rowMeans_resids){ dat <- data.frame(x = rowMeans_yrep, y = rowMeans_resids) xy_labs <- labs(x = "Average yrep", y = "Average residual") thm <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_lgnd) graph <- ggplot(dat, aes(x, y)) + geom_hline(yintercept = 0, color = vline_base_clr, size = 0.75) + geom_point(fill = pp_yrep_fill, color = pp_yrep_clr, size = 2.75, alpha = 0.75, shape = 21) + xy_labs graph + xy_labs + thm } .pp_y_vs_avg_rep <- function(y, colMeans_yrep, zoom_to_zero = FALSE){ dat <- data.frame(x = y, y = colMeans_yrep, z = abs(y-colMeans_yrep)) xy_labs <- labs(x = "y", y = "Average yrep") thm <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis) graph <- ggplot(dat, aes(x, y)) + geom_abline(intercept = 0, slope = 1, color = vline_base_clr, size = 0.75) + geom_point(fill = pp_yrep_fill, color = pp_yrep_clr, size = 2.75, alpha = 0.75, shape = 21) + xy_labs + thm if (zoom_to_zero) { graph <- graph + geom_hline(yintercept = 0, size = 3, color = axis_line_color) + geom_vline(xintercept = 0, size = 0.5, color = axis_line_color) + thm %+replace% theme(axis.line = element_blank()) } graph } shinystan/inst/ShinyStan/server_files/pages/estimate/0000755000176200001440000000000013035516434022575 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/estimate/ui/0000755000176200001440000000000013035516434023212 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/estimate/ui/multiparam_selectize.R0000644000176200001440000000326713035516434027567 0ustar liggesusersoutput$ui_multiparam_selectize <- renderUI({ choices <- make_param_list_with_groups_sort() selected <- c(input$params_to_plot) selectizeInput( "params_to_plot", label = h5("Select or enter parameter names"), width = '100%', choices = choices, multiple = TRUE ) }) # updating the choices and selected for the selectizeInput input$params_to_plot # when the sorting option is changed in input$param_plot_sort_j or when # parameters added by regex search copy_params_to_plot <- reactive({ copy <- input$params_to_plot if (is.null(copy) || !length(copy)) NULL else copy }) observe({ x <- input$param_plot_sort_j choices <- make_param_list_with_groups_sort() selected <- copy_params_to_plot() selected <- .update_params_with_groups(selected, PARAM_NAMES) updateSelectizeInput( session, inputId = "params_to_plot", choices = choices, selected = selected ) }) observeEvent(input$param_plot_regex, { pattern <- input$params_to_plot_regex if (pattern != "") { choices <- make_param_list_with_groups_sort() selected <- copy_params_to_plot() selected <- .update_params_with_groups(selected, PARAM_NAMES) if (.test_valid_regex(pattern)) { selected <- .update_params_with_regex(selected, PARAM_NAMES, pattern) updateSelectizeInput( session, inputId = "params_to_plot", choices = choices, selected = selected ) } } }) output$invalid_regex <- renderText({ pattern <- input$params_to_plot_regex if (length(pattern)) { msg <- "Invalid regular expression.\nYou might need to add the escape character '\\' ." validate(need(.test_valid_regex(pattern), message = msg)) } }) shinystan/inst/ShinyStan/server_files/pages/estimate/server/0000755000176200001440000000000013035516434024103 5ustar liggesusersshinystan/inst/ShinyStan/server_files/pages/estimate/server/summary_stats_latex.R0000644000176200001440000000207713035516434030344 0ustar liggesuserssummary_stats_latex <- reactive({ params <- unique(.update_params_with_groups(input$tex_params, PARAM_NAMES)) nParams <- length(params) if (nParams == 0) params <- PARAM_NAMES if (nParams == 1) { x <- do.call(".param_summary", args = list( param = params, summary = SUMMARY )) } else { x <- do.call(".tex_summary", args = list( summary = SUMMARY[params,], cols = input$tex_columns )) } pkgs <- input$tex_pkgs tab_env <- if ("Longtable" %in% pkgs) "longtable" else getOption("xtable.tabular.environment", "tabular") caption <- if (nzchar(input$tex_caption)) input$tex_caption else NULL xt <- xtable::xtable(x, caption = caption) xtable::digits(xt) <- input$tex_digits if ("n_eff" %in% colnames(xt)) xtable::display(xt)[1 + which(colnames(xt) == "n_eff")] <- "d" xtable::print.xtable( xt, booktabs = "Booktabs" %in% pkgs, tabular.environment = tab_env, include.rownames = FALSE ) }) output$summary_stats_latex_out <- renderPrint({ input$tex_go isolate(summary_stats_latex()) }) shinystan/inst/ShinyStan/server_files/pages/estimate/server/summary_stats.R0000644000176200001440000000307413035516434027145 0ustar liggesuserssummary_stats <- reactive({ `%>%` <- DT::`%>%` validate(need(input$table_digits, "loading")) DT::datatable(data = round(TABLE_STATS, digits = input$table_digits), colnames = c('mcse' = 'se_mean'), options = list( colReorder = list(realtime = TRUE), # dom = 'RBfClrTtip', dom = "Bflrtip", buttons = list('copy', 'print', list( extend = 'collection', buttons = c('csv', 'pdf'), text = 'Download' ), list(extend = 'colvis', columns = 1:ncol(TABLE_STATS))), pageLength = 10, pagingType = "full", processing = TRUE, deferRender = TRUE, scrollY = 400, scrollX = TRUE, scrollCollapse = FALSE, language = list(search = "_INPUT_", searchPlaceholder = "Regex searching"), search = list(regex = TRUE) ), extensions = c("Buttons", "ColReorder", "FixedColumns", "Scroller")) %>% DT::formatStyle(columns = "Rhat", color = DT::styleInterval(1.1, c("blue", "red"))) %>% DT::formatRound(columns = "n_eff", digits = 0) # %>% # DT::formatRound(columns = c("Rhat", "mean", "mcse", "sd", "2.5%", "25%", # "50%", "75%", "97.5%"), digits = input$table_digits) }) output$all_summary_out <- DT::renderDataTable({ summary_stats() }) shinystan/inst/ShinyStan/server_files/pages/estimate/server/multiparameter_plot.R0000644000176200001440000000433313035516434030322 0ustar liggesuserscalc_height_param_plot <- reactive({ if (!isTRUE(input$param_plot_show_density)) { "auto" } else { params <- input$params_to_plot params <- .update_params_with_groups(params, PARAM_NAMES) LL <- length(params) LL <- ifelse(LL < 8, 8, LL) if (!is.null(input$param_plot_color_by_rhat)) { # delay until input is ready if (input$param_plot_color_by_rhat == TRUE) { LL <- LL + 1 } } round(50 * LL) } }) multiparam_plot <- reactive({ validate(need(input$param_plot_fill_color, message = "Loading...")) if (is.null(input$param_plot_ci_level)) { # delay until input is ready return() } customize <- !is.null(input$param_plot_show_density) do.call( ".multiparam_plot", args = list( samps = SAMPS_post_warmup, params = input$params_to_plot, all_param_names = PARAM_NAMES, CI.level = input$param_plot_ci_level / 100, rhat_values = SUMMARY[, "Rhat"], show_density = ifelse(customize, input$param_plot_show_density, FALSE), # == "yes", FALSE), show_ci_line = ifelse(customize, input$param_plot_show_ci_line, TRUE), # == "yes", TRUE), color_by_rhat = ifelse(customize, input$param_plot_color_by_rhat, FALSE), # == "yes", FALSE), rhat_palette = ifelse(customize, input$param_plot_rhat_palette, "Oranges"), point_est = ifelse(customize, input$param_plot_point_est, "Median"), fill_color = ifelse(customize, input$param_plot_fill_color, "gray35"), outline_color = ifelse(customize, input$param_plot_outline_color, "black"), est_color = ifelse(customize, input$param_plot_est_color, "black") ) ) }) output$multiparam_plot_out <- renderPlot({ multiparam_plot() }, height = calc_height_param_plot, bg = "transparent") # download the plot output$download_multiparam_plot <- downloadHandler( filename = 'shinystan-multiparam-gg.RData', content = function(file) { shinystan_multiparam_gg <- multiparam_plot() save(shinystan_multiparam_gg, file = file) } ) output$save_pdf_multiparam = downloadHandler( filename = "shinstan-multiparam.pdf", content = function(file) { ggsave(file, plot = multiparam_plot(), device = pdf) } ) shinystan/inst/ShinyStan/server_files/utilities/0000755000176200001440000000000013044376075021703 5ustar liggesusersshinystan/inst/ShinyStan/server_files/utilities/par_samps_reactive.R0000644000176200001440000000044213035516434025670 0ustar liggesusers# reactive function to get samples for a single parameter par_samps_all <- reactive({ param <- input$param p <- which(PARAM_NAMES == param) SAMPS_all[, , p] }) par_samps_post_warmup <- reactive({ param <- input$param p <- which(PARAM_NAMES == param) SAMPS_post_warmup[, , p] }) shinystan/inst/ShinyStan/server_files/utilities/extract_sso.R0000644000176200001440000000476213044376075024375 0ustar liggesusers# Extract the contents of the shiny_stan_object slots and do some additional # processing MODEL_NAME <- slot(object, "model_name") PARAM_NAMES <- slot(object, "param_names") PARAM_DIMS <- slot(object, "param_dims") SAMPS_all <- slot(object, "posterior_sample") SAMPLER_PARAMS <- slot(object, "sampler_params") N_ITER <- slot(object, "n_iter") N_CHAIN <- slot(object, "n_chain") N_WARMUP <- slot(object, "n_warmup") SAMPS_post_warmup <- SAMPS_all[seq(from = N_WARMUP + 1, to = N_ITER), , , drop = FALSE] MISC <- slot(object, "misc") MISC_nms <- names(MISC) STAN_METHOD <- if ("stan_method" %in% MISC_nms) MISC$stan_method else "Not Stan" STAN_ALGORITHM <- if ("stan_algorithm" %in% MISC_nms) MISC$stan_algorithm else "Not Stan" pp_yrep <- if ("pp_yrep" %in% MISC_nms) MISC[["pp_yrep"]] else NULL pp_y <- if ("pp_y" %in% MISC_nms) MISC[["pp_y"]] else NULL SAMPLER_PARAMS_post_warmup <- if (!is.list(SAMPLER_PARAMS) | identical(SAMPLER_PARAMS, list(NA))) FALSE else if (!is.matrix(SAMPLER_PARAMS[[1L]])) FALSE else { lapply(seq_along(SAMPLER_PARAMS), function(i) { out <- SAMPLER_PARAMS[[i]] out <- if (N_WARMUP == 0) out else out[-(1:N_WARMUP), ] rownames(out) <- seq(from = N_WARMUP + 1, to = N_WARMUP + nrow(out)) out }) } if (!identical(FALSE, SAMPLER_PARAMS_post_warmup)) { .stepsize_pw <- .sampler_param_pw(SAMPLER_PARAMS_post_warmup, which = "stepsize__", warmup_val = N_WARMUP) .ndivergent_pw <- .sampler_param_pw(SAMPLER_PARAMS_post_warmup, which = "divergent__", warmup_val = N_WARMUP) .treedepth_pw <- .sampler_param_pw(SAMPLER_PARAMS_post_warmup, which = "treedepth__", warmup_val = N_WARMUP) .accept_stat_pw <- .sampler_param_pw(SAMPLER_PARAMS_post_warmup, which = "accept_stat__", warmup_val = N_WARMUP) .energy_pw <- .sampler_param_pw(SAMPLER_PARAMS_post_warmup, which = "energy__", warmup_val = N_WARMUP) } SUMMARY <- slot(object, "summary") TABLE_STATS <- SUMMARY if (!STAN_METHOD == "variational") { sel <- colnames(TABLE_STATS) %in% c("Rhat", "n_eff") TABLE_STATS <- cbind(TABLE_STATS[, sel], TABLE_STATS[,!sel]) sel <- NULL TABLE_STATS[, "n_eff"] <- round(TABLE_STATS[, "n_eff"]) } # ppcheck plots from rstanarm if (isTRUE(MISC$stanreg)) PPC_plots <- MISC$pp_check_plots shinystan/inst/ShinyStan/server_files/utilities/make_param_list_with_groups_sort.R0000644000176200001440000000250313035516434030652 0ustar liggesusersmake_param_list_with_groups_sort <- reactive({ validate(need(!is.null(input$param_plot_sort_j), message = "Loading...")) sort_j <- input$param_plot_sort_j choices <- list() param_groups <- names(PARAM_DIMS) ll <- length(PARAM_DIMS) LL <- sapply(seq_len(ll), function(i) length(PARAM_DIMS[[i]])) choices[seq_len(ll)] <- "" names(choices) <- param_groups for(i in seq_len(ll)) { if (LL[i] == 0) { choices[[i]] <- list(param_groups[i]) } else { group <- param_groups[i] temp <- paste0("^",group,"\\[") ch <- PARAM_NAMES[grep(temp, PARAM_NAMES)] # the next line avoids parameters whose names include the group name of a # different group of parameters being included in the latter group, e.g. # if we have b_bias[1], b_bias[2], bias[1], bias[2] then we want to avoid # bias[1] and bias[2] being included in the b_bias group ch <- ch[which(substr(ch, 1, nchar(group)) == group)] if (sort_j == TRUE & (LL[i] > 1)) { # change sorting so e.g. "beta[1,1] beta[1,2] beta[2,1] beta[2,2]" # instead of "beta[1,1] beta[2,1] beta[1,2] beta[2,2]" ch <- gtools::mixedsort(ch) } ch_out <- c(paste0(group,"_as_shinystan_group"), ch) names(ch_out) <- c(paste("ALL", group), ch) choices[[i]] <- ch_out } } choices }) shinystan/inst/ShinyStan/markdown/0000755000176200001440000000000013035516434017015 5ustar liggesusersshinystan/inst/ShinyStan/markdown/pp_check_tutorial.md0000644000176200001440000000615113035516434023041 0ustar liggesusers## Using Stan and ShinyStan for posterior predictive checking In this tutorial we do the following: 1. Generate some fake data to play with 2. Write code for a simple Stan model 3. Fit the model using **RStan** 4. Use **ShinyStan** for graphical posterior predictive checks ### Data First we'll generate some fake data in R to use for this example # Number of observations N <- 100 # Model matrix (with column of 1s for intercept and one covariate) X <- cbind(Const = 1, X1 = rnorm(N)) K <- ncol(X) # Generate fake outcome y beta <- c(2, 1/2) # pick intercept and coefficient sigma <- 1 # standard deviation y <- rnorm(N, mean = X %*% beta, sd = sigma) # generate data ### Stan code Now we can write Stan code for a simple linear regression model. data { int N ; # integer, number of observations int K ; # integer, number of columns in model matrix matrix[N,K] X ; # N by K model matrix vector[N] y ; # vector of N observations } parameters { real sigma ; # real number > 0, standard deviation vector[K] beta ; # K-vector of regression coefficients } model { beta ~ normal(0, 5) ; # prior for betas sigma ~ cauchy(0, 2.5) ; # prior for sigma y ~ normal(X*beta, sigma) ; # vectorized likelihood } generated quantities { # Here we do the simulations from the posterior predictive distribution vector[N] y_rep ; # vector of same length as the data y for (n in 1:N) y_rep[n] <- normal_rng(X[n]*beta, sigma) ; } In this case the posterior predictive distribution we want to simulate from is the normal distribution with mean and standard deviation updated to reflect the posterior draws of `beta` and `sigma`. The code in the `generated quantities` block will be evaluated for each posterior draw of the parameters. For example, if we have 100 post-warmup iterations then we will have 100 `y_rep` vectors, each of length `N`. ### Fit the model If we've saved our Stan code in a file called `stan_code.stan` then we can run this model with **RStan** and then launch **ShinyStan** like this: library(rstan) library(ShinyStan) # Prepare the data we'll need as a list stan_data <- list(y = y, X = X, N = N, K = K) # Fit the model stanfit <- stan(file = "stan_code.stan", data = stan_data) # Launch ShinyStan launch_shinystan(stanfit) ### Graphical posterior predictive checks with ShinyStan Once we've launched **ShinyStan** we can navigate to the page for posterior predictive checking. In the dropdown menus it will ask us to select the object containing our data from our R global environment and the name of the paramter from our model containing the posterior predictive replications. 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/v؀;9Nx;;:+x *-Z|>?23ws\8a+sk׆- mm- -\q,+?66c;s\V+)P4%!=a>rqr499so4*- -{̖-$JSS٭[_º\@O;mT*g<Ni(\ʉDnddClBt%ԪW^Fluŋ==4wYNW=X4=m+/)mp044 tw_:dɡc 'OD}ʳ@w]"Kxۃ}}x p:tH=Ït5d\FtE{@TUO,7:;}񸯳 >|8QPLUM5Sjĸ\D<33mv& ^z!BC"8LM&3[$6mb"US)%/MOۆj7R4-yh=+-ZF=Xsx FQK+T9H5p\yf2 B{EmpQqlZ&r;ynrrYBQ(s={s2;jƒ͎~o{e$uuVd 89]M6?4󄢚 (gȺ\,[Z(j{b1Nh._흯@8 ^ȍz6[fd= rYAD˥+J p$xiVz6[M*}06&|UI: {b1O,ŊW^)BR($ɡ+8>j b1?22쳎m`-Uy%wl(ű.S ѨFjȲe=y" 0 EIlْ(ʱj:]dR;v&'eˁ(;;6O,Ƌb5i9f'rK;NG5FX|YShXT %k7T^ONQT׹ܱXs8N}]];?-XPhTffL~dd#PZ6 tֻC,Ȋŋl]D -4Tfnq90xPhJS=(GՎA a,@OO bybʳz(T=;sE ~yV;ʚM/|,WI)_^ @@ yI2Tmp0Ӄ`M9BQ*5F~yBr[[s8kXA0UF.CAVcy>bEBPӑ(JP޼49jnS (EӶiZqh@Ѵ >ff8Q2@w2ͲsF8UXWeRQsQ4T+٧_ұwD1L9ϩ/'dw ~<'AU鵚R,*|fxxv6}>RQKF\I$P4j46Z*Bŋ]^^ˍ*ɑH<vy<.؄tXPhfWc%,'e1U*jjw࠮(zjjFOsXK)8Q,]ʺ\<'eFFի^bb%@OOs 5,l ݻ-ㆪZmӞvVlj5SLUjFHza7`vOsZ*Rhh(d vsIrĉ"`p800u^xljbZڱöm7TUTؿyL^J,] ܑ/I4 o W޴ .`XeB'UN$xI >S |`(f?Z*UI^`yVs,kjFZZehcFa꺥iT*c'IxuV CV-]W KOl_xן9j 1) ch <- gtools::mixedsort(ch) ch_out <- c(paste0(group,"_as_shinystan_group"), ch) names(ch_out) <- c(paste("ALL", group), ch) choices[[i]] <- ch_out } } choices } # update parameter selection for multi-parameter plots -------------------- # update with regex .test_valid_regex <- function(pattern) { trygrep <- try(grep(pattern, ""), silent = TRUE) if (inherits(trygrep, "try-error")) FALSE else TRUE } .update_params_with_regex <- function(params, all_param_names, regex_pattern) { sel <- which(all_param_names %in% params) to_search <- if (length(sel)) all_param_names[-sel] else all_param_names if (!length(regex_pattern)) return(params) to_add <- grep(regex_pattern, to_search, value = TRUE) if (!length(to_add)) params else c(params, to_add) } # update with groups .update_params_with_groups <- function(params, all_param_names) { as_group <- grep("_as_shinystan_group", params) if (!length(as_group)) return(params) make_group <- function(group_name) { all_param_names[grep(paste0("^",group_name,"\\["), all_param_names)] } single_params <- params[-as_group] grouped_params <- params[as_group] groups <- gsub("_as_shinystan_group", "", grouped_params) groups <- sapply(groups, make_group) c(single_params, unlist(groups)) } # generate color vectors -------------------------------------------------- color_vector <- function(n) { hues = seq(15, 375, length = n + 1) hcl(h=hues, l=50, c=50)[1:n] } color_vector_chain <- function(n) { hues = seq(15, 375, length = n + 1) hcl(h=hues, l=80, c=50)[1:n] } # transformations --------------------------------------------------------- transformation_choices <- c( "abs", "atanh", cauchit = "pcauchy", "cloglog", "exp", "expm1", "identity", "inverse", inv_logit = "plogis", "log", "log10", "log2", "log1p", logit = "qlogis", probit = "pnorm", "square", "sqrt" ) inverse <- function(x) 1/x cloglog <- function(x) log(-log1p(-x)) square <- function(x) x^2 # extra distributions for density comparisons ----------------------------- # t distribution with location and scale .dt_loc_scale <- function(x, df, location, scale) { 1/scale * dt((x - location)/scale, df) } # inverse gamma distribution .dinversegamma <- function(x, shape, scale) { logout <- log(scale)*shape - lgamma(shape) - (1+shape)*log(x) - (scale/x) exp(logout) } # bold/strong text generators --------------------------------------------- # used both in ui files and server files that call renderUI strongMed <- function(...) strong(style = "font-size: 14px; margin-bottom: 5px;", ...) strongBig <- function(...) strong(style = "font-size: 18px; margin-bottom: 5px;", ...) strong_bl <- function(...) strong(style = "color: #006DCC;", ...) shinystan/inst/ShinyStan/css/0000755000176200001440000000000013137732115015762 5ustar liggesusersshinystan/inst/ShinyStan/css/ShinyStan_dygraphs.css0000644000176200001440000000064713137732115022324 0ustar liggesusers/* css for dynamic trace plot */ .dygraph { background: transparent !important; } .dygraph-label { font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-style: normal; font-variant: normal; font-weight: 500; line-height: 26px; } .dygraphs input[type="text"] { width: 30px; border: 2px solid #346fa1; border-radius: 5px; text-align: center; background-color: black ; color: white ; } shinystan/inst/ShinyStan/css/ShinyStan.css0000644000176200001440000003500313035516434020416 0ustar liggesusers/* Also see shinyStan_datatables.css and shinyStan_dygraphs.css*/ /* red color: #B2001D dark red color: #590815 more red colors: #FCFAFA (Lightest) #F5ECEC #EBDADA #DCBCBC #C79999 #B97C7C #A25050 #8F2727 #7C0000 (Darkest) complimentary accent colors #6B8E8E (Light Teal) #487575 (Mid Teal) #1D4F4F (Dark Teal) #CBDDA6 (Light Olive) #9FB670 (Mid Olive) #617B2D (Dark Olive) */ @import url('//fonts.googleapis.com/css?family=Volkhov:400,400italic,700,700italic'); @import url(http://fonts.googleapis.com/css?family=Source+Sans+Pro); #save_and_close_button{ position: absolute; top: 5px; left: 10px; background: white; color: #B2001D; padding: 3px; border: 1px solid black; font-size: 12px; font-weight: bold; } #save_and_close_button:hover { background: black; color: white; font-weight: bolder; border: 2px solid #B2001D; } #shinystan-title { max-width: 100%; text-align: center; font-size: 5.25rem; line-height: 1.05882; margin-bottom: 1.5rem; margin-top: 0; font-family: "volkhov",serif; font-style: italic; font-weight: 400; letter-spacing: -3px; text-shadow: -1px -1px 0 #fff, 1px -1px 0 #fff, -1px 1px 0 #fff, 1px 1px 0 #fff; } #model-name { text-align: center; padding-top: 10px; } .wide-ensemble { z-index: -1; position: fixed; top: 100px; margin: 20px 0 0; width: 100%; } .stan-logo { z-index: 5; position: relative; /*width: 15%;*/ display: block; margin-left: auto; margin-right: auto; width: 150px; height: 150px; } #all_summary_out{ z-index: -1; } #controls_table{ z-index: 1; } #table_options{ max-height: 500px; overflow-y: scroll; } .home-links { text-align: center; } .home-links:hover { font-size: 1.05em; } .help-links-active { text-decoration: underline; } .btn-default { background: black; color: white; box-shadow: 0 0 1px #ffffff inset; } .btn-default:link { color: white; } .btn-default:focus,.btn-default.focus{ color:#ffffff;background-color:#798d8f;border-color:#566566 } .btn-default:hover{ color:#ffffff;background-color:#798d8f;border-color:#74898a } .btn-default:active,.btn-default.active,.open>.dropdown-toggle.btn-default{ color:#ffffff;background-color:#798d8f;border-color:#74898a } .btn-default:active:hover,.btn-default.active:hover,.open>.dropdown-toggle.btn-default:hover,.btn-default:active:focus,.btn-default.active:focus,.open>.dropdown-toggle.btn-default:focus,.btn-default:active.focus,.btn-default.active.focus,.open>.dropdown-toggle.btn-default.focus{ color:#ffffff;background-color:#687b7c;border-color:#566566 } .btn-default:active,.btn-default.active,.open>.dropdown-toggle.btn-default{ background-image:none } /***********************/ /* NAVBAR */ .navbar-fixed-top { background-color: transparent; border-top: 5px solid black; } .navbar-brand { background: transparent; /*border-radius: 0 0 0 10px;*/ } .navbar .nav { position: absolute; left: 150px; z-index: 5; text-align: center; padding-right: 20px; padding-left: 20px; border-radius: 0 0 10px 10px; background: black; opacity: 0.9; height: 47px; font-size: 13px; font-family: "Lucida Sans Unicode", "Lucida Grande", sans-serif; } #nav_toggle { z-index: 20; display: block; vertical-align: top; margin: 0; padding: 6px 20px; background-color: black; height: 44px; } .navbar .nav > .active > a { background: black; color: #B2001D; } .navbar .nav > .active > a:hover { background: black; color: #B2001D; border-radius: 3px 3px 3px 3px; box-shadow: 0 0 1px #B2001D inset; } .navbar .nav li { padding-top: 7px; color: #ffffff; line-height: 1.71429; display: inline-block; text-align:center; /*font-family: "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif; */ font-family: 'source-sans-pro', sans-serif; text-transform: uppercase; } .navbar .nav li a{ transition-delay: 0.1s; font-weight: lighter; margin-bottom: 0; padding: 7px; } /* .navbar .nav li a:link, a:visited { } */ .navbar .nav li a:hover, a:active{ /*background: #191919;*/ color: white; border-radius: 3px 3px 3px 3px; box-shadow: 0 0 1px #ffffff inset; } /***********************/ code { color: #590815; background: #F5ECEC; } body { padding-top: 75px; color: #222; /*font-family: "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif; font-weight: 300;*/ /*font-family: 'source-sans-pro', sans-serif;*/ } /*links*/ a:link { color: #006DCC; } a:visited { /* visited link */ color: #590815; } a:hover { /* mouse over link */ color: #590815; } a:active { /* selected link */ color: #590815; } .well { padding-top: 5px; padding-bottom: 0; } .table .alignRight { text-align:right; } /**************************************/ /* customization control panels */ .optionswell, .tableoptionswell { z-index: 200; padding-top: 20px; background-color: #dadada; border: 0 solid #222222; border-radius: 0; box-shadow: 0 0 0 rgba(0, 0, 0, 0.05) inset; margin-bottom: 20px; min-height: 20px; /*max-height: 400px;*/ overflow-y: scroll; } .draggable_controls { cursor: move; opacity: 0.9; transition: opacity 500ms 1s; } .aoptions { margin-top: 10px; } .hroptions { margin-top: 0; margin-bottom: 0; border-width: 2px; border-radius: 1px; border-color: #bdbdbd; } .hrtableoptions { margin-top: 5px; margin-bottom: 5px; border-width: 2px; border-radius: 1px; border-color: #bdbdbd; } .divoptions_light { padding-right: 10px; padding-left: 10px; margin-top: 0; margin-bottom: 10px; background-color: #f3f3f3; -webkit-box-shadow: inset 2px 1px 2px 0 rgba(189,189,189,0.5); -moz-box-shadow: inset 2px 1px 2px 0 rgba(189,189,189,0.5); box-shadow: inset 2px 1px 2px 0 rgba(189,189,189,0.5); } /**************************************/ /**************************************/ /* don't blink when recalculating */ #autocorr_plot_out.recalculating, #multiparam_plot_out.recalculating { /*#multitrace_plot_out.recalculating*/ opacity: 1.0; } /**************************************/ /*******************/ /* sliders */ /*******************/ .irs { height: 40px; } .irs-with-grid { height: 60px; } .irs-line { height: 8px; top: 25px; background: #EEE; background: linear-gradient(to bottom, #DDD -50%, #FFF 150%); /* W3C */ border: 1px solid #CCC; -moz-border-radius: 16px; border-radius: 16px; } .irs-line-left { height: 8px; } .irs-line-mid { height: 8px; } .irs-line-right { height: 8px; } .irs-bar { height: 8px; top: 25px; border-top: 1px solid #006dcc; border-bottom: 1px solid #006dcc; background: #006dcc; } .irs-bar-edge { height: 8px; top: 25px; width: 14px; border: 1px solid #006dcc; border-right: 0; background: #006dcc; -moz-border-radius: 16px 0 0 16px; border-radius: 16px 0 0 16px; } .irs-shadow { height: 2px; top: 38px; background: #000; opacity: 0.3; -moz-border-radius: 5px; border-radius: 5px; } .lt-ie9 .irs-shadow { filter: alpha(opacity=30); } .irs-slider { top: 17px; width: 22px; height: 22px; border: 1px solid #AAA; background: #DDD; -moz-border-radius: 27px; border-radius: 27px; box-shadow: 1px 1px 3px rgba(0,0,0,0.3); cursor: pointer; } #irs-active-slider, .irs-slider:hover { background: #FFF; } .irs-min, .irs-max { color: white; font-size: 10px; line-height: 1.333; text-shadow: none; top: 0; padding: 1px 3px; background: rgba(0,0,0,0.1); -moz-border-radius: 3px; border-radius: 3px; } .lt-ie9 .irs-min, .lt-ie9 .irs-max { background: #ccc; } .irs-from, .irs-to, .irs-single { color: #fff; font-size: 11px; line-height: 1.333; text-shadow: none; padding: 1px 3px; background: #006dcc; -moz-border-radius: 3px; border-radius: 3px; } .lt-ie9 .irs-from, .lt-ie9 .irs-to, .lt-ie9 .irs-single { background: #999; } .irs-grid { height: 27px; } .irs-grid-pol { opacity: 0.5; background: #006dcc; } .irs-grid-pol.small { background: #999; } .irs-grid-text { bottom: 5px; color: #99a4ac; } .irs-disabled { } /**************************************/ /* digits control for stats tables */ #table_digits { position: absolute; left: 50px; top: 5px; height: 30px; width: 50px; padding: 0 0 0 5px; } #table_digits_txt { position: absolute; left: 0; top: 8px; } #sampler_digits, #tex_digits { height: 30px; width: 60px; padding: 0 0 0 5px; } #summary_stats_latex_out { color: black; background-color: #F5ECEC; } /**************************************/ .help-glossary-div { background: white; font-family: Verdana, Geneva, sans-serif; font-weight: lighter; } .help-glossary-nav-container { background-color: #FCFAFA; /*#F6F6F6;*/ } .glossary-entry { font-size: 13px; max-height: 475px; overflow-y: scroll; } #diagnostics_help_navlist { background-color: #F5ECEC; /*#FCFAFA; (Lightest)*/ } #diagnostics_help_navlist:hover { background-color: #F5ECEC; } #dens_xzoom { width: 50%; } .diagnostics-navlist-tabpanel { max-height: 500px; overflow-y: scroll; } /**************************************/ #shinystan_citation { color: black; cursor: move; } #citation_text { color: black; font-size: 11px; } /* notes and model_code text areas */ #user_model_info { font-family: Verdana, Geneva, sans-serif; } #user_model_code { /*font-family: "Lucida Console", Monaco, monospace;*/ font-family: 'source-code-pro', monospace; } #user_model_info, #user_model_code { height: 400px; background: #f0f0f0; border: 5px solid #f0f0f0; border-radius: 2px; font-size: 11px; } #user_model_info:hover{ background: #f1f0ec; border-color: #f1f0ec; } #user_model_code:hover { background: #f1edec; border-color: #f1edec; } #user_model_info:focus { background: #f1f0ec; } #user_model_code:focus { background: #f1edec; } #user_model_info:focus, #user_model_code:focus { /* opacity: 0.9; filter: alpha(opacity=90); */ outline: none; border: 10px solid black;/*#B2001D; */ box-shadow: 0 0 10px black; font-size: 0.95em; -o-transform: scale(1.2); -ms-transform: scale(1.2); -moz-transform: scale(1.2); -webkit-transform: scale(1.2); transform: scale(1.2); transition-duration: 0.2s; } #user_text_saved, #user_code_saved { color: gray; } #save_user_model_info_safe_quit, #save_user_model_code_safe_quit{ color: gray; font-size: 12px; } .save-close-reminder { background: white; color: #B2001D; padding: 3px; border: 1px solid black; border-radius: 3px; font-size: 11px; text-align: center; width: 80px; white-space:nowrap; } /************************************** Transition effect for home page links with secondary text modified from freely usable code at Codrops http://tympanus.net/codrops/ http://tympanus.net/codrops/licensing/ **************************************/ #links_nav_div { text-align: center; } #links_nav a { position: relative; display: inline-block; outline: none; text-decoration: none; text-transform: uppercase; letter-spacing: 1px; font-weight: 400; text-shadow: 0 0 1px rgba(255,255,255,0.3); font-size: 1.35em; color: #590815; text-align: left; } #links_nav a:hover, #links_nav a:focus { outline: none; } /* Effect 9: second text and borders */ .cl-effect-9 a { margin: 0 0px; padding: 8px 10px; } .cl-effect-9 a::before, .cl-effect-9 a::after { position: absolute; top: 0; left: 0; width: 100%; height: 1px; background: #fff; content: ''; opacity: 0.2; -webkit-transition: opacity 0.3s, height 0.3s; -moz-transition: opacity 0.3s, height 0.3s; transition: opacity 0.3s, height 0.3s; } .cl-effect-9 a::after { top: 100%; opacity: 0; -webkit-transition: -webkit-transform 0.3s, opacity 0.3s; -moz-transition: -moz-transform 0.3s, opacity 0.3s; -o-transition: -o-transform 0.3s, opacity 0.3s; transition: transform 0.3s, opacity 0.3s; -webkit-transform: translateY(-10px); -moz-transform: translateY(-10px); -ms-transform: translateY(-10px); -o-transform: translateY(-10px); transform: translateY(-10px); } .cl-effect-9 a span:first-child { z-index: 2; display: block; font-weight: 300; } .cl-effect-9 a span:last-child { z-index: 1; display: block; padding: 8px 0 0 0; color: rgba(0,0,0,0.4); text-shadow: none; text-transform: none; font-style: italic; font-size: 0.75em; font-family: 'source-sans-pro', sans-serif; opacity: 0; -webkit-transition: -webkit-transform 0.3s, opacity 0.3s; -moz-transition: -moz-transform 0.3s, opacity 0.3s; -o-transition: -o-transform 0.3s, opacity 0.3s; transition: transform 0.3s, opacity 0.3s; -webkit-transform: translateY(-100%); -moz-transform: translateY(-100%); -ms-transform: translateY(-100%); -o-transform: translateY(-100%); transform: translateY(-100%); } .cl-effect-9 a:hover::before, .cl-effect-9 a:focus::before { height: 6px; } .cl-effect-9 a:hover::before, .cl-effect-9 a:hover::after, .cl-effect-9 a:focus::before, .cl-effect-9 a:focus::after { opacity: 1; -webkit-transform: translateY(0px); -moz-transform: translateY(0px); -ms-transform: translateY(0px); -o-transform: translateY(0px); transform: translateY(0px); } .cl-effect-9 a:hover span:last-child, .cl-effect-9 a:focus span:last-child { opacity: 1; -webkit-transform: translateY(0%); -moz-transform: translateY(0%); -ms-transform: translateY(0%); -o-transform: translateY(0%); transform: translateY(0%); } /* ggplot, pdf download buttons */ .plot-download { color: white; background: #222222; font-size: 12px; height: 30px; width: 85px; padding: 5px; margin: 3px; border-radius: 15px; } .pdf-download { width: 60px; } .plot-download:hover { background: #8B69AA; color: #222222; font-weight: bold; } .pdf-download:hover { background: #00888B; } /* transform action buttons */ .transform-go { color: white; background: #222222; font-size: 12px; font-weight: bold; height: 30px; width: 100px; padding: 5px; margin: 3px; border-radius: 15px; } .transform-go:hover { background: white; color: #222222; font-weight: bolder; } /* regex parameter search textInput*/ .regex-go { color: white; background: #222222; font-size: 12px; font-weight: bold; height: 30px; width: 75px; padding: 5px; margin: 3px; border-radius: 15px; } .regex-go:hover { background: white; color: #222222; font-weight: bolder; } #params_to_plot_regex_label { padding-left: 5px; font-size: 85%; } #params_to_plot_regex { font-size: 85%; height: 40px; margin: 3px; } #invalid_regex { font-size: 80%; } shinystan/inst/ShinyStan/css/ShinyStan_datatables.css0000644000176200001440000014732513035516434022615 0ustar liggesusers/*! jQuery UI - v1.11.2 - 2014-10-16 * http://jqueryui.com * Includes: core.css, accordion.css, autocomplete.css, button.css, datepicker.css, dialog.css, draggable.css, menu.css, progressbar.css, resizable.css, selectable.css, selectmenu.css, slider.css, sortable.css, spinner.css, tabs.css, tooltip.css, theme.css * To view and modify this theme, visit http://jqueryui.com/themeroller/?ffDefault=Verdana%2CArial%2Csans-serif&fwDefault=normal&fsDefault=1.1em&cornerRadius=4px&bgColorHeader=cccccc&bgTextureHeader=highlight_soft&bgImgOpacityHeader=75&borderColorHeader=aaaaaa&fcHeader=222222&iconColorHeader=222222&bgColorContent=ffffff&bgTextureContent=flat&bgImgOpacityContent=75&borderColorContent=aaaaaa&fcContent=222222&iconColorContent=222222&bgColorDefault=e6e6e6&bgTextureDefault=glass&bgImgOpacityDefault=75&borderColorDefault=d3d3d3&fcDefault=555555&iconColorDefault=888888&bgColorHover=dadada&bgTextureHover=glass&bgImgOpacityHover=75&borderColorHover=999999&fcHover=212121&iconColorHover=454545&bgColorActive=ffffff&bgTextureActive=glass&bgImgOpacityActive=65&borderColorActive=aaaaaa&fcActive=212121&iconColorActive=454545&bgColorHighlight=fbf9ee&bgTextureHighlight=glass&bgImgOpacityHighlight=55&borderColorHighlight=fcefa1&fcHighlight=363636&iconColorHighlight=2e83ff&bgColorError=fef1ec&bgTextureError=glass&bgImgOpacityError=95&borderColorError=cd0a0a&fcError=cd0a0a&iconColorError=cd0a0a&bgColorOverlay=aaaaaa&bgTextureOverlay=flat&bgImgOpacityOverlay=0&opacityOverlay=30&bgColorShadow=aaaaaa&bgTextureShadow=flat&bgImgOpacityShadow=0&opacityShadow=30&thicknessShadow=8px&offsetTopShadow=-8px&offsetLeftShadow=-8px&cornerRadiusShadow=8px * Copyright 2014 jQuery Foundation and other contributors; Licensed MIT */ /* MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ /* Layout helpers ----------------------------------*/ .ui-helper-hidden { display: none; } .ui-helper-hidden-accessible { border: 0; clip: rect(0 0 0 0); height: 1px; margin: -1px; overflow: hidden; padding: 0; position: absolute; width: 1px; } .ui-helper-reset { margin: 0; padding: 0; border: 0; outline: 0; line-height: 1.3; text-decoration: none; font-size: 100%; list-style: none; } .ui-helper-clearfix:before, .ui-helper-clearfix:after { content: ""; display: table; border-collapse: collapse; } .ui-helper-clearfix:after { clear: both; } .ui-helper-clearfix { min-height: 0; /* support: IE7 */ } .ui-helper-zfix { width: 100%; height: 100%; top: 0; left: 0; position: absolute; opacity: 0; filter:Alpha(Opacity=0); /* support: IE8 */ } .ui-front { z-index: 100; } /* Interaction Cues ----------------------------------*/ .ui-state-disabled { cursor: default !important; } /* Icons ----------------------------------*/ /* states and images */ .ui-icon { display: block; text-indent: -99999px; overflow: hidden; background-repeat: no-repeat; } /* Misc visuals ----------------------------------*/ /* Overlays */ .ui-widget-overlay { position: fixed; top: 0; left: 0; width: 100%; height: 100%; } .ui-accordion .ui-accordion-header { display: block; cursor: pointer; position: relative; margin: 2px 0 0 0; padding: .5em .5em .5em .7em; min-height: 0; /* support: IE7 */ font-size: 100%; } .ui-accordion .ui-accordion-icons { padding-left: 2.2em; } .ui-accordion .ui-accordion-icons .ui-accordion-icons { padding-left: 2.2em; } .ui-accordion .ui-accordion-header .ui-accordion-header-icon { position: absolute; left: .5em; top: 50%; margin-top: -8px; } .ui-accordion .ui-accordion-content { padding: 1em 2.2em; border-top: 0; overflow: auto; } .ui-autocomplete { position: absolute; top: 0; left: 0; cursor: default; } .ui-button { display: inline-block; position: relative; padding: 0; line-height: normal; margin-right: .1em; cursor: pointer; vertical-align: middle; text-align: center; overflow: visible; /* removes extra width in IE */ } .ui-button, .ui-button:link, .ui-button:visited, .ui-button:hover, .ui-button:active { text-decoration: none; } /* to make room for the icon, a width needs to be set here */ .ui-button-icon-only { width: 2.2em; } /* button elements seem to need a little more width */ button.ui-button-icon-only { width: 2.4em; } .ui-button-icons-only { width: 3.4em; } button.ui-button-icons-only { width: 3.7em; } /* button text element */ .ui-button .ui-button-text { display: block; line-height: normal; } .ui-button-text-only .ui-button-text { padding: .4em 1em; } .ui-button-icon-only .ui-button-text, .ui-button-icons-only .ui-button-text { padding: .4em; text-indent: -9999999px; } .ui-button-text-icon-primary .ui-button-text, .ui-button-text-icons .ui-button-text { padding: .4em 1em .4em 2.1em; } .ui-button-text-icon-secondary .ui-button-text, .ui-button-text-icons .ui-button-text { padding: .4em 2.1em .4em 1em; } .ui-button-text-icons .ui-button-text { padding-left: 2.1em; padding-right: 2.1em; } /* no icon support for input elements, provide padding by default */ input.ui-button { padding: .4em 1em; } /* button icon element(s) */ .ui-button-icon-only .ui-icon, .ui-button-text-icon-primary .ui-icon, .ui-button-text-icon-secondary .ui-icon, .ui-button-text-icons .ui-icon, .ui-button-icons-only .ui-icon { position: absolute; top: 50%; margin-top: -8px; } .ui-button-icon-only .ui-icon { left: 50%; margin-left: -8px; } .ui-button-text-icon-primary .ui-button-icon-primary, .ui-button-text-icons .ui-button-icon-primary, .ui-button-icons-only .ui-button-icon-primary { left: .5em; } .ui-button-text-icon-secondary .ui-button-icon-secondary, .ui-button-text-icons .ui-button-icon-secondary, .ui-button-icons-only .ui-button-icon-secondary { right: .5em; } /* button sets */ .ui-buttonset { margin-right: 7px; } .ui-buttonset .ui-button { margin-left: 0; margin-right: -.3em; } /* workarounds */ /* reset extra padding in Firefox, see h5bp.com/l */ input.ui-button::-moz-focus-inner, button.ui-button::-moz-focus-inner { border: 0; padding: 0; } .ui-datepicker { width: 17em; padding: .2em .2em 0; display: none; } .ui-datepicker .ui-datepicker-header { position: relative; padding: .2em 0; } .ui-datepicker .ui-datepicker-prev, .ui-datepicker .ui-datepicker-next { position: absolute; top: 2px; width: 1.8em; height: 1.8em; } .ui-datepicker .ui-datepicker-prev-hover, .ui-datepicker .ui-datepicker-next-hover { top: 1px; } .ui-datepicker .ui-datepicker-prev { left: 2px; } .ui-datepicker .ui-datepicker-next { right: 2px; } .ui-datepicker .ui-datepicker-prev-hover { left: 1px; } .ui-datepicker .ui-datepicker-next-hover { right: 1px; } .ui-datepicker .ui-datepicker-prev span, .ui-datepicker .ui-datepicker-next span { display: block; position: absolute; left: 50%; margin-left: -8px; top: 50%; margin-top: -8px; } .ui-datepicker .ui-datepicker-title { margin: 0 2.3em; line-height: 1.8em; text-align: center; } .ui-datepicker .ui-datepicker-title select { font-size: 1em; margin: 1px 0; } .ui-datepicker select.ui-datepicker-month, .ui-datepicker select.ui-datepicker-year { width: 45%; } .ui-datepicker table { width: 100%; font-size: .9em; border-collapse: collapse; margin: 0 0 .4em; } .ui-datepicker th { padding: .7em .3em; text-align: center; font-weight: bold; border: 0; } .ui-datepicker td { border: 0; padding: 1px; } .ui-datepicker td span, .ui-datepicker td a { display: block; padding: .2em; text-align: right; text-decoration: none; } .ui-datepicker .ui-datepicker-buttonpane { background-image: none; margin: .7em 0 0 0; padding: 0 .2em; border-left: 0; border-right: 0; border-bottom: 0; } .ui-datepicker .ui-datepicker-buttonpane button { float: right; margin: .5em .2em .4em; cursor: pointer; padding: .2em .6em .3em .6em; width: auto; overflow: visible; } .ui-datepicker .ui-datepicker-buttonpane button.ui-datepicker-current { float: left; } /* with multiple calendars */ .ui-datepicker.ui-datepicker-multi { width: auto; } .ui-datepicker-multi .ui-datepicker-group { float: left; } .ui-datepicker-multi .ui-datepicker-group table { width: 95%; margin: 0 auto .4em; } .ui-datepicker-multi-2 .ui-datepicker-group { width: 50%; } .ui-datepicker-multi-3 .ui-datepicker-group { width: 33.3%; } .ui-datepicker-multi-4 .ui-datepicker-group { width: 25%; } .ui-datepicker-multi .ui-datepicker-group-last .ui-datepicker-header, .ui-datepicker-multi .ui-datepicker-group-middle .ui-datepicker-header { border-left-width: 0; } .ui-datepicker-multi .ui-datepicker-buttonpane { clear: left; } .ui-datepicker-row-break { clear: both; width: 100%; font-size: 0; } /* RTL support */ .ui-datepicker-rtl { direction: rtl; } .ui-datepicker-rtl .ui-datepicker-prev { right: 2px; left: auto; } .ui-datepicker-rtl .ui-datepicker-next { left: 2px; right: auto; } .ui-datepicker-rtl .ui-datepicker-prev:hover { right: 1px; left: auto; } .ui-datepicker-rtl .ui-datepicker-next:hover { left: 1px; right: auto; } .ui-datepicker-rtl .ui-datepicker-buttonpane { clear: right; } .ui-datepicker-rtl .ui-datepicker-buttonpane button { float: left; } .ui-datepicker-rtl .ui-datepicker-buttonpane button.ui-datepicker-current, .ui-datepicker-rtl .ui-datepicker-group { float: right; } .ui-datepicker-rtl .ui-datepicker-group-last .ui-datepicker-header, .ui-datepicker-rtl .ui-datepicker-group-middle .ui-datepicker-header { border-right-width: 0; border-left-width: 1px; } .ui-dialog { overflow: hidden; position: absolute; top: 0; left: 0; padding: .2em; outline: 0; } .ui-dialog .ui-dialog-titlebar { padding: .4em 1em; position: relative; } .ui-dialog .ui-dialog-title { float: left; margin: .1em 0; white-space: nowrap; width: 90%; overflow: hidden; text-overflow: ellipsis; } .ui-dialog .ui-dialog-titlebar-close { position: absolute; right: .3em; top: 50%; width: 20px; margin: -10px 0 0 0; padding: 1px; height: 20px; } .ui-dialog .ui-dialog-content { position: relative; border: 0; padding: .5em 1em; background: none; overflow: auto; } .ui-dialog .ui-dialog-buttonpane { text-align: left; border-width: 1px 0 0 0; background-image: none; margin-top: .5em; padding: .3em 1em .5em .4em; } .ui-dialog .ui-dialog-buttonpane .ui-dialog-buttonset { float: right; } .ui-dialog .ui-dialog-buttonpane button { margin: .5em .4em .5em 0; cursor: pointer; } .ui-dialog .ui-resizable-se { width: 12px; height: 12px; right: -5px; bottom: -5px; background-position: 16px 16px; } .ui-draggable .ui-dialog-titlebar { cursor: move; } .ui-draggable-handle { -ms-touch-action: none; touch-action: none; } .ui-menu { list-style: none; padding: 0; margin: 0; display: block; outline: none; } .ui-menu .ui-menu { position: absolute; } .ui-menu .ui-menu-item { position: relative; margin: 0; padding: 3px 1em 3px .4em; cursor: pointer; min-height: 0; /* support: IE7 */ /* support: IE10, see #8844 */ list-style-image: url("data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"); } .ui-menu .ui-menu-divider { margin: 5px 0; height: 0; font-size: 0; line-height: 0; border-width: 1px 0 0 0; } .ui-menu .ui-state-focus, .ui-menu .ui-state-active { margin: -1px; } /* icon support */ .ui-menu-icons { position: relative; } .ui-menu-icons .ui-menu-item { padding-left: 2em; } /* left-aligned */ .ui-menu .ui-icon { position: absolute; top: 0; bottom: 0; left: .2em; margin: auto 0; } /* right-aligned */ .ui-menu .ui-menu-icon { left: auto; right: 0; } .ui-progressbar { height: 2em; text-align: left; overflow: hidden; } .ui-progressbar .ui-progressbar-value { margin: -1px; height: 100%; } .ui-progressbar .ui-progressbar-overlay { background: url("data:image/gif;base64,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"); height: 100%; filter: alpha(opacity=25); /* support: IE8 */ opacity: 0.25; } .ui-progressbar-indeterminate .ui-progressbar-value { background-image: none; } .ui-resizable { position: relative; } .ui-resizable-handle { position: absolute; font-size: 0.1px; display: block; -ms-touch-action: none; touch-action: none; } .ui-resizable-disabled .ui-resizable-handle, .ui-resizable-autohide .ui-resizable-handle { display: none; } .ui-resizable-n { cursor: n-resize; height: 7px; width: 100%; top: -5px; left: 0; } .ui-resizable-s { cursor: s-resize; height: 7px; width: 100%; bottom: -5px; left: 0; } .ui-resizable-e { cursor: e-resize; width: 7px; right: -5px; top: 0; height: 100%; } .ui-resizable-w { cursor: w-resize; width: 7px; left: -5px; top: 0; height: 100%; } .ui-resizable-se { cursor: se-resize; width: 12px; height: 12px; right: 1px; bottom: 1px; } .ui-resizable-sw { cursor: sw-resize; width: 9px; height: 9px; left: -5px; bottom: -5px; } .ui-resizable-nw { cursor: nw-resize; width: 9px; height: 9px; left: -5px; top: -5px; } .ui-resizable-ne { cursor: ne-resize; width: 9px; height: 9px; right: -5px; top: -5px; } .ui-selectable { -ms-touch-action: none; touch-action: none; } .ui-selectable-helper { position: absolute; z-index: 100; border: 1px dotted black; } .ui-selectmenu-menu { padding: 0; margin: 0; position: absolute; top: 0; left: 0; display: none; } .ui-selectmenu-menu .ui-menu { overflow: auto; /* Support: IE7 */ overflow-x: hidden; padding-bottom: 1px; } .ui-selectmenu-menu .ui-menu .ui-selectmenu-optgroup { font-size: 1em; font-weight: bold; line-height: 1.5; padding: 2px 0.4em; margin: 0.5em 0 0 0; height: auto; border: 0; } .ui-selectmenu-open { display: block; } .ui-selectmenu-button { display: inline-block; overflow: hidden; position: relative; text-decoration: none; cursor: pointer; } .ui-selectmenu-button span.ui-icon { right: 0.5em; left: auto; margin-top: -8px; position: absolute; top: 50%; } .ui-selectmenu-button span.ui-selectmenu-text { text-align: left; padding: 0.4em 2.1em 0.4em 1em; display: block; line-height: 1.4; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .ui-slider { position: relative; text-align: left; } .ui-slider .ui-slider-handle { position: absolute; z-index: 2; width: 1.2em; height: 1.2em; cursor: default; -ms-touch-action: none; touch-action: none; } .ui-slider .ui-slider-range { position: absolute; z-index: 1; font-size: .7em; display: block; border: 0; background-position: 0 0; } /* support: IE8 - See #6727 */ .ui-slider.ui-state-disabled .ui-slider-handle, .ui-slider.ui-state-disabled .ui-slider-range { filter: inherit; } .ui-slider-horizontal { height: .8em; } .ui-slider-horizontal .ui-slider-handle { top: -.3em; margin-left: -.6em; } .ui-slider-horizontal .ui-slider-range { top: 0; height: 100%; } .ui-slider-horizontal .ui-slider-range-min { left: 0; } .ui-slider-horizontal .ui-slider-range-max { right: 0; } .ui-slider-vertical { width: .8em; height: 100px; } .ui-slider-vertical .ui-slider-handle { left: -.3em; margin-left: 0; margin-bottom: -.6em; } .ui-slider-vertical .ui-slider-range { left: 0; width: 100%; } .ui-slider-vertical .ui-slider-range-min { bottom: 0; } .ui-slider-vertical .ui-slider-range-max { top: 0; } .ui-sortable-handle { -ms-touch-action: none; touch-action: none; } .ui-spinner { position: relative; display: inline-block; overflow: hidden; padding: 0; vertical-align: middle; } .ui-spinner-input { border: none; background: none; color: inherit; padding: 0; margin: .2em 0; vertical-align: middle; margin-left: .4em; margin-right: 22px; } .ui-spinner-button { width: 16px; height: 50%; font-size: .5em; padding: 0; margin: 0; text-align: center; position: absolute; cursor: default; display: block; overflow: hidden; right: 0; } /* more specificity required here to override default borders */ .ui-spinner a.ui-spinner-button { border-top: none; border-bottom: none; border-right: none; } /* vertically center icon */ .ui-spinner .ui-icon { position: absolute; margin-top: -8px; top: 50%; left: 0; } .ui-spinner-up { top: 0; } .ui-spinner-down { bottom: 0; } /* TR overrides */ .ui-spinner .ui-icon-triangle-1-s { /* need to fix icons sprite */ background-position: -65px -16px; } .ui-tabs { position: relative;/* position: relative prevents IE scroll bug (element with position: relative inside container with overflow: auto appear as "fixed") */ padding: .2em; } .ui-tabs .ui-tabs-nav { margin: 0; padding: .2em .2em 0; } .ui-tabs .ui-tabs-nav li { list-style: none; float: left; position: relative; top: 0; margin: 1px .2em 0 0; border-bottom-width: 0; padding: 0; white-space: nowrap; } .ui-tabs .ui-tabs-nav .ui-tabs-anchor { float: left; padding: .5em 1em; text-decoration: none; } .ui-tabs .ui-tabs-nav li.ui-tabs-active { margin-bottom: -1px; padding-bottom: 1px; } .ui-tabs .ui-tabs-nav li.ui-tabs-active .ui-tabs-anchor, .ui-tabs .ui-tabs-nav li.ui-state-disabled .ui-tabs-anchor, .ui-tabs .ui-tabs-nav li.ui-tabs-loading .ui-tabs-anchor { cursor: text; } .ui-tabs-collapsible .ui-tabs-nav li.ui-tabs-active .ui-tabs-anchor { cursor: pointer; } .ui-tabs .ui-tabs-panel { display: block; border-width: 0; padding: 1em 1.4em; background: none; } .ui-tooltip { padding: 8px; position: absolute; z-index: 9999; max-width: 300px; -webkit-box-shadow: 0 0 5px #aaa; box-shadow: 0 0 5px #aaa; } body .ui-tooltip { border-width: 2px; } /* Component containers ----------------------------------*/ .ui-widget { font-family: Verdana,Arial,sans-serif; font-size: 1.1em; } .ui-widget .ui-widget { font-size: 1em; } .ui-widget input, .ui-widget select, .ui-widget textarea, .ui-widget button { font-family: Verdana,Arial,sans-serif; font-size: 1em; } .ui-widget-content { border: 1px solid #aaaaaa; background: #ffffff url("images/ui-bg_flat_75_ffffff_40x100.png") 50% 50% repeat-x; color: #222222; } .ui-widget-content a { color: #222222; } .ui-widget-header { border: 1px solid #aaaaaa; background: #cccccc url("images/ui-bg_highlight-soft_75_cccccc_1x100.png") 50% 50% repeat-x; color: #222222; font-weight: bold; } .ui-widget-header a { color: #222222; } /* Interaction states ----------------------------------*/ .ui-state-default, .ui-widget-content .ui-state-default, .ui-widget-header .ui-state-default { border: 1px solid #d3d3d3; background: #e6e6e6 url("images/ui-bg_glass_75_e6e6e6_1x400.png") 50% 50% repeat-x; font-weight: normal; color: #555555; } .ui-state-default a, .ui-state-default a:link, .ui-state-default a:visited { color: #555555; text-decoration: none; } .ui-state-hover, .ui-widget-content .ui-state-hover, .ui-widget-header .ui-state-hover, .ui-state-focus, .ui-widget-content .ui-state-focus, .ui-widget-header .ui-state-focus { border: 1px solid #999999; background: #dadada url("images/ui-bg_glass_75_dadada_1x400.png") 50% 50% repeat-x; font-weight: normal; color: #212121; } .ui-state-hover a, .ui-state-hover a:hover, .ui-state-hover a:link, .ui-state-hover a:visited, .ui-state-focus a, .ui-state-focus a:hover, .ui-state-focus a:link, .ui-state-focus a:visited { color: #212121; text-decoration: none; } .ui-state-active, .ui-widget-content .ui-state-active, .ui-widget-header .ui-state-active { border: 1px solid #aaaaaa; background: #ffffff url("images/ui-bg_glass_65_ffffff_1x400.png") 50% 50% repeat-x; font-weight: normal; color: #212121; } .ui-state-active a, .ui-state-active a:link, .ui-state-active a:visited { color: #212121; text-decoration: none; } /* Interaction Cues ----------------------------------*/ .ui-state-highlight, .ui-widget-content .ui-state-highlight, .ui-widget-header .ui-state-highlight { border: 1px solid #fcefa1; background: #fbf9ee url("images/ui-bg_glass_55_fbf9ee_1x400.png") 50% 50% repeat-x; color: #363636; } .ui-state-highlight a, .ui-widget-content .ui-state-highlight a, .ui-widget-header .ui-state-highlight a { color: #363636; } .ui-state-error, .ui-widget-content .ui-state-error, .ui-widget-header .ui-state-error { border: 1px solid #cd0a0a; background: #fef1ec url("images/ui-bg_glass_95_fef1ec_1x400.png") 50% 50% repeat-x; color: #cd0a0a; } .ui-state-error a, .ui-widget-content .ui-state-error a, .ui-widget-header .ui-state-error a { color: #cd0a0a; } .ui-state-error-text, .ui-widget-content .ui-state-error-text, .ui-widget-header .ui-state-error-text { color: #cd0a0a; } .ui-priority-primary, .ui-widget-content .ui-priority-primary, .ui-widget-header .ui-priority-primary { font-weight: bold; } .ui-priority-secondary, .ui-widget-content .ui-priority-secondary, .ui-widget-header .ui-priority-secondary { opacity: .7; filter:Alpha(Opacity=70); /* support: IE8 */ font-weight: normal; } .ui-state-disabled, .ui-widget-content .ui-state-disabled, .ui-widget-header .ui-state-disabled { opacity: .35; filter:Alpha(Opacity=35); /* support: IE8 */ background-image: none; } .ui-state-disabled .ui-icon { filter:Alpha(Opacity=35); /* support: IE8 - See #6059 */ } /* Icons ----------------------------------*/ /* states and images */ .ui-icon { width: 16px; height: 16px; } .ui-icon, .ui-widget-content .ui-icon { background-image: url("images/ui-icons_222222_256x240.png"); } .ui-widget-header .ui-icon { background-image: url("images/ui-icons_222222_256x240.png"); } .ui-state-default .ui-icon { background-image: url("images/ui-icons_888888_256x240.png"); } .ui-state-hover .ui-icon, .ui-state-focus .ui-icon { background-image: url("images/ui-icons_454545_256x240.png"); } .ui-state-active .ui-icon { background-image: url("images/ui-icons_454545_256x240.png"); } .ui-state-highlight .ui-icon { background-image: url("images/ui-icons_2e83ff_256x240.png"); } .ui-state-error .ui-icon, .ui-state-error-text .ui-icon { background-image: url("images/ui-icons_cd0a0a_256x240.png"); } /* positioning */ .ui-icon-blank { background-position: 16px 16px; } .ui-icon-carat-1-n { background-position: 0 0; } .ui-icon-carat-1-ne { background-position: -16px 0; } .ui-icon-carat-1-e { background-position: -32px 0; } .ui-icon-carat-1-se { background-position: -48px 0; } .ui-icon-carat-1-s { background-position: -64px 0; } .ui-icon-carat-1-sw { background-position: -80px 0; } .ui-icon-carat-1-w { background-position: -96px 0; } .ui-icon-carat-1-nw { background-position: -112px 0; } .ui-icon-carat-2-n-s { background-position: -128px 0; } .ui-icon-carat-2-e-w { background-position: -144px 0; } .ui-icon-triangle-1-n { background-position: 0 -16px; } .ui-icon-triangle-1-ne { background-position: -16px -16px; } .ui-icon-triangle-1-e { background-position: -32px -16px; } .ui-icon-triangle-1-se { background-position: -48px -16px; } .ui-icon-triangle-1-s { background-position: -64px -16px; } .ui-icon-triangle-1-sw { background-position: -80px -16px; } .ui-icon-triangle-1-w { background-position: -96px -16px; } .ui-icon-triangle-1-nw { background-position: -112px -16px; } .ui-icon-triangle-2-n-s { background-position: -128px -16px; } .ui-icon-triangle-2-e-w { background-position: -144px -16px; } .ui-icon-arrow-1-n { background-position: 0 -32px; } .ui-icon-arrow-1-ne { background-position: -16px -32px; } .ui-icon-arrow-1-e { background-position: -32px -32px; } .ui-icon-arrow-1-se { background-position: -48px -32px; } .ui-icon-arrow-1-s { background-position: -64px -32px; } .ui-icon-arrow-1-sw { background-position: -80px -32px; } .ui-icon-arrow-1-w { background-position: -96px -32px; } .ui-icon-arrow-1-nw { background-position: -112px -32px; } .ui-icon-arrow-2-n-s { background-position: -128px -32px; } .ui-icon-arrow-2-ne-sw { background-position: -144px -32px; } .ui-icon-arrow-2-e-w { background-position: -160px -32px; } .ui-icon-arrow-2-se-nw { background-position: -176px -32px; } .ui-icon-arrowstop-1-n { background-position: -192px -32px; } .ui-icon-arrowstop-1-e { background-position: -208px -32px; } .ui-icon-arrowstop-1-s { background-position: -224px -32px; } .ui-icon-arrowstop-1-w { background-position: -240px -32px; } .ui-icon-arrowthick-1-n { background-position: 0 -48px; } .ui-icon-arrowthick-1-ne { background-position: -16px -48px; } .ui-icon-arrowthick-1-e { background-position: -32px -48px; } .ui-icon-arrowthick-1-se { background-position: -48px -48px; } .ui-icon-arrowthick-1-s { background-position: -64px -48px; } .ui-icon-arrowthick-1-sw { background-position: -80px -48px; } .ui-icon-arrowthick-1-w { background-position: -96px -48px; } .ui-icon-arrowthick-1-nw { background-position: -112px -48px; } .ui-icon-arrowthick-2-n-s { background-position: -128px -48px; } .ui-icon-arrowthick-2-ne-sw { background-position: -144px -48px; } .ui-icon-arrowthick-2-e-w { background-position: -160px -48px; } .ui-icon-arrowthick-2-se-nw { background-position: -176px -48px; } .ui-icon-arrowthickstop-1-n { background-position: -192px -48px; } .ui-icon-arrowthickstop-1-e { background-position: -208px -48px; } .ui-icon-arrowthickstop-1-s { background-position: -224px -48px; } .ui-icon-arrowthickstop-1-w { background-position: -240px -48px; } .ui-icon-arrowreturnthick-1-w { background-position: 0 -64px; } .ui-icon-arrowreturnthick-1-n { background-position: -16px -64px; } .ui-icon-arrowreturnthick-1-e { background-position: -32px -64px; } .ui-icon-arrowreturnthick-1-s { background-position: -48px -64px; } .ui-icon-arrowreturn-1-w { background-position: -64px -64px; } .ui-icon-arrowreturn-1-n { background-position: -80px -64px; } .ui-icon-arrowreturn-1-e { background-position: -96px -64px; } .ui-icon-arrowreturn-1-s { background-position: -112px -64px; } .ui-icon-arrowrefresh-1-w { background-position: -128px -64px; } .ui-icon-arrowrefresh-1-n { background-position: -144px -64px; } .ui-icon-arrowrefresh-1-e { background-position: -160px -64px; } .ui-icon-arrowrefresh-1-s { background-position: -176px -64px; } .ui-icon-arrow-4 { background-position: 0 -80px; } .ui-icon-arrow-4-diag { background-position: -16px -80px; } .ui-icon-extlink { background-position: -32px -80px; } .ui-icon-newwin { background-position: -48px -80px; } .ui-icon-refresh { background-position: -64px -80px; } .ui-icon-shuffle { background-position: -80px -80px; } .ui-icon-transfer-e-w { background-position: -96px -80px; } .ui-icon-transferthick-e-w { background-position: -112px -80px; } .ui-icon-folder-collapsed { background-position: 0 -96px; } .ui-icon-folder-open { background-position: -16px -96px; } .ui-icon-document { background-position: -32px -96px; } .ui-icon-document-b { background-position: -48px -96px; } .ui-icon-note { background-position: -64px -96px; } .ui-icon-mail-closed { background-position: -80px -96px; } .ui-icon-mail-open { background-position: -96px -96px; } .ui-icon-suitcase { background-position: -112px -96px; } .ui-icon-comment { background-position: -128px -96px; } .ui-icon-person { background-position: -144px -96px; } .ui-icon-print { background-position: -160px -96px; } .ui-icon-trash { background-position: -176px -96px; } .ui-icon-locked { background-position: -192px -96px; } .ui-icon-unlocked { background-position: -208px -96px; } .ui-icon-bookmark { background-position: -224px -96px; } .ui-icon-tag { background-position: -240px -96px; } .ui-icon-home { background-position: 0 -112px; } .ui-icon-flag { background-position: -16px -112px; } .ui-icon-calendar { background-position: -32px -112px; } .ui-icon-cart { background-position: -48px -112px; } .ui-icon-pencil { background-position: -64px -112px; } .ui-icon-clock { background-position: -80px -112px; } .ui-icon-disk { background-position: -96px -112px; } .ui-icon-calculator { background-position: -112px -112px; } .ui-icon-zoomin { background-position: -128px -112px; } .ui-icon-zoomout { background-position: -144px -112px; } .ui-icon-search { background-position: -160px -112px; } .ui-icon-wrench { background-position: -176px -112px; } .ui-icon-gear { background-position: -192px -112px; } .ui-icon-heart { background-position: -208px -112px; } .ui-icon-star { background-position: -224px -112px; } .ui-icon-link { background-position: -240px -112px; } .ui-icon-cancel { background-position: 0 -128px; } .ui-icon-plus { background-position: -16px -128px; } .ui-icon-plusthick { background-position: -32px -128px; } .ui-icon-minus { background-position: -48px -128px; } .ui-icon-minusthick { background-position: -64px -128px; } .ui-icon-close { background-position: -80px -128px; } .ui-icon-closethick { background-position: -96px -128px; } .ui-icon-key { background-position: -112px -128px; } .ui-icon-lightbulb { background-position: -128px -128px; } .ui-icon-scissors { background-position: -144px -128px; } .ui-icon-clipboard { background-position: -160px -128px; } .ui-icon-copy { background-position: -176px -128px; } .ui-icon-contact { background-position: -192px -128px; } .ui-icon-image { background-position: -208px -128px; } .ui-icon-video { background-position: -224px -128px; } .ui-icon-script { background-position: -240px -128px; } .ui-icon-alert { background-position: 0 -144px; } .ui-icon-info { background-position: -16px -144px; } .ui-icon-notice { background-position: -32px -144px; } .ui-icon-help { background-position: -48px -144px; } .ui-icon-check { background-position: -64px -144px; } .ui-icon-bullet { background-position: -80px -144px; } .ui-icon-radio-on { background-position: -96px -144px; } .ui-icon-radio-off { background-position: -112px -144px; } .ui-icon-pin-w { background-position: -128px -144px; } .ui-icon-pin-s { background-position: -144px -144px; } .ui-icon-play { background-position: 0 -160px; } .ui-icon-pause { background-position: -16px -160px; } .ui-icon-seek-next { background-position: -32px -160px; } .ui-icon-seek-prev { background-position: -48px -160px; } .ui-icon-seek-end { background-position: -64px -160px; } .ui-icon-seek-start { background-position: -80px -160px; } /* ui-icon-seek-first is deprecated, use ui-icon-seek-start instead */ .ui-icon-seek-first { background-position: -80px -160px; } .ui-icon-stop { background-position: -96px -160px; } .ui-icon-eject { background-position: -112px -160px; } .ui-icon-volume-off { background-position: -128px -160px; } .ui-icon-volume-on { background-position: -144px -160px; } .ui-icon-power { background-position: 0 -176px; } .ui-icon-signal-diag { background-position: -16px -176px; } .ui-icon-signal { background-position: -32px -176px; } .ui-icon-battery-0 { background-position: -48px -176px; } .ui-icon-battery-1 { background-position: -64px -176px; } .ui-icon-battery-2 { background-position: -80px -176px; } .ui-icon-battery-3 { background-position: -96px -176px; } .ui-icon-circle-plus { background-position: 0 -192px; } .ui-icon-circle-minus { background-position: -16px -192px; } .ui-icon-circle-close { background-position: -32px -192px; } .ui-icon-circle-triangle-e { background-position: -48px -192px; } .ui-icon-circle-triangle-s { background-position: -64px -192px; } .ui-icon-circle-triangle-w { background-position: -80px -192px; } .ui-icon-circle-triangle-n { background-position: -96px -192px; } .ui-icon-circle-arrow-e { background-position: -112px -192px; } .ui-icon-circle-arrow-s { background-position: -128px -192px; } .ui-icon-circle-arrow-w { background-position: -144px -192px; } .ui-icon-circle-arrow-n { background-position: -160px -192px; } .ui-icon-circle-zoomin { background-position: -176px -192px; } .ui-icon-circle-zoomout { background-position: -192px -192px; } .ui-icon-circle-check { background-position: -208px -192px; } .ui-icon-circlesmall-plus { background-position: 0 -208px; } .ui-icon-circlesmall-minus { background-position: -16px -208px; } .ui-icon-circlesmall-close { background-position: -32px -208px; } .ui-icon-squaresmall-plus { background-position: -48px -208px; } .ui-icon-squaresmall-minus { background-position: -64px -208px; } .ui-icon-squaresmall-close { background-position: -80px -208px; } .ui-icon-grip-dotted-vertical { background-position: 0 -224px; } .ui-icon-grip-dotted-horizontal { background-position: -16px -224px; } .ui-icon-grip-solid-vertical { background-position: -32px -224px; } .ui-icon-grip-solid-horizontal { background-position: -48px -224px; } .ui-icon-gripsmall-diagonal-se { background-position: -64px -224px; } .ui-icon-grip-diagonal-se { background-position: -80px -224px; } /* Misc visuals ----------------------------------*/ /* Corner radius */ .ui-corner-all, .ui-corner-top, .ui-corner-left, .ui-corner-tl { border-top-left-radius: 4px; } .ui-corner-all, .ui-corner-top, .ui-corner-right, .ui-corner-tr { border-top-right-radius: 4px; } .ui-corner-all, .ui-corner-bottom, .ui-corner-left, .ui-corner-bl { border-bottom-left-radius: 4px; } .ui-corner-all, .ui-corner-bottom, .ui-corner-right, .ui-corner-br { border-bottom-right-radius: 4px; } /* Overlays */ .ui-widget-overlay { background: #aaaaaa url("images/ui-bg_flat_0_aaaaaa_40x100.png") 50% 50% repeat-x; opacity: .3; filter: Alpha(Opacity=30); /* support: IE8 */ } .ui-widget-shadow { margin: -8px 0 0 -8px; padding: 8px; background: #aaaaaa url("images/ui-bg_flat_0_aaaaaa_40x100.png") 50% 50% repeat-x; opacity: .3; filter: Alpha(Opacity=30); /* support: IE8 */ border-radius: 8px; } /* * Table styles */ table.dataTable { width: 100%; margin: 0 auto; clear: both; border-collapse: separate; border-spacing: 0; /* * Header and footer styles */ /* * Body styles */ } table.dataTable thead th, table.dataTable thead td, table.dataTable tfoot th, table.dataTable tfoot td { padding: 4px 10px; } table.dataTable thead th, table.dataTable tfoot th { font-weight: bold; } table.dataTable thead th:active, table.dataTable thead td:active { outline: none; } table.dataTable thead .sorting_asc, table.dataTable thead .sorting_desc, table.dataTable thead .sorting { cursor: pointer; *cursor: hand; } table.dataTable thead th div.DataTables_sort_wrapper { position: relative; padding-right: 10px; } table.dataTable thead th div.DataTables_sort_wrapper span { position: absolute; top: 50%; margin-top: -8px; right: -5px; } table.dataTable thead th.ui-state-default { border-right-width: 0; } table.dataTable thead th.ui-state-default:last-child { border-right-width: 1px; } table.dataTable tbody tr { background-color: white; } table.dataTable tbody tr.selected { background-color: #b0bed9; } table.dataTable tbody th, table.dataTable tbody td { padding: 8px 10px; } table.dataTable th.center, table.dataTable td.center, table.dataTable td.dataTables_empty { text-align: center; } table.dataTable th.right, table.dataTable td.right { text-align: right; } table.dataTable.row-border tbody th, table.dataTable.row-border tbody td, table.dataTable.display tbody th, table.dataTable.display tbody td { border-top: 1px solid #dddddd; } table.dataTable.row-border tbody tr:first-child th, table.dataTable.row-border tbody tr:first-child td, table.dataTable.display tbody tr:first-child th, table.dataTable.display tbody tr:first-child td { border-top: none; } table.dataTable.cell-border tbody th, table.dataTable.cell-border tbody td { border-top: 1px solid #dddddd; border-right: 1px solid #dddddd; } table.dataTable.cell-border tbody tr th:first-child, table.dataTable.cell-border tbody tr td:first-child { border-left: 1px solid #dddddd; } table.dataTable.cell-border tbody tr:first-child th, table.dataTable.cell-border tbody tr:first-child td { border-top: none; } table.dataTable.stripe tbody tr.odd, table.dataTable.display tbody tr.odd { background-color: #f9f9f9; } table.dataTable.stripe tbody tr.odd.selected, table.dataTable.display tbody tr.odd.selected { background-color: #abb9d3; } table.dataTable.hover tbody tr:hover, table.dataTable.hover tbody tr.odd:hover, table.dataTable.hover tbody tr.even:hover, table.dataTable.display tbody tr:hover, table.dataTable.display tbody tr.odd:hover, table.dataTable.display tbody tr.even:hover { background-color: whitesmoke; } table.dataTable.hover tbody tr:hover.selected, table.dataTable.hover tbody tr.odd:hover.selected, table.dataTable.hover tbody tr.even:hover.selected, table.dataTable.display tbody tr:hover.selected, table.dataTable.display tbody tr.odd:hover.selected, table.dataTable.display tbody tr.even:hover.selected { background-color: #a9b7d1; } table.dataTable.order-column tbody tr > .sorting_1, table.dataTable.order-column tbody tr > .sorting_2, table.dataTable.order-column tbody tr > .sorting_3, table.dataTable.display tbody tr > .sorting_1, table.dataTable.display tbody tr > .sorting_2, table.dataTable.display tbody tr > .sorting_3 { background-color: #f9f9f9; } table.dataTable.order-column tbody tr.selected > .sorting_1, table.dataTable.order-column tbody tr.selected > .sorting_2, table.dataTable.order-column tbody tr.selected > .sorting_3, table.dataTable.display tbody tr.selected > .sorting_1, table.dataTable.display tbody tr.selected > .sorting_2, table.dataTable.display tbody tr.selected > .sorting_3 { background-color: #acbad4; } table.dataTable.display tbody tr.odd > .sorting_1, table.dataTable.order-column.stripe tbody tr.odd > .sorting_1 { background-color: #f1f1f1; } table.dataTable.display tbody tr.odd > .sorting_2, table.dataTable.order-column.stripe tbody tr.odd > .sorting_2 { background-color: #f3f3f3; } table.dataTable.display tbody tr.odd > .sorting_3, table.dataTable.order-column.stripe tbody tr.odd > .sorting_3 { background-color: whitesmoke; } table.dataTable.display tbody tr.odd.selected > .sorting_1, table.dataTable.order-column.stripe tbody tr.odd.selected > .sorting_1 { background-color: #a6b3cd; } table.dataTable.display tbody tr.odd.selected > .sorting_2, table.dataTable.order-column.stripe tbody tr.odd.selected > .sorting_2 { background-color: #a7b5ce; } table.dataTable.display tbody tr.odd.selected > .sorting_3, table.dataTable.order-column.stripe tbody tr.odd.selected > .sorting_3 { background-color: #a9b6d0; } table.dataTable.display tbody tr.even > .sorting_1, table.dataTable.order-column.stripe tbody tr.even > .sorting_1 { background-color: #f9f9f9; } table.dataTable.display tbody tr.even > .sorting_2, table.dataTable.order-column.stripe tbody tr.even > .sorting_2 { background-color: #fbfbfb; } table.dataTable.display tbody tr.even > .sorting_3, table.dataTable.order-column.stripe tbody tr.even > .sorting_3 { background-color: #fdfdfd; } table.dataTable.display tbody tr.even.selected > .sorting_1, table.dataTable.order-column.stripe tbody tr.even.selected > .sorting_1 { background-color: #acbad4; } table.dataTable.display tbody tr.even.selected > .sorting_2, table.dataTable.order-column.stripe tbody tr.even.selected > .sorting_2 { background-color: #adbbd6; } table.dataTable.display tbody tr.even.selected > .sorting_3, table.dataTable.order-column.stripe tbody tr.even.selected > .sorting_3 { background-color: #afbdd8; } table.dataTable.display tbody tr:hover > .sorting_1, table.dataTable.display tbody tr.odd:hover > .sorting_1, table.dataTable.display tbody tr.even:hover > .sorting_1, table.dataTable.order-column.hover tbody tr:hover > .sorting_1, table.dataTable.order-column.hover tbody tr.odd:hover > .sorting_1, table.dataTable.order-column.hover tbody tr.even:hover > .sorting_1 { background-color: #eaeaea; } table.dataTable.display tbody tr:hover > .sorting_2, table.dataTable.display tbody tr.odd:hover > .sorting_2, table.dataTable.display tbody tr.even:hover > .sorting_2, table.dataTable.order-column.hover tbody tr:hover > .sorting_2, table.dataTable.order-column.hover tbody tr.odd:hover > .sorting_2, table.dataTable.order-column.hover tbody tr.even:hover > .sorting_2 { background-color: #ebebeb; } table.dataTable.display tbody tr:hover > .sorting_3, table.dataTable.display tbody tr.odd:hover > .sorting_3, table.dataTable.display tbody tr.even:hover > .sorting_3, table.dataTable.order-column.hover tbody tr:hover > .sorting_3, table.dataTable.order-column.hover tbody tr.odd:hover > .sorting_3, table.dataTable.order-column.hover tbody tr.even:hover > .sorting_3 { background-color: #eeeeee; } table.dataTable.display tbody tr:hover.selected > .sorting_1, table.dataTable.display tbody tr.odd:hover.selected > .sorting_1, table.dataTable.display tbody tr.even:hover.selected > .sorting_1, table.dataTable.order-column.hover tbody tr:hover.selected > .sorting_1, table.dataTable.order-column.hover tbody tr.odd:hover.selected > .sorting_1, table.dataTable.order-column.hover tbody tr.even:hover.selected > .sorting_1 { background-color: #a1aec7; } table.dataTable.display tbody tr:hover.selected > .sorting_2, table.dataTable.display tbody tr.odd:hover.selected > .sorting_2, table.dataTable.display tbody tr.even:hover.selected > .sorting_2, table.dataTable.order-column.hover tbody tr:hover.selected > .sorting_2, table.dataTable.order-column.hover tbody tr.odd:hover.selected > .sorting_2, table.dataTable.order-column.hover tbody tr.even:hover.selected > .sorting_2 { background-color: #a2afc8; } table.dataTable.display tbody tr:hover.selected > .sorting_3, table.dataTable.display tbody tr.odd:hover.selected > .sorting_3, table.dataTable.display tbody tr.even:hover.selected > .sorting_3, table.dataTable.order-column.hover tbody tr:hover.selected > .sorting_3, table.dataTable.order-column.hover tbody tr.odd:hover.selected > .sorting_3, table.dataTable.order-column.hover tbody tr.even:hover.selected > .sorting_3 { background-color: #a4b2cb; } table.dataTable.nowrap th, table.dataTable.nowrap td { white-space: nowrap; } table.dataTable.compact thead th, table.dataTable.compact thead td { padding: 5px 9px; } table.dataTable.compact tfoot th, table.dataTable.compact tfoot td { padding: 5px 9px 3px 9px; } table.dataTable.compact tbody th, table.dataTable.compact tbody td { padding: 4px 5px; } table.dataTable th.dt-left, table.dataTable td.dt-left { text-align: left; } table.dataTable th.dt-center, table.dataTable td.dt-center, table.dataTable td.dataTables_empty { text-align: center; } table.dataTable th.dt-right, table.dataTable td.dt-right { text-align: right; } table.dataTable th.dt-justify, table.dataTable td.dt-justify { text-align: justify; } table.dataTable th.dt-nowrap, table.dataTable td.dt-nowrap { white-space: nowrap; } table.dataTable thead th.dt-head-left, table.dataTable thead td.dt-head-left, table.dataTable tfoot th.dt-head-left, table.dataTable tfoot td.dt-head-left { text-align: left; } table.dataTable thead th.dt-head-center, table.dataTable thead td.dt-head-center, table.dataTable tfoot th.dt-head-center, table.dataTable tfoot td.dt-head-center { text-align: center; } table.dataTable thead th.dt-head-right, table.dataTable thead td.dt-head-right, table.dataTable tfoot th.dt-head-right, table.dataTable tfoot td.dt-head-right { text-align: right; } table.dataTable thead th.dt-head-justify, table.dataTable thead td.dt-head-justify, table.dataTable tfoot th.dt-head-justify, table.dataTable tfoot td.dt-head-justify { text-align: justify; } table.dataTable thead th.dt-head-nowrap, table.dataTable thead td.dt-head-nowrap, table.dataTable tfoot th.dt-head-nowrap, table.dataTable tfoot td.dt-head-nowrap { white-space: nowrap; } table.dataTable tbody th.dt-body-left, table.dataTable tbody td.dt-body-left { text-align: left; } table.dataTable tbody th.dt-body-center, table.dataTable tbody td.dt-body-center { text-align: center; } table.dataTable tbody th.dt-body-right, table.dataTable tbody td.dt-body-right { text-align: right; } table.dataTable tbody th.dt-body-justify, table.dataTable tbody td.dt-body-justify { text-align: justify; } table.dataTable tbody th.dt-body-nowrap, table.dataTable tbody td.dt-body-nowrap { white-space: nowrap; } table.dataTable, table.dataTable th, table.dataTable td { -webkit-box-sizing: content-box; -moz-box-sizing: content-box; box-sizing: content-box; } /* * Control feature layout */ .dataTables_wrapper { position: relative; clear: both; *zoom: 1; zoom: 1; } .dataTables_wrapper .dataTables_length { float: left; } .dataTables_wrapper .dataTables_filter { float: right; text-align: right; } .dataTables_wrapper .dataTables_filter input { margin-left: 0.5em; } .dataTables_wrapper .dataTables_info { clear: both; float: left; padding-top: 0.55em; } .dataTables_wrapper .dataTables_paginate { float: right; text-align: right; } .dataTables_wrapper .dataTables_paginate .fg-button { box-sizing: border-box; display: inline-block; min-width: 1.5em; padding: 0.5em; margin-left: 2px; text-align: center; text-decoration: none !important; cursor: pointer; *cursor: hand; color: #333333 !important; border: 1px solid transparent; } .dataTables_wrapper .dataTables_paginate .fg-button:active { outline: none; } .dataTables_wrapper .dataTables_paginate .fg-button:first-child { border-top-left-radius: 3px; border-bottom-left-radius: 3px; } .dataTables_wrapper .dataTables_paginate .fg-button:last-child { border-top-right-radius: 3px; border-bottom-right-radius: 3px; } .dataTables_wrapper .dataTables_processing { position: absolute; top: 50%; left: 50%; width: 100%; height: 40px; margin-left: -50%; margin-top: -25px; padding-top: 20px; text-align: center; font-size: 1.2em; background-color: white; background: -webkit-gradient(linear, left top, right top, color-stop(0%, rgba(255, 255, 255, 0)), color-stop(25%, rgba(255, 255, 255, 0.9)), color-stop(75%, rgba(255, 255, 255, 0.9)), color-stop(100%, rgba(255, 255, 255, 0))); /* Chrome,Safari4+ */ background: -webkit-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); /* Chrome10+,Safari5.1+ */ background: -moz-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); /* FF3.6+ */ background: -ms-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); /* IE10+ */ background: -o-linear-gradient(left, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); /* Opera 11.10+ */ background: linear-gradient(to right, rgba(255, 255, 255, 0) 0%, rgba(255, 255, 255, 0.9) 25%, rgba(255, 255, 255, 0.9) 75%, rgba(255, 255, 255, 0) 100%); /* W3C */ } .dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing, .dataTables_wrapper .dataTables_paginate { color: #333333; } .dataTables_wrapper .dataTables_scroll { clear: both; } .dataTables_wrapper .dataTables_scrollBody { *margin-top: -1px; -webkit-overflow-scrolling: touch; } .dataTables_wrapper .ui-widget-header { font-weight: normal; } .dataTables_wrapper .ui-toolbar { padding: 8px; } .dataTables_wrapper:after { visibility: hidden; display: block; content: ""; clear: both; height: 0; } @media screen and (max-width: 767px) { .dataTables_wrapper .dataTables_length, .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_paginate { float: none; text-align: center; } .dataTables_wrapper .dataTables_filter, .dataTables_wrapper .dataTables_paginate { margin-top: 0.5em; } } /* * TableTools styling for jQuery UI */ div.DTTT_container { position: relative; float: left; } .DTTT_button { position: relative; float: left; margin-right: 3px; padding: 3px 10px; border: 1px solid #d0d0d0; background-color: #fff; color: #333; cursor: pointer; } .DTTT_button::-moz-focus-inner { border: none !important; padding: 0; } .DTTT_disabled { color: #999; } table.DTTT_selectable tbody tr { cursor: pointer; } div.DTTT_collection { width: 150px; background-color: #f3f3f3; overflow: hidden; z-index: 2002; box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.5); -moz-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.5); -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.5); } div.DTTT_collection button.DTTT_button, div.DTTT_collection div.DTTT_button, div.DTTT_collection a.DTTT_button { float: none; width: 100%; margin-bottom: -0.1em; } div.DTTT_collection_background { background: black; z-index: 2001; } .DTTT_print_info { position: absolute; top: 50%; left: 50%; width: 400px; height: 150px; margin-left: -200px; margin-top: -75px; text-align: center; background-color: #3f3f3f; color: white; padding: 10px 30px; opacity: 0.9; border-radius: 5px; -moz-border-radius: 5px; -webkit-border-radius: 5px; box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.5); -moz-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.5); -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.5); } .DTTT_print_info .DTTT_print_info h6 { font-weight: normal; font-size: 28px; line-height: 28px; margin: 1em; } .DTTT_print_info .DTTT_print_info p { font-size: 14px; line-height: 20px; } shinystan/inst/ShinyStan/ui_utils.R0000644000176200001440000001436013137732324017160 0ustar liggesuserssource_ui <- function(...) { source( file.path("ui_files", ...), local = TRUE )$value } save_and_close_button <- function() { tags$button( id = 'save_and_close_button', type = "button", class = "btn action-button", onclick = "window.close();", "Save & Close" ) } shinystan_version <- function() { # prevents error when deployed to shinyapps.io ver <- try(utils::packageVersion("shinystan")) if (inherits(ver, "try-error")) return() else strong(paste("Version", ver)) } logo_and_name <- function() { div(div( img( src = "wide_ensemble.png", class = "wide-ensemble", width = "100%" ) ), div( style = "margin-top: 25px", img(src = "stan_logo.png", class = "stan-logo"), div(id = "shinystan-title", "ShinyStan") )) } # save and close reminder ------------------------------------------------- save_and_close_reminder <- function(id) { helpText( id = id, p( "To make sure the changes aren't lost, use the", span(class = "save-close-reminder", "Save & Close"), "button in the top left corner to exit the app before", "closing the browser window." ) ) } # show/hide options/glossary --------------------------------------------- a_options <- function(name) { lab <- if (name == "table") "Table Options" else "Show/Hide Options" div(class = "aoptions", checkboxInput( inputId = paste0(name, "_options_show"), label = strong(style = "margin-top: 20px; color: #222222;", lab), value = FALSE )) } a_glossary <- function(id) { div(class = "aoptions", actionLink( inputId = id, label = strong(style = "margin-top: 20px; color: #222222;", "Glossary"), icon = icon("book", lib = "glyphicon") )) } # plotOutput generators --------------------------------------------------- dygraphOutput_175px <- function(id) dygraphs::dygraphOutput(id, height = "175px") plotOutput_200px <- function(id, ...) plotOutput(id, height = "200px") plotOutput_400px <- function(id, ...) plotOutput(id, height = "400px") # conditionalPanel generator for EXPLORE/density ------------------------- condPanel_dens_together <- function(...) { conditionalPanel(condition = "input.dens_chain_split == 'Together'", ...) } condPanel_dens_prior <- function(dist, ...) { cond <- paste0("input.dens_prior ==","'", dist,"'") conditionalPanel(cond, ...) } # conditional transparency settings --------------------------------------- alpha_calc_pt <- function(N) { if (N <= 100) return(1) else if (N <= 200) return(0.75) else if (N >= 1500) return(0.15) else 1 - pnorm(N/1500) } alpha_calc_lines <- function(N) { if (N < 50) return(0.5) if (N < 500) return(0.4) if (N < 1000) return(0.3) if (N < 5000) return(0.2) else return(0.1) } # transformations --------------------------------------------------------- transformation_selectInput <- function(id) { selectInput( id, label = NULL, choices = transformation_choices, selected = "identity" ) } transform_helpText <- function(var = "x") { div( if (var == "x") helpText(style = "font-size: 13px;", "To apply a transformation", "select a function and click", code("Transform")) else if (var == "x,y") helpText(style = "font-size: 13px;", "To apply transformations", "select a function for x and/or y", "and click", code("Transform")) else helpText(style = "font-size: 13px;", "To apply transformations", "select a function for x, y, and/or z", "and click", code("Transform")) ) } # diagnostics help text --------------------------------------------------- hT11 <- function(...) helpText(style = "font-size: 11px;", ...) help_interval <- hT11("Highlighted interval shows \\(\\bar{x} \\pm sd(x)\\)") help_lines <- hT11("Lines are mean (solid) and median (dashed)") help_max_td <- hT11("Horizontal line indicates the max_treedepth setting") help_points <- hT11( "Large red points indicate which (if any) iterations", "encountered a divergent transition. Yellow indicates", "a transition hitting the maximum treedepth." ) help_dynamic <- hT11( "Use your mouse to select a range in the traceplot to zoom into. ", "The other plots on the screen will update accordingly. ", "Double-click to reset." ) # ppcheck plot descriptions ---------------------------------------------- plot_descriptions <- c( plot_hists_rep_vs_obs = "Distributions of observed data and a random sample of replications", plot_dens_rep_vs_obs = "Density estimate of observed data (blue) and a random sample of replications", plot_obs_vs_avg_y_rep = "Observations vs average simulated value", plot_hist_resids = "Residuals", plot_avg_rep_vs_avg_resid_rep = "Average simulated value vs average residual", plot_test_statistics = "Distributions of test statistics \\(T(y^{rep})\\)" ) # stan manual reference --------------------------------------------------- stan_manual <- function() { helpText( style = "font-size: 12px;", "Glossary entries are compiled (with minor edits) from various excerpts of the", a( "Stan Modeling Language User's Guide and Reference Manual", href = "http://mc-stan.org/documentation/" ), "(", a(href = "http://creativecommons.org/licenses/by/3.0/", "CC BY (v3)"), ")" ) } # objects to use in ui.R and ui_files ------------------------------------- if (!exists(".SHINYSTAN_OBJECT")) { .SHINYSTAN_OBJECT <- shinystan:::.sso_env[[".SHINYSTAN_OBJECT"]] } .model_name <- slot(.SHINYSTAN_OBJECT, "model_name") .param_names <- slot(.SHINYSTAN_OBJECT, "param_names") .param_list <- .make_param_list(.SHINYSTAN_OBJECT) .param_list_with_groups <- .make_param_list_with_groups(.SHINYSTAN_OBJECT) .nChains <- slot(.SHINYSTAN_OBJECT, "n_chain") .nIter <- slot(.SHINYSTAN_OBJECT, "n_iter") .nWarmup <- slot(.SHINYSTAN_OBJECT, "n_warmup") .model_code <- slot(.SHINYSTAN_OBJECT, "model_code") .notes <- slot(.SHINYSTAN_OBJECT, "user_model_info") .has_rstanarm_ppcs <- isTRUE(.SHINYSTAN_OBJECT@misc$stanreg) && !is.null(.SHINYSTAN_OBJECT@misc$pp_check_plots) if (exists("object")) rm(object) if (exists(".SHINYSTAN_OBJECT")) rm(.SHINYSTAN_OBJECT) gc() shinystan/inst/ShinyStan/ggplot_fns.rda0000644000176200001440000000436313035516434020033 0ustar liggesusers}Yr7J;%Kk?OrRl*ٲ$}g!f$16A'/qrio'Mdprjwxbp06NzȮX4dxrK𢔍TEX%xۅgoF;xQ ]*xذjXLq}^)\W?I-:!U սhxۊf+&^Zb~gdya- reSRmYV&2X^FK3D\&䋭6ֶā4zqtWon 3F&6H6 )a ]Z'cLx%p~1"| kvvCr_4}0وF ΨR)8q&WfϜuZɖ:kq3kȹyZY U/+sTm[ eX%9K5ugB`%0/;V[AR PIWg/n̥eDKC  r\(z9isKZje6b2}JRsx;*Ǿ-mf"vjFߤ3QJJm #D%3?n5)8 5umm˺$8)EIZ2bbb" OͰi( >Hjy92f_1 XyfV+N~͡OG71 byFsg'&0i 07%L1 Y\yX:3t^&p^OQnyP՞I3"6}zfҀ89oEcmW[gyt'3"2@54 +5m9qA)[d0@A}[.h DpdgƘ dԷbSXB*~pQrȘ$Ļgr&BNQ6 R2mKQ'xl yeQ s`')*e@s`ge1mcJ* ]0؟=GHb" )ȑH6LNIŴ%@Ah#wh {"VG_s #R^rk5rwde:v}k@; :$_hેsda(Zn)e# &/@s0̚"Hҝbx8,{݁XPI-["\;N}BP#t/ sGNg5B4uAyd쇓=_qu!s"iYJi39ͮl'_e-p7`A?&"BD&nntGk]81L権痂YN~+j3<ѝCob]b+$:5KB1×䝫Җ⛨l/t2b/ ]ӥ4J67|y43'R5ݘjf$w<Gm 5DӫְBzlo/E' 0O7CҀW,7(Ƿ+nlbw!c%CWp8gN + /nQhǤM@{or0!£ I Nv NǼW7"A;W ?"Gshinystan/inst/ShinyStan/helper_functions/0000755000176200001440000000000013137732363020546 5ustar liggesusersshinystan/inst/ShinyStan/helper_functions/shinystan_helpers.R0000644000176200001440000006361413137733063024443 0ustar liggesusers# param_trace_multi ------------------------------------------------------ # trace plots for multiple parameters .param_trace_multi <- function(params = NULL, all_param_names, dat, warmup_val = 0, chain = 0, palette = "Default", rect = "Samples", rect_color = "skyblue", rect_alpha = 0.1, layout = "Long", x1, x2) { params <- .update_params_with_groups(params, all_param_names) if(length(params) == 0) { params <- dimnames(dat)$parameters[1:min(4, dim(dat)[3])] } params <- unique(params) dat <- reshape2::melt(dat[,,params, drop=FALSE]) if (!("chains" %in% colnames(dat))) { # fixes for if there's only 1 chain: dat$chains <- "chain:1" } dat$iterations <- x1:x2 if (chain != 0) dat <- subset(dat, chains == paste0("chain:",chain)) rect_xmin <- ifelse(rect == "Samples", Inf, -Inf) shading_rect <- annotate("rect", xmin = rect_xmin, xmax = warmup_val, ymin = -Inf, ymax = Inf, fill = rect_color, alpha = rect_alpha) xy_labs <- labs(y = "Value", x = "Iteration") nclrs <- length(unique(dat$chains)) lgnd_title <- "" if(palette == "Default") clrs <- scale_color_discrete(name = lgnd_title) if(palette == "Gray") clrs <- scale_color_grey(name = lgnd_title) if(palette == "Brewer (spectral)") clrs <- scale_color_brewer(name = lgnd_title, palette = "Spectral") if(palette == "Rainbow") clrs <- scale_colour_manual(name = lgnd_title, values = rainbow(nclrs)) lgnd_txt <- theme(legend.text = element_text(size = 13, face = "bold")) graph <- ggplot(dat, aes(x = iterations, y = value, color = chains)) graph <- graph + xy_labs + clrs + theme_classic() %+replace% (axis_color + axis_labs + fat_axis + h_lines + lgnd_top + lgnd_txt + strip_txt + transparent) if (rect != "None") graph <- graph + shading_rect graph <- graph + geom_line(size = 0.35) + scale_x_continuous(limits = c(x1, x2)) if (layout == "Grid") { graph <- graph + facet_wrap(~ parameters, scales = "free_y") } else { graph <- graph + facet_grid(parameters ~., scales = "free_y") } graph } # param_hist -------------------------------------------------------------- # histogram for a single parameter .param_hist <- function(param, dat, chain, binwd, transform_x = "identity", fill_color = "gray20", line_color = "gray35", title = TRUE) { ttl <- "Histogram of Posterior Draws \n" if (transform_x != "identity") { t_x <- get(transform_x) dat <- apply(dat, 2, t_x) } x_lab <- if (transform_x != "identity") paste0(transform_x, "(", param, ")") else param dat <- reshape2::melt(dat) if (!("chains" %in% colnames(dat))) { # fixes for if there's only 1 chain: dat$chains <- "chain:1" dat$iterations <- 1:nrow(dat) } if (chain != 0) dat <- subset(dat, chains == paste0("chain:",chain)) graph <- ggplot(dat, aes(x = value)) if (binwd == 0) { graph <- graph + geom_histogram(fill = fill_color, color = line_color, size = 0.2) } else { graph <- graph + geom_histogram(fill = fill_color, color = line_color, binwidth = binwd, size = 0.2) } graph <- graph + labs(x = x_lab, y = "") + theme_classic() %+replace% (title_txt + axis_color + axis_labs + fat_axis + no_yaxs + transparent) if (title == TRUE) graph <- graph + ggtitle(ttl) graph } # param_dens -------------------------------------------------------------- # density plot for a single parameter # data.frame of prior families and function names priors <- data.frame(family = c("Normal", "t", "Cauchy", "Beta", "Exponential", "Gamma", "Inverse Gamma"), fun = c("dnorm", ".dt_loc_scale", "dcauchy", "dbeta", "dexp", "dgamma", ".dinversegamma")) .param_dens <- function(param, dat, chain, fill_color = NULL, line_color = NULL, point_est = "None", CI, x_breaks = "Some", # y_breaks = "None", x_lim = NULL, chain_split = FALSE, title = TRUE, transform_x = "identity", prior_fam = "None", prior_params) { ttl <- "Kernel Density Estimate \n" if (transform_x != "identity") { t_x <- get(transform_x) dat <- apply(dat, 2, t_x) } x_lab <- if (transform_x != "identity") paste0(transform_x, "(", param, ")") else param dat <- reshape2::melt(dat) if (!("chains" %in% colnames(dat))) { # fixes for if there's only 1 chain: dat$chains <- "chain:1" dat$iterations <- 1:nrow(dat) } if (chain != 0) dat <- subset(dat, chains == paste0("chain:",chain)) Mean <- mean(dat$value) Median <- median(dat$value) dens_dat <- with(density(dat$value), data.frame(x,y)) MAP <- with(dens_dat, x[which.max(y)]) fclr <- ifelse(is.null(fill_color), "black", fill_color) lclr <- ifelse(is.null(line_color), "lightgray", line_color) many_breaks <- function(x) pretty(x, n = 15) too_many_breaks <- function(x) pretty(x, n = 35) if(x_breaks == "None") x_scale <- scale_x_continuous(breaks = NULL) if(x_breaks == "Some") x_scale <- scale_x_continuous() if(x_breaks == "Many") x_scale <- scale_x_continuous(breaks = many_breaks) if (chain == 0 & chain_split == TRUE) { graph <- ggplot(dat, aes(x = value, color = chains, fill = chains)) if (prior_fam != "None") { graph <- graph + stat_function(alpha=0.75,color = "black", fun = as.character(priors$fun[priors$family==prior_fam]), args = prior_params, show_guides = TRUE) } graph <- graph + geom_density(alpha = 0.15) + scale_color_discrete("") + scale_fill_discrete("") + labs(x = x_lab, y = "") + x_scale + # y_scale + theme_classic() %+replace% (title_txt + axis_color + axis_labs + fat_axis + no_yaxs + transparent) if (title == TRUE) graph <- graph + ggtitle(ttl) return(graph) } graph <- ggplot(dens_dat, aes(x = x, ymax = y)) if (prior_fam != "None") { graph <- graph + stat_function(fun = as.character(priors$fun[priors$family==prior_fam]), args = prior_params) } graph <- graph + labs(x = param, y = "") + x_scale + # y_scale + labs(x = x_lab, y = "") + geom_ribbon(ymin = 0, fill = fclr, color = lclr, alpha = if (prior_fam == "None") 1 else 0.85) + theme_classic() %+replace% (title_txt + axis_color + axis_labs + fat_axis + no_yaxs + transparent) if (title == TRUE) graph <- graph + ggtitle(ttl) if (point_est != "None") { graph <- graph + annotate("segment", x = get(point_est), xend = get(point_est), y = 0, yend = max(dens_dat$y), color = lclr, lwd = 1, lty = 2) } if (CI != "None") { lev <- (1 - as.numeric(CI))/2 quant <- quantile(dat$value, probs = c(lev, 1 - lev)) graph <- (graph + annotate("segment", x = quant, xend = quant, y = 0, yend = max(dens_dat$y), color = lclr, lty = rep(1:length(CI),2)) ) } if (!is.null(x_lim)) graph <- graph + scale_x_continuous(limits = x_lim) graph } # autocorr ---------------------------------------------------- .ac_fun <- function(x, lag.max, partial = FALSE) { if (!partial) acf(x, lag.max = lag.max, plot = FALSE)$acf[,, 1L] else pacf(x, lag.max = lag.max, plot = FALSE)$acf[,, 1L] } .ac_plot_data <- function(dat, lags, partial = FALSE) { nc <- length(unique(dat$chains)) ac_list <- tapply(dat$value, INDEX = dat$chains, FUN = .ac_fun, lag.max = lags, partial = partial, simplify = FALSE) nl <- if (partial) lags else lags + 1 ch <- factor(rep(1:nc, each = nl), labels = paste0("chain:", 1:nc)) ll <- rep(seq(if (partial) 1 else 0, lags), nc) data.frame(chains = ch, ac = do.call("c", args = ac_list), lag = ll) } .ac_plot_data_multi <- function(dat, lags, partial = FALSE) { nc <- length(unique(dat$chains)) np <- length(unique(dat$parameters)) ac_list <- tapply(dat$value, INDEX = list(dat$chains, dat$parameters), FUN = .ac_fun, lag.max = lags, partial = partial, simplify = FALSE) nl <- if (partial) lags else lags + 1 ch <- factor(rep(rep(1:nc, each = nl), np), labels = paste0("chain:", 1:nc)) ll <- rep(seq(if (partial) 1 else 0, lags), nc * np) pp <- factor(rep(1:np, each = nc * nl), labels = levels(dat$parameters)) data.frame(parameters = pp, chains = ch, ac = do.call("c", args = ac_list), lag = ll) } # markov chain autocorrelation plot for single parameters .autocorr_single_plot <- function(samps, lags) { dat <- reshape2::melt(samps) if (!("chains" %in% colnames(dat))) { # fixes for if there's only 1 chain: dat$chains <- "chain:1" dat$iterations <- 1:nrow(dat) } ac_dat <- .ac_plot_data(dat, lags) ac_labs <- labs(x = "Lag", y = "Autocorrelation") ac_theme <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_lgnd + transparent) y_scale <- scale_y_continuous(breaks = seq(0, 1, 0.25), labels = c("0","","0.5","","")) graph <- ggplot(ac_dat, aes(x = lag, y = ac)) graph <- graph + geom_bar(position = "identity", stat = "identity", fill = base_fill) + y_scale + ac_theme graph } # markov chain autocorrelation plot for multiple parameters .autocorr_plot <- function(samps, partial = FALSE, lags = 25, flip = FALSE, combine_chains = FALSE) { params <- dimnames(samps)$parameters nParams <- length(params) nChains <- dim(samps)[2L] dat <- reshape2::melt(samps) if (!("chains" %in% colnames(dat))) { # fixes for if there's only 1 chain: dat$chains <- "chain:1" dat$iterations <- 1:nrow(dat) } ac_type <- if (partial) "partial" else "correlation" if (nParams == 1) ac_dat <- .ac_plot_data(dat, lags = lags, partial = partial) else ac_dat <- .ac_plot_data_multi(dat, lags = lags, partial = partial) ac_labs <- labs(x = "Lag", y = if (partial) "Partial autocorrelation" else "Autocorrelation") ac_theme <- theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_lgnd + strip_txt + transparent) y_scale <- scale_y_continuous(breaks = seq(0, 1, 0.25), labels = c("0","","0.5","","")) title_theme <- theme(plot.title = element_text(face = "bold", size = 18)) if (combine_chains) { graph <- ggplot(ac_dat, aes(x= lag, y = ac)) graph <- graph + geom_bar(position = "identity", stat = "identity", fill = base_fill, size = 0.4) + y_scale + ac_labs + ac_theme if (nParams == 1) return(graph + ggtitle(paste(params, "\n")) + title_theme) else return(graph + facet_wrap(~parameters)) } graph <- ggplot(ac_dat, aes(x = lag, y = ac, color = factor(chains), fill = factor(chains))) graph <- graph + geom_bar(position = "identity", stat = "identity", size = 0.4) + scale_fill_manual(values = rep(base_fill, nChains)) + scale_color_manual(values = rep(vline_base_clr, nChains)) + y_scale + ac_labs + ac_theme if (nParams == 1) { graph <- graph + facet_wrap(~chains) + ggtitle(paste(params, "\n")) + title_theme return(graph) } else { # nParams > 1 while(is.null(flip)) return() graph <- graph + if (flip) facet_grid(chains ~ parameters) else facet_grid(parameters ~ chains) return(graph) } } # multiparam_plot -------------------------------------------------- # main plot of multiple parameters .multiparam_plot <- function(samps, params = NULL, all_param_names, show_density, show_ci_line, CI.level = 0.5, show.level = 0.95, point_est, rhat_values, color_by_rhat, rhat_palette, fill_color, outline_color, est_color) { # params <- .update_params_with_regex(params, all_param_names) params <- .update_params_with_groups(params, all_param_names) .e <- environment() dim.samps <- dim(samps) #nIter, nChain, nParam if(length(params) == 0) { params <- dimnames(samps)$parameters[1:min(12, dim.samps[3])] if ("log-posterior" %in% params) { params <- params[-which(params == "log-posterior")] } } params <- unique(params) Blues <- c("#C6DBEF", "#4292C6", "#08306B") Grays <- c("#D9D9D9", "#737373", "#000000") Greens <- c("#C7E9C0", "#41AB5D", "#00441B") Oranges <- c("#FDD0A2", "#F16913", "#7F2704") Purples <- c("#DADAEB", "#807DBA", "#3F007D") Reds <- c("#FCBBA1", "#EF3B2C", "#67000D") rhat_pal <- get(rhat_palette) rhat_id <- ifelse(rhat_values < 1.05, "A", ifelse(rhat_values < 1.1, "B", "C")) rhat_id <- factor(rhat_id[params], levels = c("A","B", "C"), labels = c("<1.05", "<1.1", ">1.1")) rhat_colors <- scale_color_manual(name = bquote(hat(R)), values = rhat_pal, drop = FALSE) rhat_lgnd <- theme(legend.position = "top", legend.title = element_text(size = 13, face = "bold"), legend.text = element_text(size = 12)) nParams <- length(params) nIter <- prod(dim.samps[1:2]) samps.use <- array(samps[,,params], c(nIter, nParams)) colnames(samps.use) <- params probs.use <- c(0.5 - show.level / 2, 0.5 - CI.level / 2, 0.5, 0.5 + CI.level / 2, 0.5 + show.level / 2) samps.quantile <- t(apply(samps.use, 2, quantile, probs = probs.use)) y <- as.numeric(seq(nParams, 1, by = -1)) xlim.use <- c(min(samps.quantile[,1]), max(samps.quantile[,5])) xrange <- diff(xlim.use) xlim.use[1] <- xlim.use[1] - 0.05 * xrange xlim.use[2] <- xlim.use[2] + 0.05 * xrange xy.df <- data.frame(params, y, samps.quantile) colnames(xy.df) <- c("params", "y", "ll", "l", "m", "h", "hh") if (point_est == "Mean") { xy.df$m <- unname(colMeans(samps.use)) } p.base <- ggplot(xy.df, environment = .e) p.name <- scale_y_continuous(breaks = y, labels = params, limits = c(0.5, nParams + 1)) p.theme <- theme(axis.title= element_blank(), panel.background = element_blank(), panel.border = element_blank(), axis.ticks.y = element_blank(), axis.text= element_text(size=12), axis.text.y= element_text(face = "bold"), axis.line= element_line(size = 4, color = axis_line_color), axis.line.y= element_line(size = 0.5, color = axis_line_color), legend.position = "none", panel.grid.major = element_line(size = 0.4), panel.grid.minor.y = element_blank()) p.all <- p.base + xlim(xlim.use) + p.name + theme_bw() + p.theme + transparent if (show_ci_line | show_density) { p.ci <- geom_segment(aes(x = ll, xend = hh, y = y, yend = y), colour = outline_color) p.all <- p.all + p.ci } if (show_density) { nPoint.den <- 512 #plot density y.den <- matrix(0, nrow = nPoint.den, ncol = nParams) x.den <- matrix(0, nrow = nPoint.den, ncol = nParams) for(i in 1:nParams){ d.temp <- density(samps.use[,i], from = samps.quantile[i,1], to = samps.quantile[i,5], n = nPoint.den) x.den[,i] <- d.temp$x y.max <- max(d.temp$y) y.den[,i] <- d.temp$y / y.max * 0.8 + y[i] } df.den <- data.frame(x = as.vector(x.den), y = as.vector(y.den), name = rep(params, each = nPoint.den)) p.den <- geom_line(data = df.den, aes(x = x, y = y, group = name), color = outline_color) #shaded interval y.poly <- matrix(0, nrow = nPoint.den + 2, ncol = nParams) x.poly <- matrix(0, nrow = nPoint.den + 2, ncol = nParams) for(i in 1:nParams){ d.temp <- density(samps.use[,i], from = samps.quantile[i,2], to = samps.quantile[i,4], n = nPoint.den) x.poly[,i] <- c(d.temp$x[1], as.vector(d.temp$x), d.temp$x[nPoint.den]) y.max <- max(d.temp$y) y.poly[,i] <- as.vector(c(0, as.vector(d.temp$y) / y.max * 0.8, 0) + y[i]) } df.poly <- data.frame(x = as.vector(x.poly), y = as.vector(y.poly), name = rep(params, each = nPoint.den + 2)) p.poly <- geom_polygon(data = df.poly, aes(x = x, y = y, group = name, fill = y)) p.col <- scale_fill_gradient(low = fill_color, high = fill_color, guide = "none") #point estimator if (color_by_rhat) { p.point <- geom_segment(aes(x = m, xend = m, y = y, yend = y + 0.25, color = rhat_id), size = 1.5) p.all + p.poly + p.den + p.col + p.point + rhat_colors + rhat_lgnd } else { p.point <- geom_segment(aes(x = m, xend = m, y = y, yend = y + 0.25), colour = est_color, size = 1.5) p.all + p.poly + p.den + p.col + p.point } } else { p.ci.2 <- geom_segment(aes(x = l, xend = h, y = y, yend = y), colour = fill_color, size = 2) if (color_by_rhat) { p.point <- geom_point(aes(x = m, y = y, fill = rhat_id), color = "black", shape = 21, size = 4) p.all + p.ci.2 + p.point + rhat_colors + rhat_lgnd } else { p.point <- geom_point(aes(x = m, y = y), size = 4, color = fill_color, fill = est_color, shape = 21) p.all + p.ci.2 + p.point } } } # histogram of rhat, n_eff/N or mcse/sd ----------------------------------- .rhat_neff_mcse_hist <- function(dat, which, N) { # samps: post-warmup samples xlab <- switch(which, rhat = "Rhat statistic", n_eff = "Effective sample size / iterations", mcse = "Monte Carlo se / posterior sd" ) my_labs <- labs(y = "", x = xlab) base_fill graph <- qplot(x = x, data = dat, color = I(vline_base_clr), fill = I(base_fill), size = I(0.2)) graph <- graph + my_labs + theme_classic() %+replace% (axis_color + axis_labs + fat_axis + no_yaxs + transparent) graph } # n_eff_warnings ----------------------------------------------------------- .n_eff_warnings <- function(summary, threshold = 10, N_total = NULL) { n_eff <- summary[,"n_eff"] warn_params <- names(which(n_eff / N_total < threshold / 100)) ll <- length(warn_params) if (ll == 0) "None" else paste0(warn_params, collapse = ", ") } # rhat_warnings ----------------------------------------------------------- .rhat_warnings <- function(summary, threshold = 1.10) { rhat <- summary[,"Rhat"] warn_params <- names(which(rhat > threshold)) ll <- length(warn_params) if (ll == 0) "None" else paste0(warn_params, collapse = ", ") } # mcse_over_sd_warnings ----------------------------------------------------------- .mcse_over_sd_warnings <- function(summary, threshold = 10) { dat <- summary[,c("se_mean", "sd")] warn_params <- names(which(dat[,1] > (threshold/100) * dat[,2])) ll <- length(warn_params) if (ll == 0) "None" else paste0(warn_params, collapse = ", ") } # dynamic trace plot ------------------------------------------------------ .param_trace_dynamic <- function(param_samps, chain, warmup_val, warmup_shade = TRUE, stack = FALSE, grid = FALSE, x_lab = NULL, y_lab = NULL) { dim_samps <- dim(param_samps) if (is.null(dim_samps)) nChains <- 1 else nChains <- dim_samps[2] if (nChains == 1) { param_chains <- xts::as.xts(ts(param_samps, start = 1)) } else { if (chain != 0) { param_samps <- param_samps[, chain] param_chains <- xts::as.xts(ts(param_samps, start = 1)) } else { param_chains <- xts::as.xts(ts(param_samps[,1], start = 1)) for (i in 2:nChains) { param_chains <- cbind(param_chains, xts::as.xts(ts(param_samps[,i], start = 1))) } colnames(param_chains) <- paste0("Chain", 1:nChains) } } `%>%` <- dygraphs::`%>%` shade_to <- if (warmup_shade) paste0(warmup_val,"-01-01") else "0001-01-01" y_axis_label_remove <- if (stack) "white" else NULL clrs <- color_vector(nChains) if (chain != 0) clrs <- clrs[chain] dygraphs::dygraph(param_chains, xlab = x_lab, ylab = y_lab) %>% dygraphs::dyAxis("y", axisLabelColor = y_axis_label_remove) %>% dygraphs::dyAxis("x", axisLabelColor = "white") %>% dygraphs::dyOptions(colors = clrs, stackedGraph = stack, drawGrid = grid, animatedZooms = TRUE, axisLineColor = axis_line_color) %>% dygraphs::dyLegend(show = "never") %>% dygraphs::dyHighlight(highlightCircleSize = 4, highlightSeriesBackgroundAlpha = 1/3, hideOnMouseOut = TRUE, highlightSeriesOpts = list(strokeWidth = 1.75)) %>% dygraphs::dyRoller(rollPeriod = 1) %>% dygraphs::dyShading(from = "0001-01-01", to = shade_to, color = "#EFEFEF", axis = "x") %>% dygraphs::dyCSS(css = "css/ShinyStan_dygraphs.css") } # trivariate_plot --------------------------------------------------------- .param_trivariate <- function(samps, params, transform_x = "identity", transform_y = "identity", transform_z = "identity", pt_size = 1, pt_color = "gray35", show_grid = TRUE, flip_y = TRUE) { nParams <- 3 dim_samps <- dim(samps) nIter <- dim_samps[1] * dim_samps[2] samps_use <- array(samps[,, params], c(nIter, nParams)) colnames(samps_use) <- params t_x <- get(transform_x) t_y <- get(transform_y) t_z <- get(transform_z) if (transform_x != "identity") { samps_use[,1] <- t_x(samps_use[,1]) colnames(samps_use)[1] <- paste0(transform_x, "(", params[1], ")") } if (transform_y != "identity") { samps_use[,2] <- t_y(samps_use[,2]) colnames(samps_use)[2] <- paste0(transform_y, "(", params[2], ")") } if (transform_z != "identity") { samps_use[,3] <- t_z(samps_use[,3]) colnames(samps_use)[3] <- paste0(transform_z, "(", params[3], ")") } threejs::scatterplot3js(samps_use, size = pt_size, color = pt_color, grid = show_grid, flip.y = flip_y) } # bivariate plot ---------------------------------------------------------- .bivariate_plot <- function(samps, sp = NULL, max_td = NULL, param, param2, pt_alpha = 0.10, pt_size = 2, pt_shape = 10, pt_color = "gray20", ellipse_color = "black", ellipse_lev = "None", ellipse_lty = 1, ellipse_lwd = 1, ellipse_alpha = 1, lines = "back", lines_color = "gray", lines_alpha, points = TRUE, transform_x = "identity", transform_y = "identity" ){ shape_translator <- function(x) { shape <- if (x >= 6) x + 9 else x shape } params <- c(param, param2) nParams <- 2 nIter <- dim(samps)[1] * dim(samps)[2] samps_use <- array(samps[,,params], c(nIter, nParams)) colnames(samps_use) <- params t_x <- get(transform_x) # t_x <- function(x) eval(parse(text = transform_x)) t_y <- get(transform_y) x_lab <- if (transform_x != "identity") paste0(transform_x, "(", param, ")") else param y_lab <- if (transform_y != "identity") paste0(transform_y, "(", param2, ")") else param2 param_labs <- labs(x = x_lab, y = y_lab) dat <- data.frame( x = if (transform_x == "identity") samps_use[,param] else t_x(samps_use[,param]), y = if (transform_y == "identity") samps_use[,param2] else t_y(samps_use[,param2])) if (!is.null(sp)) { dat$divergent <- c(sapply(sp, FUN = function(y) y[, "divergent__"])) dat$hit_max_td <- if (is.null(max_td)) 0 else c(sapply(sp, FUN = function(y) as.numeric(y[, "treedepth__"] == max_td))) } else { dat$divergent <- 0 dat$hit_max_td <- 0 } graph <- ggplot(dat, aes(x = x, y = y, xend=c(tail(x, n=-1), NA), yend=c(tail(y, n=-1), NA))) if (lines == "hide") { graph <- graph + geom_point(alpha = pt_alpha, size = pt_size, shape = shape_translator(pt_shape), color = pt_color) } else { # if lines = "back" or "front" if (lines == "back") { graph <- graph + geom_path(alpha = lines_alpha, color = lines_color) + geom_point(alpha = pt_alpha, size = pt_size, shape = shape_translator(pt_shape), color = pt_color) } else { # lines = "front" graph <- graph + geom_point(alpha = pt_alpha, size = pt_size, shape = shape_translator(pt_shape), color = pt_color) + geom_path(alpha = lines_alpha, color = lines_color) } } if (ellipse_lev != "None") graph <- graph + stat_ellipse(level = as.numeric(ellipse_lev), color = ellipse_color, linetype = ellipse_lty, size = ellipse_lwd, alpha = ellipse_alpha) if (!all(dat$divergent == 0)) graph <- graph + geom_point(data = subset(dat, divergent == 1), aes(x,y), size = pt_size + 0.5, shape = 21, color = "#570000", fill = "#ae0001") if (!all(dat$hit_max_td == 0)) graph <- graph + geom_point(data = subset(dat, hit_max_td == 1), aes(x,y), size = pt_size + 0.5, shape = 21, color = "#5f4a13", fill = "#eeba30") graph + param_labs + theme_classic() %+replace% (no_lgnd + axis_labs + fat_axis + axis_color + transparent) } shinystan/inst/ShinyStan/helper_functions/summary_stats_helpers.R0000644000176200001440000000457413035516434025334 0ustar liggesusers# param_summary ----------------------------------------------------------- # summary stats for a single parameter .param_summary <- function(param, summary) { stats <- c("Rhat", "n_eff", "mean", "sd", "2.5%", "50%", "97.5%") out <- summary[param, stats] out["n_eff"] <- round(out["n_eff"]) outmat <- matrix(out, 1, length(out)) colnames(outmat) <- names(out) rownames(outmat) <- NULL outmat } # all_summary ------------------------------------------------------------- # summary stats for all parameters .all_summary <- function(summary, digits = 2, cols) { if (missing(cols)) cols <- seq_len(ncol(summary)) df <- as.data.frame(summary[, cols]) df <- round(df, digits) if ("n_eff" %in% cols) df[, "n_eff"] <- round(df[, "n_eff"]) df } # tex_summary ------------------------------------------------------------- # prep for latex table .tex_summary <- function(summary, params, cols) { df <- as.data.frame(summary[, cols]) if ("n_eff" %in% cols) df[, "n_eff"] <- round(df[, "n_eff"]) cbind(Parameter = rownames(df), df) } # sampler_summary --------------------------------------------------------- .sampler_stuff <- function(X, param, report) { sapply_funs <- function(x, fun_name) { funs <- list( maxf = function(x) max(x[, param]), minf = function(x) min(x[, param]), meanf = function(x) mean(x[, param]), sdf = function(x) sd(x[, param]) ) sapply(x, FUN = funs[[fun_name]]) } out <- if (report == "maximum") sapply_funs(X, "maxf") else if (report == "minimum") sapply_funs(X, "minf") else if (report == "sd") sapply_funs(X, "sdf") else sapply_funs(X, "meanf") names(out) <- paste0("chain",1:length(out)) out } # summary statistics for algorithm=NUTS or algorithm=HMC sampler parameters .sampler_summary <- function(sampler_params, warmup_val, report = "average", digits = 4){ params <- colnames(sampler_params[[1]]) out <- sapply(params, FUN = function(p) .sampler_stuff(X = sampler_params, param = p, report = report)) if (length(dim(out)) > 1) { # if multiple chains out <- rbind("All chains" = colMeans(out), out) colnames(out) <- gsub("__","",colnames(out)) out <- formatC(round(out, digits), format = 'f', digits = digits) } else { # if only 1 chain names(out) <- gsub("__.chain1", "", names(out)) out <- round(t(out), digits) } out } shinystan/inst/ShinyStan/helper_functions/hmc_diagnostics_helpers.R0000644000176200001440000002136613137732363025561 0ustar liggesusersthm <- theme_classic() %+replace% (no_lgnd + fat_axis + axis_labs + transparent) thm_no_yaxs <- thm + no_yaxs .sampler_param_pw <- function(sp, which = "accept_stat__", warmup_val) { if (!which %in% colnames(sp[[1]])) return(NULL) sp_pw <- lapply(1:length(sp), function(i) { out <- sp[[i]][, which] }) sp_mat <- do.call("cbind", sp_pw) colnames(sp_mat) <- paste0("chain:", 1:ncol(sp_mat)) sp_mat <- cbind(iterations = seq(from = warmup_val + 1, to = warmup_val + nrow(sp_mat)), sp_mat) as.data.frame(sp_mat) } .sampler_param_vs_param <- function(p, sp, divergent = NULL, hit_max_td = NULL, p_lab, sp_lab, chain = 0, violin = FALSE, smoother = FALSE) { xy_labs <- labs( y = if (missing(p_lab)) NULL else p_lab, x = if (missing(sp_lab)) NULL else sp_lab ) df <- data.frame(sp = do.call("c", sp), p = c(p)) if (violin) df$sp <- as.factor(round(df$sp, 4)) if (!is.null(divergent)) df$divergent <- do.call("c", divergent) if (!is.null(hit_max_td)) df$hit_max_td <- do.call("c", hit_max_td) base <- ggplot(df, aes(sp,p)) + xy_labs + thm if (chain == 0) { if (violin) graph <- base + geom_violin(color = vline_base_clr, fill = base_fill) else { graph <- base + geom_point(alpha = 1/3, color = pt_outline_clr, fill = base_fill, shape = 19) if (smoother) graph <- graph + stat_smooth(color = overlay_fill, se = FALSE) if (!is.null(divergent)) graph <- graph + geom_point(data = subset(df, divergent == 1), aes(sp,p), color = divergent_clr, fill = divergent_fill, size = 3, shape = div_and_hit_shape) if (!is.null(hit_max_td)) graph <- graph + geom_point(data = subset(df, hit_max_td == 1), aes(sp,p), color = hit_max_td_clr, fill = hit_max_td_fill, size = 3, shape = div_and_hit_shape) } return(graph) } chain_data <- data.frame(sp = sp[, chain], p = p[, chain]) if (!is.null(divergent)) chain_data$div <- divergent[, chain] if (!is.null(hit_max_td)) chain_data$hit <- hit_max_td[, chain] chain_clr <- color_vector_chain(ncol(sp))[chain] chain_fill <- chain_clr if (violin) { chain_data$sp <- as.factor(round(chain_data$sp, 4)) graph <- base + geom_violin(color = vline_base_clr, fill = base_fill) + geom_violin(data = chain_data, aes(sp, p), color = chain_clr, fill = chain_fill, alpha = 0.5) return(graph) } graph <- base + geom_point(alpha = 1/3, color = pt_outline_clr, fill = base_fill, shape = 19) if (smoother) graph <- graph + stat_smooth(color = overlay_fill, se = FALSE) graph <- graph + geom_point(data = chain_data, aes(sp,p), color = chain_fill, alpha = 0.5) if (smoother) graph <- graph + stat_smooth(data = chain_data, aes(sp,p), color = chain_fill, se = FALSE) if (!is.null(divergent)) graph <- graph + geom_point(data = subset(chain_data, div == 1), aes(sp,p), color = divergent_clr, fill = divergent_fill, size = 3, shape = div_and_hit_shape) if (!is.null(hit_max_td)) graph <- graph + geom_point(data = subset(chain_data, hit == 1), aes(sp,p), color = hit_max_td_clr, fill = hit_max_td_fill, size = 3, shape = div_and_hit_shape) graph } .sampler_param_vs_sampler_param_violin <- function(df_x, df_y, lab_x, lab_y, chain = 0) { xy_labs <- labs(y = lab_y, x = lab_x) df <- data.frame(x = do.call("c", df_x), y = do.call("c", df_y)) df$x <- as.factor(df$x) base <- ggplot(df, aes(x,y)) + xy_labs + thm graph <- base + geom_violin(color = vline_base_clr, fill = base_fill) if (chain == 0) return(graph) chain_clr <- color_vector_chain(ncol(df_x))[chain] chain_fill <- chain_clr chain_data <- data.frame(x = as.factor(df_x[, chain]), y = df_y[, chain]) graph + geom_violin(data = chain_data, aes(x,y), color = chain_clr, fill = chain_fill, alpha = 0.5) } .p_hist <- function(df, lab, chain = 0) { thm <- thm_no_yaxs mdf <- reshape2::melt(df, id.vars = "iterations") base <- ggplot(mdf, aes(x = value)) + geom_histogram(aes_string(y="..density.."), binwidth = diff(range(mdf$value))/30, fill = base_fill, color = vline_base_clr, size = 0.2) + labs(x = if (missing(lab)) NULL else lab, y = "") + thm if (chain == 0) { graph <- base + geom_vline(xintercept = mean(mdf$value), color = vline_base_clr, size = .8) + geom_vline(xintercept = median(mdf$value), color = vline_base_clr, lty = 2, size = 1) return(graph) } chain_data <- subset(mdf, variable == paste0("chain:",chain)) chain_clr <- color_vector_chain(ncol(df) - 1)[chain] chain_fill <- chain_clr base + thm + geom_histogram(data = chain_data, aes_string(y="..density.."), binwidth = diff(range(chain_data$value))/30, fill = chain_fill, alpha = 0.5) + geom_vline(xintercept = mean(chain_data$value), color = chain_clr, size = .8) + geom_vline(xintercept = median(chain_data$value), color = chain_clr, lty = 2, size = 1) } .treedepth_ndivergent_hist <- function(df_td, df_nd, chain = 0, divergent = c("All", 0, 1)) { plot_title <- theme(plot.title = element_text(size = 11, hjust = 0)) plot_theme <- thm_no_yaxs + plot_title x_lab <- if (divergent == "All") "Treedepth (All)" else paste0("Treedepth (Divergent = ", divergent,")") plot_labs <- labs(x = x_lab, y = "") mdf_td <- reshape2::melt(df_td, id.vars = "iterations") mdf_nd <- reshape2::melt(df_nd, id.vars = "iterations") mdf <- cbind(mdf_td, div = mdf_nd$value) plot_data <- if (divergent == "All") mdf else subset(mdf, div == divergent) if (nrow(plot_data) == 0) return(NULL) graph <- ggplot(plot_data, aes(x = factor(value)), na.rm = TRUE) + geom_bar(aes(y=..count../sum(..count..)), width=1, fill = base_fill, color = vline_base_clr, size = 0.2) + plot_labs + plot_theme if (chain == 0) return(graph) chain_clr <- color_vector_chain(ncol(df_td) - 1)[chain] chain_fill <- chain_clr chain_data <- subset(plot_data, variable == paste0("chain:",chain)) graph + geom_bar(data = chain_data, aes(y=..count../sum(..count..)), fill = chain_fill, alpha = 0.5, width = 1) } .dynamic_trace_diagnostics <- function(param_samps, param_name, chain = 0, stack = FALSE, grid = FALSE, group = NULL) { dim_samps <- dim(param_samps) if (is.null(dim_samps)) { nChains <- 1 } else { nChains <- dim_samps[2] } if (nChains == 1) { param_chains <- xts::as.xts(ts(param_samps, start = 1)) } else { if (chain != 0) { param_samps <- param_samps[, chain] param_chains <- xts::as.xts(ts(param_samps, start = 1)) } else { param_chains <- xts::as.xts(ts(param_samps[,1], start = 1)) for (i in 2:nChains) { param_chains <- cbind(param_chains, xts::as.xts(ts(param_samps[,i], start = 1))) } colnames(param_chains) <- paste0("Chain", 1:nChains) } } `%>%` <- dygraphs::`%>%` y_axis_label_remove <- if (stack) "white" else NULL step_plot <- param_name %in% c("Treedepth", "Divergent") fill_graph <- param_name == "Divergent" stroke_width <- if (step_plot) 0.33 else 0.75 clrs <- color_vector(nChains) if (chain != 0) clrs <- clrs[chain] dygraphs::dygraph(param_chains, xlab = param_name, ylab = NULL, group = group) %>% dygraphs::dyOptions(colors = clrs, stackedGraph = stack, drawGrid = grid, stepPlot = step_plot, #axisLabelFontSize = 11, fillGraph = fill_graph, fillAlpha = 0.5, strokeWidth = 0.75, animatedZooms = TRUE, drawXAxis = TRUE, drawYAxis = !fill_graph, drawAxesAtZero = TRUE, axisLineColor = "black", retainDateWindow = TRUE) %>% dygraphs::dyAxis("x", pixelsPerLabel = 1e7, axisLineWidth = 3) %>% dygraphs::dyAxis("y", pixelsPerLabel = 30, axisLabelWidth = 30) %>% dygraphs::dyLegend(show = "never") %>% dygraphs::dyHighlight(highlightCircleSize = 2, highlightSeriesBackgroundAlpha = 1/3, hideOnMouseOut = TRUE, highlightSeriesOpts = list(strokeWidth = 1.5)) %>% dygraphs::dyCSS(css = "css/ShinyStan_dygraphs.css") } shinystan/inst/ShinyStan/helper_functions/gg_theme_elements.R0000644000176200001440000000437713035516434024353 0ustar liggesusers# ggplot theme elements to be used as needed # transparent background -------------------------------------------------- transparent <- theme( panel.background = element_blank(), plot.background = element_blank() ) # axes and titles --------------------------------------------------------- axis_line_color <- "gray20" axis_color <- theme( axis.line = element_line(color = axis_line_color) ) axis_labs <- theme( axis.title = element_text(face = "bold", size = 13) ) title_txt <- theme( plot.title = element_text(face = "bold", size = 14) ) fat_axis <- theme( axis.line.x = element_line(size = 3, color = axis_line_color), axis.line.y = element_line(size = 0.5, color = axis_line_color) ) no_yaxs <- theme( axis.line.y = element_blank(), axis.ticks.y = element_blank(), axis.text.y = element_blank() ) # facet strips ------------------------------------------------------------ strip_txt <- theme( strip.text = element_text(size = 12, face = "bold", color = "white"), strip.background = element_rect(color = axis_line_color, fill = axis_line_color) ) # horizontal and vertical lines ------------------------------------------- h_lines <- theme( panel.grid.major = element_line(size = 0.10, linetype = 3, color = "turquoise4"), panel.grid.major.x = element_blank() ) v_lines <- theme( panel.grid.major = element_line(size = 0.25, linetype = 3, color = "turquoise4"), panel.grid.major.y = element_blank() ) # legends ----------------------------------------------------------------- no_lgnd <- theme( legend.position = "none" ) lgnd_bot <- theme( legend.position = "bottom", legend.background = element_blank() ) lgnd_top <- theme( legend.position = "top", legend.background = element_blank() ) lgnd_left <- theme( legend.position = "left", legend.background = element_blank() ) lgnd_right <- theme( legend.position = "right", legend.background = element_blank() ) # colors and shapes ------------------------------------------------------- base_fill <- "#66a7e0" overlay_fill <- "#006dcc" vline_base_clr <- "#006dcc" pt_outline_clr <- "#328ad6" divergent_fill <- "#ae0001" hit_max_td_fill <- "#eeba30" divergent_clr <- "black" hit_max_td_clr <- "black" div_and_hit_shape <- 21 blue_color <- "#006dcc" shinystan/inst/ShinyStan/ui_files/0000755000176200001440000000000013137714217016774 5ustar liggesusersshinystan/inst/ShinyStan/ui_files/hist_customize.R0000644000176200001440000000224613035516434022172 0ustar liggesusersshinyjs::hidden(div( id = "hist_options", wellPanel( class = "optionswell", hr(class = "hroptions"), strongBig("Transformation"), transform_helpText("x"), fluidRow( column(width = 4, transformation_selectInput("hist_transform_x")), column( width = 2, actionButton("hist_transform_x_go", label = "Transform", class = "transform-go") ) ), hr(class = "hroptions"), fluidRow( column( width = 2, numericInput( "hist_chain", label = strongMed("Chain"), min = 0, max = .nChains, step = 1, value = 0 ) ), column( width = 4, sliderInput( "hist_binwd", label = strongMed("Binwidth (0 = default)"), min = 0, value = 0, max = 50, step = 0.05, ticks = FALSE ) ), column( width = 3, colourpicker::colourInput("hist_fill_color", strongMed("Fill"), base_fill) ), column( width = 3, colourpicker::colourInput("hist_line_color", strongMed("Line"), vline_base_clr) ) ) ) )) shinystan/inst/ShinyStan/ui_files/rhat_neff_mcse_layout.R0000644000176200001440000000447013035516434023462 0ustar liggesuserssidebarLayout( position = "right", sidebarPanel( width = 3, class = "optionswell", strongBig("Definitions"), hr(class = "hroptions"), div(actionLink("open_quick_mcse", "mcse (se_mean)")), div(actionLink("open_quick_neff", "n_eff (ESS)")), div(actionLink("open_quick_rhat", "Rhat")), br(), strongBig("Warnings"), hr(class = "hroptions"), withMathJax(), sliderInput( "n_eff_threshold", "\\(n_{eff} / N\\) warning threshold", ticks = FALSE, value = 10, min = 0, max = 100, step = 5, post = "%" ), sliderInput( "mcse_threshold", "\\(\\text{se}_{mean} / sd\\) warning threshold", ticks = FALSE, value = 10, min = 0, max = 100, step = 5, post = "%" ), sliderInput( "rhat_threshold", "\\(\\hat{R}\\) warning threshold", ticks = FALSE, value = 1.1, min = 1, max = 1.2, step = 0.01 ) ), mainPanel( width = 9, withMathJax(), br(), splitLayout( h4("\\(n_{eff} / N\\)", align = "center"), h4("\\(mcse / sd\\)", align = "center"), h4("\\(\\hat{R}\\)", align = "center") ), splitLayout( plotOutput("n_eff_plot_out", height = "200px"), plotOutput("mcse_over_sd_plot_out", height = "200px"), plotOutput("rhat_plot_out", height = "200px") ), hr(), div( fluidRow( column(width = 4, strong(textOutput( "n_eff_warnings_title" ))), column(width = 4, strong( textOutput("mcse_over_sd_warnings_title") )), column(width = 4, strong(textOutput( "rhat_warnings_title" ))) ), tags$style( type = "text/css", "#n_eff_warnings_title, #rhat_warnings_title, #mcse_over_sd_warnings_title {font-size: 13px;}" ), br(), fluidRow( column(width = 4, div(style = "color: #006dcc;", textOutput("n_eff_warnings"))), column(width = 4, div( style = "color: #006dcc;", textOutput("mcse_over_sd_warnings") )), column(width = 4, div(style = "color: #006dcc;", textOutput("rhat_warnings"))) ), tags$style( type = "text/css", "#n_eff_warnings, #rhat_warnings, #mcse_over_sd_warnings {font-size: 12px;}" ) ) ) )shinystan/inst/ShinyStan/ui_files/table_latex_sidebar.R0000644000176200001440000000225413035516434023075 0ustar liggesuserssidebarPanel( width = 4, h4(strong( withMathJax("\\(\\LaTeX\\) table generator") )), selectInput( "tex_params", width = "100%", label = "Parameters (blank = all)", multiple = TRUE, choices = .param_list_with_groups, selected = if (length(.param_names) == 1) .param_names else .param_names[1:2] ), numericInput( "tex_digits", label = "Digits", value = 1, min = 0 ), div( style = "padding: 1px;", checkboxGroupInput( "tex_columns", label = "Columns", choices = c( "Rhat", "Effective sample size" = "n_eff", "Posterior mean" = "mean", "Posterior standard deviation" = "sd", "Monte Carlo error" = "se_mean", "Quantile: 2.5%" = "2.5%", "Quantile: 25%" = "25%", "Quantile: 50%" = "50%", "Quantile: 75%" = "75%", "Quantile: 97.5%" = "97.5%" ), selected = c("Rhat", "n_eff", "mean", "sd", "2.5%", "50%", "97.5%") ) ), textInput("tex_caption", label = "Caption"), checkboxGroupInput( "tex_pkgs", "Packages", choices = c("Booktabs", "Longtable"), selected = "Booktabs", inline = TRUE ), br() ) shinystan/inst/ShinyStan/ui_files/diagnostics_sample.R0000644000176200001440000000127613035516434022773 0ustar liggesusers# sample (accept_stat, lp) div(class = "diagnostics-navlist-tabpanel", fluidRow( column( width = 7, fluidRow( column( width = 6, help_dynamic, dygraphOutput_175px("dynamic_trace_diagnostic_lp_out"), br(), dygraphOutput_175px("dynamic_trace_diagnostic_accept_stat_out") ), column( width = 6, help_lines, plotOutput_200px("lp_hist_out"), br(), plotOutput_200px("accept_stat_hist_out") ) )), column( width = 5, help_points, plotOutput_400px("accept_stat_vs_lp_out") ) ))shinystan/inst/ShinyStan/ui_files/trivariate_select.R0000644000176200001440000000103613035516434022626 0ustar liggesusersfluidRow( column( width = 3, uiOutput("ui_trivariate_select_x") ), column( width = 3, selectizeInput( "trivariate_param_y", label = strong_bl("y-axis"), choices = .param_list, selected = if (length(unlist(.param_list)) > 1) unlist(.param_list)[2] else unlist(.param_list)[1], multiple = FALSE ) ), column( width = 3, selectizeInput( "trivariate_param_z", label = strong_bl("z-axis"), choices = rev(.param_list), multiple = FALSE ) ) )shinystan/inst/ShinyStan/ui_files/multiparam_customize.R0000644000176200001440000000576713044376075023416 0ustar liggesusersabsolutePanel( id = "controls_multiparam", class = "draggable_controls", fixed = TRUE, top = 190, right = 20, width = 200, draggable = TRUE, shinyjs::hidden(div( id = "multiparam_options", wellPanel( class = "optionswell", strongBig("Parameter estimates"), hr(class = "hroptions"), selectInput( "multiparam_options_display", label = strongBig("Control"), choices = c("Options", "Aesthetics", "Sorting"), selected = "Options", width = "100%" ), conditionalPanel( condition = "input.multiparam_options_display == 'Options'", checkboxInput( "param_plot_show_density", label = "Kernel density estimates", value = FALSE ), checkboxInput( "param_plot_show_ci_line", label = "95% interval line", value = TRUE ), radioButtons( "param_plot_point_est", label = "Point estimate", choices = c("Median", "Mean"), selected = "Median", inline = TRUE ), hr(class = "hroptions"), downloadButton("download_multiparam_plot", "ggplot2", class = "plot-download"), downloadButton('save_pdf_multiparam', "pdf", class = "plot-download pdf-download") ), conditionalPanel( condition = "input.multiparam_options_display == 'Aesthetics'", withMathJax(), checkboxInput( "param_plot_color_by_rhat", label = "Color point est. by \\(\\hat{R}\\)", value = FALSE ), colourpicker::colourInput( "param_plot_fill_color", span(style = "font-size: 12px", "Density/CI color"), "#590815" ), colourpicker::colourInput( "param_plot_outline_color", span(style = "font-size: 12px", "Outline color"), "#487575" ), conditionalPanel( condition = "input.param_plot_color_by_rhat == false", colourpicker::colourInput( "param_plot_est_color", span(style = "font-size: 12px", "Point estimate color"), base_fill ) ), conditionalPanel( condition = "input.param_plot_color_by_rhat == true", selectInput( "param_plot_rhat_palette", span(style = "font-size: 12px", "Rhat palette"), choices = c("Blues", "Grays", "Greens", "Oranges", "Purples", "Reds"), selected = "Blues", selectize = TRUE ) ) ), conditionalPanel( condition = "input.multiparam_options_display == 'Sorting'", radioButtons( "param_plot_sort_j", label = "Sort parameters in select list by", choices = c(Row = TRUE, Column = FALSE), selected = TRUE, inline = TRUE ), helpText( style = "font-size: 12px;", "If applicable, sort with x[1,2] before x[2,1] or vice-versa" ) ) ) )) ) shinystan/inst/ShinyStan/ui_files/PAGE_more_menu.R0000644000176200001440000000134613035516434021703 0ustar liggesusersnavbarMenu( title = "More", #### model code #### tabPanel( title = "Model Code", source_ui("model_code.R") ), #### notepad #### tabPanel( title = "Notepad", source_ui("notepad.R") ), #### about #### tabPanel( title = "About", logo_and_name(), div( style = "margin-top: 75px;", source_ui("about.R") ) ), #### glossary #### tabPanel( title = "Glossary", div( style = "background-color: white;", h1(style = "text-align: center;", "Glossary"), source_ui("glossary.R"), hr(), stan_manual() ) ), #### help #### tabPanel( title = "Help", h1(style = "text-align: center;", "Help"), source_ui("help.R") ) )shinystan/inst/ShinyStan/ui_files/sampler_stats_customize.R0000644000176200001440000000115413035516434024101 0ustar liggesusersfluidRow( column( width = 3, radioButtons( "sampler_warmup", label = h5("Warmup"), choices = list(Omit = "omit", Include = "include"), inline = TRUE ) ), column( width = 4, radioButtons( "sampler_report", label = h5("Statistic"), choices = list( Mean = "average", SD = "sd", Max = "maximum", Min = "minimum" ), inline = TRUE ) ), column( width = 2, numericInput( "sampler_digits", label = h5("Decimals"), value = 4, min = 0, max = 10, step = 1 ) ) ) shinystan/inst/ShinyStan/ui_files/diagnostics_stepsize.R0000644000176200001440000000042313035516434023351 0ustar liggesusers# stepsize fluidRow( column( width = 7, help_dynamic, dygraphOutput_175px("dynamic_trace_diagnostic_stepsize_out"), br(),br(), plotOutput("stepsize_vs_lp_out", height = "150px") ), column(width = 5, plotOutput_400px("stepsize_vs_accept_stat_out")) )shinystan/inst/ShinyStan/ui_files/diagnostics_customize.R0000644000176200001440000000232113035516434023524 0ustar liggesusersdiv(id = "diagnostics_customize", wellPanel( fluidRow( column(width = 3, h4(textOutput("diagnostic_chain_text"))), column(width = 4, h5("Parameter")), column(width = 4, h5("Transformation")) ), fluidRow( column( width = 3, div(style = "width: 100px;", numericInput( "diagnostic_chain", label = NULL, value = 0, min = 0, # don't allow changing chains if only 1 chain max = ifelse(.nChains == 1, 0, .nChains) ) )), column( width = 4, selectizeInput( inputId = "diagnostic_param", label = NULL, multiple = FALSE, choices = .param_list, selected = .param_list[1] ) ), column( width = 3, transformation_selectInput("diagnostic_param_transform") ), column( width = 2, actionButton("diagnostic_param_transform_go", "Transform", class = "transform-go") ) ), helpText(strong( style = "color: red; font-size: 13px;", textOutput("diagnostics_warnings_text") )) )) shinystan/inst/ShinyStan/ui_files/PAGE_explore.R0000644000176200001440000000615413035516434021375 0ustar liggesuserstagList( fluidRow( column( width = 3, selectizeInput( inputId = "param", label = h4("Select parameter"), choices = .param_list, selected = .param_list[1], multiple = FALSE ) ), column( width = 7, offset = 1, DT::dataTableOutput("parameter_summary_out") ) ), navlistPanel( well = FALSE, widths = c(3, 9), #### multiview #### tabPanel( title = "Multiview", icon = icon("th-large", lib = "glyphicon"), checkboxInput( "multiview_warmup", label = strong("Include warmup"), value = FALSE ), splitLayout(h5("Kernel Density Estimate"), h5("Autocorrelation")), splitLayout( plotOutput("multiview_density_out", height = "150"), plotOutput("multiview_autocorr_out", height = "150"), cellArgs = list(class = "plot_hover_shadow") ), h5("Trace"), dygraphs::dygraphOutput("multiview_trace_out", height = "200px"), source_ui("dynamic_trace_helptext.R") ), #### bivariate ##### tabPanel( title = "Bivariate", selectizeInput( "bivariate_param_y", label = strong(style = "color: #006DCC;", "y-axis"), choices = rev(.param_list), selected = rev(.param_list)[1], multiple = FALSE ), a_options("bivariate"), source_ui("bivariate_customize.R"), plotOutput("bivariate_plot_out", height = "350px"), helpText( style = "font-size: 11px", "For Stan models using the NUTS algorithm, red points indicate iterations that encountered a divergent transition.", "Yellow points indicate a transition that hit the maximum treedepth", "rather than terminated its evolution normally." ), hr(), downloadButton("download_bivariate", "ggplot2", class = "plot-download"), downloadButton('save_pdf_bivariate', "pdf", class = "plot-download pdf-download") ), #### trivariate ##### tabPanel( title = "Trivariate", source_ui("trivariate_select.R"), a_options("trivariate"), source_ui("trivariate_customize.R"), br(), threejs::scatterplotThreeOutput("trivariate_plot_out", height = "400px"), helpText( style = "font-size: 12px;", "Use your mouse and trackpad to rotate the plot and zoom in or out." ) ), #### density ##### tabPanel( title = "Density", a_options("density"), source_ui("density_customize.R"), plotOutput("density_plot_out", height = "250px"), hr(), downloadButton("download_density", "ggplot2", class = "plot-download"), downloadButton('save_pdf_density', "pdf", class = "plot-download pdf-download") ), #### histogram ##### tabPanel( title = "Histogram", a_options("hist"), source_ui("hist_customize.R"), plotOutput("hist_plot_out", height = "250px"), hr(), downloadButton("download_histogram", "ggplot2", class = "plot-download"), downloadButton('save_pdf_histogram', "pdf", class = "plot-download pdf-download") ) ) )shinystan/inst/ShinyStan/ui_files/diagnostics_by_parameter.R0000644000176200001440000000115013035516434024153 0ustar liggesusers# model parameter div(class = "diagnostics-navlist-tabpanel", fluidRow( column( width = 7, help_dynamic, dygraphOutput_175px("dynamic_trace_diagnostic_parameter_out") ), column(width = 5, help_lines, plotOutput_200px("p_hist_out")) ), help_points, fluidRow( column( width = 6, plotOutput_200px("param_vs_lp_out"), plotOutput_200px("param_vs_stepsize_out") ), column( width = 6, plotOutput_200px("param_vs_accept_stat_out"), plotOutput_200px("param_vs_treedepth_out") ) ), br() )shinystan/inst/ShinyStan/ui_files/diagnostics_ndivergent.R0000644000176200001440000000043613044122070023641 0ustar liggesusers# Divergences fluidRow( column( width = 7, help_dynamic, dygraphOutput_175px("dynamic_trace_diagnostic_ndivergent_out"), br(), br(), plotOutput("ndivergent_vs_lp_out", height = "150px") ), column(width = 5, plotOutput_400px("ndivergent_vs_accept_stat_out")) ) shinystan/inst/ShinyStan/ui_files/about.R0000644000176200001440000000261613035516434020234 0ustar liggesusersdiv( style = "text-align: center; margin-top: 100px;", shinystan_version(), br(),br(), a( style = "font-size: 16px;", strong("Stan Development Team"), href = "http://mc-stan.org/team/" ), br(), a( style = "font-size: 14px;", "mc-stan.org", href = "http://mc-stan.org/" ), div( actionLink( inputId = "shinystan_citation_show", label = "Show Citation", style = "font-size: 12px;", class = "aoptions" ), div(shinyjs::hidden( wellPanel( id = "citation_div", style = "text-align: left;", includeHTML("html/citation.html") ) )) ), br(),br(), h6("Author"), helpText(style = "font-size: 12px;", "Jonah Gabry"), br(), h6(style = "font-size: 12px;", "Contributors"), helpText(style = "font-size: 12px;", includeHTML("html/contribs.html")), br(), h6("Logo"), helpText( style = "font-size: 12px;", a( href = "http://mc-stan.org/team/", "Michael Betancourt" ) ), br(), h6("Shiny"), helpText( style = "font-size: 12px;", "ShinyStan is powered by the", a( href = "http://shiny.rstudio.com", "Shiny web application framework" ), "(RStudio)" ), br(), h6("Source code"), a( style = "color: #190201;", href = "http://github.com/stan-dev/shinystan", target = "_blank", tags$i(class = "fa fa-github fa-3x") ) ) shinystan/inst/ShinyStan/ui_files/table_customize.R0000644000176200001440000000121313035516434022303 0ustar liggesusersfluidRow( # column( # width = 4, # helpText(style = "margin-bottom: 2px;", "Table tips: Drag column names to rearrange the table columns.") # # helpText(style = "margin-top: 2px; font-size: 11px;", "Drag column names to rearrange the table columns.") # ), style = "margin-left: 4px; margin-bottom: 6px;", column( width = 2, div( strong(id = "table_digits_txt", "Digits"), numericInput( "table_digits", label = NULL, value = 1, min = 0, max = 7, step = 1 ) ) ), column( width = 2, offset = 8, a_glossary("open_glossary_from_table") ) )shinystan/inst/ShinyStan/ui_files/trivariate_customize.R0000644000176200001440000000312213035516434023367 0ustar liggesusersshinyjs::hidden(div( id = "trivariate_options", wellPanel( class = "optionswell", hr(class = "hroptions"), strongBig("Transformation"), transform_helpText("x,y,z"), fluidRow( column( width = 3, transformation_selectInput("trivariate_transform_x") ), column( width = 3, transformation_selectInput("trivariate_transform_y") ), column( width = 3, transformation_selectInput("trivariate_transform_z") ), column( width = 2, actionButton( "trivariate_transform_go", label = "Transform", class = "transform-go" ) ) ), hr(class = "hroptions"), fluidRow( column( width = 3, colourpicker::colourInput("trivariate_pt_color", strongMed("Color"), value = base_fill) ), column( width = 3, sliderInput( "trivariate_pt_size", strongMed("Size"), value = 0.5, min = 0, max = 2, step = 0.1, ticks = FALSE ) ), column( width = 2, radioButtons( "trivariate_grid", strongMed("Grid"), choices = list(Show = "show", Hide = "hide"), selected = "show", inline = FALSE ) ), column( width = 2, radioButtons( "trivariate_flip", strongMed("y-axis"), choices = list(Normal = "normal", Flipped = "flip"), selected = "normal", inline = FALSE ) ) ) ) )) shinystan/inst/ShinyStan/ui_files/model_code.R0000644000176200001440000000146213035516434021212 0ustar liggesuserssidebarLayout( sidebarPanel( width = 3, style = "height: 550px;", br(), h4("Model Code"), helpText( style = "font-size: 12px;", p( "Model code will be displayed here each", "time you launch ShinyStan with this shinystan object." ) ), br(), actionButton( "save_user_model_code", label = "Save code", icon = icon("save") ), div(style = "font-size: 11px;", textOutput("user_code_saved")), conditionalPanel( condition = "input.save_user_model_code > 0", br(), save_and_close_reminder("save_user_model_code_safe_quit") ) ), mainPanel( width = 9, br(), br(), tags$textarea( id = "user_model_code", wrap = "off", cols = 80, rows = 20, .model_code ) ) ) shinystan/inst/ShinyStan/ui_files/diagnostics_energy.R0000644000176200001440000000051513044376075023003 0ustar liggesusers# Energy # fluidRow( # column( # width = 7, # help_dynamic, # dygraphOutput_175px("dynamic_trace_diagnostic_ndivergent_out"), # br(), br(), # plotOutput("ndivergent_vs_lp_out", height = "150px") # ), # column(width = 5, plotOutput_400px("ndivergent_vs_accept_stat_out")) # ) plotOutput("energy_hist_out") shinystan/inst/ShinyStan/ui_files/PAGE_diagnose.R0000644000176200001440000000415113044376075021510 0ustar liggesuserstabsetPanel( id = "diagnose_tabset", #### hmc/nuts plots #### tabPanel( title = "NUTS (plots)", source_ui("diagnostics_customize.R"), navlistPanel( id = "diagnostics_navlist", tabPanel( "By model parameter", source_ui("diagnostics_by_parameter.R") ), tabPanel( "Sample information", source_ui("diagnostics_sample.R") ), tabPanel( "Divergence information", source_ui("diagnostics_ndivergent.R") ), tabPanel( "Energy information", source_ui("diagnostics_energy.R") ), tabPanel( "Treedepth information", source_ui("diagnostics_treedepth.R") ), tabPanel( "Step size information", source_ui("diagnostics_stepsize.R") ), tabPanel( "Help", source_ui("diagnostics_help.R") ), well = FALSE, widths = c(2, 10) ) ), #### hmc/nuts stats #### tabPanel( title = "HMC/NUTS (stats)", h2("Summary of sampler parameters"), a_glossary("open_glossary_from_nuts_table"), br(), source_ui("sampler_stats_customize.R"), DT::dataTableOutput("sampler_summary"), br() ), #### rhat, n_eff, mcse #### tabPanel( title = "\\(\\hat{R}, n_{eff}, \\text{se}_{mean}\\)", source_ui("rhat_neff_mcse_layout.R") ), #### autocorrelation #### tabPanel( title = "Autocorrelation", source_ui("autocorr_customize.R"), wellPanel(fluidRow( column( width = 8, selectizeInput( "ac_params", width = "100%", label = h5("Select or enter parameter names"), choices = .param_list_with_groups, multiple = TRUE ) ), column( width = 3, offset = 1, a_options("autocorr") ) )), plotOutput("autocorr_plot_out") ), #### ppcheck #### tabPanel( title = "PPcheck", h2("Graphical posterior predictive checks"), h6("Experimental feature"), source_ui(if (.has_rstanarm_ppcs) "pp_navlist_rstanarm.R" else "pp_navlist.R"), br() ) )shinystan/inst/ShinyStan/ui_files/density_customize.R0000644000176200001440000001474513035516434022711 0ustar liggesusersshinyjs::hidden(div( id = "density_options", wellPanel( class = "optionswell", hr(class = "hroptions"), strongBig("Transformation"), transform_helpText("x"), fluidRow( column(width = 4, transformation_selectInput("dens_transform_x")), column( width = 2, actionButton("dens_transform_x_go", label = "Transform", class = "transform-go") ) ), hr(class = "hroptions"), selectInput( "dens_options_display", label = strongBig("Control"), choices = c("Options", "Aesthetics", "Compare to function" = "Compare"), selected = "Options", width = "50%" ), conditionalPanel( condition = "input.dens_options_display == 'Options'", fluidRow( column( width = 3, numericInput( "dens_chain", label = strongMed("Chain"), min = 0, max = .nChains, step = 1, value = 0 ) ), column( width = 3, conditionalPanel( condition = "input.dens_chain == 0", radioButtons( "dens_chain_split", label = strongMed("All chains"), choices = c("Together", "Separate"), selected = "Together", inline = FALSE ) ) ), column( width = 3, selectInput( "dens_point_est", strongMed("Point est"), choices = c("None", "Mean", "Median", "MAP"), selected = "None" ) ), column( width = 3, selectInput( "dens_ci", strongMed("CI %"), choices = c( "None" = "None", "50%" = 0.5, "80%" = 0.8, "95%" = 0.95 ), selected = "None" ) ) )), conditionalPanel( condition = "input.dens_options_display == 'Aesthetics'", fluidRow( column( width = 3, selectInput( "dens_x_breaks", strongMed("x breaks"), choices = c("None", "Some", "Many"), selected = "Some" ) ), column( width = 3, colourpicker::colourInput("dens_fill_color", strongMed("Fill"), base_fill) ), column( width = 3, colourpicker::colourInput("dens_line_color", strongMed("Line"), vline_base_clr) ) )), conditionalPanel( condition = "input.dens_options_display == 'Compare'", fluidRow( column( width = 4, selectInput( "dens_prior", strongMed("Family"), choices = list( "None", "Normal", "t", "Cauchy", "Exponential", "Gamma", "Inverse Gamma", "Beta" ) )), column( width = 2, condPanel_dens_prior( "Normal", numericInput( "dens_prior_normal_mu", "Location", value = 0, step = 0.1 ) ), condPanel_dens_prior( "t", numericInput( "dens_prior_t_df", "df", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Cauchy", numericInput( "dens_prior_cauchy_mu", "Location", value = 0, step = 0.1 ) ), condPanel_dens_prior( "Beta", numericInput( "dens_prior_beta_shape1", "Shape1", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Exponential", numericInput( "dens_prior_expo_rate", "Rate", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Gamma", numericInput( "dens_prior_gamma_shape", "Shape", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Inverse Gamma", numericInput( "dens_prior_inversegamma_shape", "Shape", value = 1, min = 0, step = 0.1 ) ) ), column( width = 2, condPanel_dens_prior( "Normal", numericInput( "dens_prior_normal_sigma", "Scale", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "t", numericInput( "dens_prior_t_mu", "Location", value = 0, step = 0.1 ) ), condPanel_dens_prior( "Cauchy", numericInput( "dens_prior_cauchy_sigma", "Scale", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Beta", numericInput( "dens_prior_beta_shape2", "Shape2", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Gamma", numericInput( "dens_prior_gamma_rate", "Rate", value = 1, min = 0, step = 0.1 ) ), condPanel_dens_prior( "Inverse Gamma", numericInput( "dens_prior_inversegamma_scale", "Scale", value = 1, min = 0, step = 0.1 ) ) ), column(width = 2, condPanel_dens_prior( "t", numericInput( "dens_prior_t_sigma", "Scale", value = 1, min = 0, step = 0.1 ) )) ), condPanel_dens_together( textInput( "dens_xzoom", label = strongMed("x-axis limits"), value = "c(min, max)" ) ), br() ) ) )) shinystan/inst/ShinyStan/ui_files/glossary.R0000644000176200001440000000223113045141323020746 0ustar liggesusersdiv( class = "help-glossary-div", withMathJax(), br(),br(), div( withMathJax(), class = "help-glossary-nav-container", navlistPanel( well = TRUE, id = "glossary_navlist", tabPanel( "Effective sample size", includeHTML("html/neff.html") ), tabPanel( "Monte Carlo uncertainty", includeHTML("html/mcse.html") ), tabPanel( "Rhat", includeHTML("html/rhat.html") ), tabPanel( "No-U-Turn Sampler (NUTS)", includeHTML("html/nuts.html") ), tabPanel( "accept_stat", includeHTML("html/accept_stat.html") ), tabPanel( "divergent", withMathJax(), includeHTML("html/ndivergent.html") ), tabPanel( "energy", withMathJax(), includeHTML("html/energy.html") ), tabPanel( "stepsize", includeHTML("html/stepsize.html") ), tabPanel( "n_leapfrog", includeHTML("html/nleapfrog.html") ), tabPanel( "treedepth", includeHTML("html/treedepth.html") ) ) ), br(),br() ) shinystan/inst/ShinyStan/ui_files/help.R0000644000176200001440000000623413137714217020054 0ustar liggesusersdiv( class = "help-glossary-div", br(), br(), div( class = "help-glossary-nav-container", navlistPanel( well = TRUE, id = "help_navlist", "Topics", tabPanel( "Questions, bugs, and new features", div( class = "glossary-entry", h4("Stan users group"), p( "To ask a question or suggest a new feature visit the", a( "Stan users message board.", href = "http://discourse.mc-stan.org" ) ), br(), h4("GitHub issue tracker"), p( "To report a bug or suggest a new feature visit the", a( "GitHub issue tracker.", href = "https://github.com/stan-dev/shinystan/issues" ) ) ) ), tabPanel( "Saving plots", div( class = "glossary-entry", h4("Saving plots as ggplot2 objects"), p( "Clicking on a 'Save ggplot2 object' button will be save an .RData file that you can load into your Global Environment using the", code("load"), "function in R. You can then make changes to the plot using the functions in the ggplot2 package." ), p( "Any plot that can be saved as a ggplot2 object can also be saved as a PDF." ) )), tabPanel( "Large models and launch speed", div( class = "glossary-entry", h4("Launching ShinyStan faster"), p( "The", code("drop_parameters"), "function in the", strong("shinystan"), "R package will allow you to reduce the size", "of a shinystan object by removing parameters.", "See", code("help('drop_parameters', 'shinystan')"), "for the documentation." ), p( "Additionally, for large models, the", code("launch_shinystan"), "function will launch the app faster when used with a", "shinystan object rather than a stanfit object", "(because no conversion is required).", "If ShinyStan takes a long time to launch for your", "model then it can help to first create a", "shinystan object using the", code("as.shinystan"), "function.", "Alternatively, the first time you launch", "ShinyStan using a stanfit object, a shinystan", "object will be returned if you assign the value of", code("launch_shinystan"), "to a name, e.g." ), p(code("sso <- launch_shinystan(stanfit)")), p("rather than just"), p(code("launch_shinystan(stanfit)")), p( "The next time you launch ShinyStan for the same", "model you can launch it using", code("sso"), "rather than", code("stanfit"), "and it should be quicker to launch.", "If it is still too slow then dropping some large parameters", "from the shinystan object is the best solution." ) ) ) ) ), br(), br() ) shinystan/inst/ShinyStan/ui_files/autocorr_customize.R0000644000176200001440000000202113035516434023050 0ustar liggesusersabsolutePanel( id = "controls_autocorr", class = "draggable_controls", fixed = TRUE, top = 185, right = 20, width = 200, draggable = TRUE, shinyjs::hidden(div( id = "autocorr_options", wellPanel( class = "optionswell", strongBig("Autocorrelation"), hr(class = "hroptions"), br(), sliderInput( "ac_lags", label = NULL, post = " lags", min = 0, max = .nIter - .nWarmup - 5, step = 5, value = min(25, round((.nIter - .nWarmup) / 2)) ), checkboxInput("ac_partial", label = "Partial autocorrelation", value = FALSE), checkboxInput("ac_warmup", label = "Include warmup", FALSE), checkboxInput("ac_combine", label = "Combine chains", FALSE), checkboxInput("ac_flip", label = "Flip facets", FALSE), hr(class = "hroptions"), downloadButton("download_autocorr", "ggplot2", class = "plot-download"), downloadButton('save_pdf_autocorr', "pdf", class = "plot-download pdf-download") ) )) ) shinystan/inst/ShinyStan/ui_files/PAGE_home.R0000644000176200001440000000034013035516434020636 0ustar liggesuserstagList( logo_and_name(), div(class = "home-links", div(id = "model-name", br(), h2("Model:"), h4(.model_name))), br(), br(), br(), br(), includeHTML("html/home_page_links.html") )shinystan/inst/ShinyStan/ui_files/PAGE_estimate.R0000644000176200001440000000324713035516434021532 0ustar liggesuserstabsetPanel( id = "estimate_tabset", #### multiparameter plot #### tabPanel( title = "Parameters plot", wellPanel( fluidRow( column(width = 6, uiOutput("ui_multiparam_selectize")), column( width = 3, offset = 1, sliderInput( "param_plot_ci_level", h5("Posterior interval"), width = "75%", ticks = FALSE, min = 50, max = 95, value = 50, step = 5, post = "%" ) ), column(width = 2, a_options("multiparam")) ), fluidRow(column( width = 3, offset = 1, span(id = "params_to_plot_regex_label", "Add parameters by regex search") )), fluidRow( column( width = 1, actionButton("param_plot_regex", label = "Search", class = "regex-go") ), column( width = 3, textInput("params_to_plot_regex", label = NULL, value = "") ), column(width = 5, textOutput("invalid_regex")) ) ), source_ui("multiparam_customize.R"), plotOutput("multiparam_plot_out", width = "90%"), br() ), #### posterior summary statistics #### tabPanel( "Posterior summary statistics", source_ui("table_customize.R"), div(DT::dataTableOutput("all_summary_out"), style = "overflow-x: auto") ), #### LaTex tables #### tabPanel( "Generate LaTeX table", br(), sidebarLayout( mainPanel = source_ui("table_latex_main.R"), sidebarPanel = source_ui("table_latex_sidebar.R") ) ) )shinystan/inst/ShinyStan/ui_files/diagnostics_treedepth.R0000644000176200001440000000110413035516434023464 0ustar liggesusers# treedepth div( class = "diagnostics-navlist-tabpanel", fluidRow( column( width = 7, help_dynamic, dygraphOutput_175px("dynamic_trace_diagnostic_treedepth_out"), br(), br(), plotOutput("treedepth_vs_lp_out", height = "150px") ), column(width = 5, plotOutput_400px("treedepth_vs_accept_stat_out")) ), splitLayout( plotOutput("treedepth_ndivergent_hist_out", height = "125px"), plotOutput("treedepth_ndivergent0_hist_out", height = "125px"), plotOutput("treedepth_ndivergent1_hist_out", height = "125px") ), br() ) shinystan/inst/ShinyStan/ui_files/pp_navlist.R0000644000176200001440000000645413035516434021305 0ustar liggesusersnavlistPanel( id = "pp_navlist", widths = c(3, 9), well = TRUE, tabPanel( "Select data", div( br(), uiOutput("ui_pp_get_y"), br(), uiOutput("ui_pp_get_yrep") ) ), "Plots", tabPanel( "Distribution of observed data vs replications", div( br(), h4(withMathJax(plot_descriptions["plot_hists_rep_vs_obs"])), br(), actionButton( "resample_hist_go", label = "Show different replications", icon = icon("refresh") ), fluidRow( column( width = 5, radioButtons( "pp_hists_rep_vs_obs_type", label = "", choices = list(Histograms = "histogram", Densities = "density"), inline = TRUE ) ), column( width = 4, conditionalPanel( condition = "input.pp_hists_rep_vs_obs_type == 'density'", radioButtons( "pp_hists_rep_vs_obs_overlay", label = "", choices = list(Separate = FALSE, Overlay = TRUE), selected = FALSE, inline = TRUE ) ) ) ), plotOutput("pp_hists_rep_vs_obs_out", width = "90%"), br() ) ), tabPanel( "Distributions of test statistics", div( br(), h4(withMathJax(plot_descriptions["plot_test_statistics"])), helpText( "The blue lines show \\(T(y)\\), the value of the statistic computed from the observed data." ), radioButtons( "pp_hists_test_statistics_type", label = "", choices = list(Histograms = "histogram", Densities = "density"), inline = TRUE ), fluidRow( column( width = 6, plotOutput("pp_hists_test_statistics_mean_out", height = "200px") ), column( width = 6, plotOutput("pp_hists_test_statistics_sd_out", height = "200px") ) ), br(), fluidRow( column( width = 6, plotOutput("pp_hists_test_statistics_min_out", height = "200px") ), column( width = 6, plotOutput("pp_hists_test_statistics_max_out", height = "200px") ) ), br() ) ), tabPanel( "Scatterplots", div( br(), h4(withMathJax(plot_descriptions["plot_obs_vs_avg_y_rep"])), checkboxInput("pp_zoom_to_zero", "Zoom to include (0,0)", value = FALSE), plotOutput("pp_y_vs_avg_rep_out", height = "250px", width = "80%"), # h5(withMathJax(plot_descriptions["plot_avg_rep_vs_avg_resid_rep"])), # plotOutput("pp_avg_rep_vs_avg_resid_rep_out", height = "250px", width = "80%"), br() ) ), tabPanel( "Histograms of residuals", div( br(), h4(withMathJax(plot_descriptions["plot_hist_resids"])), br(), actionButton( "resample_resids_go", label = "Show a different replication", icon = icon("refresh") ), br(),br(), plotOutput("pp_hist_resids_out", height = "250px", width = "75%") ) ), "About", tabPanel( "About graphical posterior predictive checking", source(file.path("ui_files", "pp_about.R"), local = TRUE)$value ), tabPanel( "Tutorial", includeMarkdown("markdown/pp_check_tutorial.md") ) ) shinystan/inst/ShinyStan/ui_files/pp_about.R0000644000176200001440000000233013271442134020721 0ustar liggesusersdiv( withMathJax(), h3(style = "color: #337ab7;", "What is posterior predictive checking?"), p( strong("The idea behind posterior predictive checking is simple:") ), p(em("If our model is a good fit then we should be able to use it to generate")), p(em("data that looks a lot like the data we observed.")), br(), p( "To generate this 'replicated' data we use the", em("posterior predictive distribution") ), span( style = "color: #337ab7; font-face: bold;", withMathJax( "$$ p(y^{rep} | y ) = \\int p(y^{rep} | \\theta) p(\\theta | y ) d \\theta,$$" ) ), p( "where \\(y\\) is the observed data and \\(\\theta\\) the parameters in our model." ), br(), p( "For each draw of \\(\\theta\\) from the posterior \\(p(\\theta | y) \\) we simulate data \\(y^{rep}\\) from the posterior predictive distribution \\(p(y^{rep} | y) \\)." ), br(), p( "Using the simulations of \\(y^{rep}\\) we can make various graphical displays comparing our observed data to the replications." ), hr(), helpText( "For a more thorough discussion of posterior predictive checking see Chapter 6 of", a("BDA3.", href = "http://www.stat.columbia.edu/~gelman/book/") ) ) shinystan/inst/ShinyStan/ui_files/dynamic_trace_helptext.R0000644000176200001440000000065513137733311023640 0ustar liggesusersdiv( br(), helpText( style = "font-size: 11px;", "Use your mouse to highlight areas in the traceplot to zoom into. Double-click to reset.", "The number in the small black box in the bottom left corner controls the", em("roll period."), "If you specify a roll period of N the resulting graph will be a moving average,", "with each plotted point representing the average of N points in the data." ) ) shinystan/inst/ShinyStan/ui_files/table_latex_main.R0000644000176200001440000000031713035516434022406 0ustar liggesusersmainPanel( width = 8, actionButton( "tex_go", withMathJax("Update \\(\\LaTeX\\)"), icon = icon("print", lib = "glyphicon") ), br(), br(), verbatimTextOutput("summary_stats_latex_out") )shinystan/inst/ShinyStan/ui_files/bivariate_customize.R0000644000176200001440000001103213035516434023162 0ustar liggesusersshinyjs::hidden( div(id = "bivariate_options", wellPanel( class = "optionswell", hr(class = "hroptions"), strongBig("Transformation"), transform_helpText("x,y"), fluidRow( column(width = 3, transformation_selectInput("bivariate_transform_x")), column(width = 3, transformation_selectInput("bivariate_transform_y")), column( width = 2, actionButton("bivariate_transform_go", label = "Transform", class = "transform-go") ) ), hr(class = "hroptions"), selectInput( "bivariate_options_display", label = strongBig("Control"), choices = c("Points", "Ellipse", "Lines"), selected = "Points", width = "50%" ), conditionalPanel( condition = "input.bivariate_options_display == 'Points'", fluidRow( column( width = 3, colourpicker::colourInput("bivariate_pt_color", strongMed("Color"), base_fill) ), column( width = 2, numericInput( "bivariate_pt_size", strongMed("Size"), value = 3.5, min = 0, max = 10, step = 0.5 ) ), column( width = 2, numericInput( "bivariate_pt_shape", strongMed("Shape"), value = 10, min = 1, max = 10, step = 1 ) ), column( width = 2, sliderInput( "bivariate_pt_alpha", strongMed("Opacity"), value = alpha_calc_pt(.nIter), min = 0, max = 1, step = 0.01, ticks = FALSE ) ) )), conditionalPanel( condition = "input.bivariate_options_display == 'Ellipse'", fluidRow( column( width = 2, selectizeInput( inputId = "bivariate_ellipse_lev", label = strongMed("Type"), selected = "None", choices = list( "None" = "None", "50%" = 0.5, "80%" = 0.8, "95%" = 0.95, "99%" = 0.99 ) ) ), column( width = 3, colourpicker::colourInput( "bivariate_ellipse_color", strongMed("Color"), vline_base_clr ) ), column( width = 2, numericInput( "bivariate_ellipse_lwd", strongMed("Size"), value = 1, min = 0, max = 5, step = 0.5 ) ), column( width = 2, numericInput( "bivariate_ellipse_lty", strongMed("Shape"), value = 1, min = 1, max = 6, step = 1 ) ), column( width = 2, sliderInput( "bivariate_ellipse_alpha", strongMed("Opacity"), value = 1, min = 0, max = 1, step = 0.01, ticks = FALSE ) ) ) ), conditionalPanel( condition = "input.bivariate_options_display == 'Lines'", fluidRow( column( width = 2, selectizeInput( inputId = "bivariate_lines", label = strongMed("Position"), choices = c(Hide = "hide", Back = "back", Front = "front"), selected = "back" ) ), column( width = 3, colourpicker::colourInput("bivariate_lines_color", strongMed("Color"), "gray") ), column( width = 2, sliderInput( "bivariate_lines_alpha", label = strongMed("Opacity"), value = alpha_calc_lines(.nIter), min = 0, max = 1, step = 0.01, ticks = FALSE ) ) ) ) ) ) ) shinystan/inst/ShinyStan/ui_files/diagnostics_help.R0000644000176200001440000000163213044376075022443 0ustar liggesusersnavlistPanel( well = FALSE, widths = c(2, 10), id = "diagnostics_help_navlist", tabPanel( "accept_stat", withMathJax(), includeHTML("html/accept_stat.html"), hr(), stan_manual() ), tabPanel( "divergent", withMathJax(), includeHTML("html/ndivergent.html"), hr(), stan_manual() ), tabPanel( "energy", withMathJax(), includeHTML("html/energy.html"), hr(), stan_manual() ), tabPanel( "stepsize", withMathJax(), includeHTML("html/stepsize.html"), hr(), stan_manual() ), tabPanel( "n_leapfrog", withMathJax(), includeHTML("html/nleapfrog.html"), hr(), stan_manual() ), tabPanel( "treedepth", withMathJax(), includeHTML("html/treedepth.html"), hr(), stan_manual() ), tabPanel( "NUTS", withMathJax(), includeHTML("html/nuts.html"), hr(), stan_manual() ) ) shinystan/inst/ShinyStan/ui_files/notepad.R0000644000176200001440000000162213035516434020550 0ustar liggesusersdiv(id = "notepad_div", sidebarLayout( sidebarPanel( width = 3, style = "height: 550px;", br(), h4("Notes"), helpText( style = "font-size: 12px;", p("Notes are displayed here each time you launch ShinyStan with this shinystan object.") ), br(), actionButton( "save_user_model_info", label = "Save notes", icon = icon("save") ), div(style = "font-size: 11px;", textOutput("user_text_saved")), conditionalPanel( condition = "input.save_user_model_info > 0", br(), save_and_close_reminder("save_user_model_info_safe_quit") ) ), mainPanel( width = 9, br(), br(), tags$textarea( id = "user_model_info", rows = 20, cols = 80, .notes ) ) )) shinystan/inst/ShinyStan/ui_files/pp_navlist_rstanarm.R0000644000176200001440000000431013035516434023201 0ustar liggesusersnavlistPanel( id = "pp_navlist", widths = c(3, 9), well = TRUE, tabPanel( "Distribution of observed data vs replications", div( br(), h4(withMathJax(plot_descriptions["plot_hists_rep_vs_obs"])), br(), # actionButton("resample_go", label = "Show different replications", icon = icon("refresh")), fluidRow( column( width = 5, radioButtons( "pp_rep_vs_obs_overlay_rstanarm", label = "", choices = list(Histograms = "histograms", "Overlaid Densities" = "density"), inline = TRUE ) ) ), plotOutput("pp_rep_vs_obs_out_rstanarm"), br() ) ), tabPanel( "Distributions of test statistics", div( br(), h4(withMathJax(plot_descriptions["plot_test_statistics"])), helpText( "The blue lines show \\(T(y)\\), the value of the statistic computed from the observed data." ), fluidRow( column( width = 6, plotOutput("pp_hists_test_statistics_mean_out_rstanarm", height = "200px") ), column( width = 6, plotOutput("pp_hists_test_statistics_sd_out_rstanarm", height = "200px") ) ), br(), fluidRow( column( width = 6, plotOutput("pp_hists_test_statistics_min_out_rstanarm", height = "200px") ), column( width = 6, plotOutput("pp_hists_test_statistics_max_out_rstanarm", height = "200px") ) ), br() ) ), tabPanel( "Scatterplots", div( br(), h4(withMathJax(plot_descriptions["plot_obs_vs_avg_y_rep"])), plotOutput("pp_y_vs_avg_rep_out_rstanarm"), br() ) ), tabPanel( "Histograms of residuals", div( br(), h4(withMathJax(plot_descriptions["plot_hist_resids"])), # br(), # actionButton("resample_resids_go", label = "Show a different replication", icon = icon("refresh")), # br(),br(), plotOutput("pp_hist_resids_out_rstanarm") ) ), "About", tabPanel( "About graphical posterior predictive checking", source(file.path("ui_files", "pp_about.R"), local = TRUE)$value ) ) shinystan/inst/ShinyStan/global.R0000644000176200001440000000004413035516434016554 0ustar liggesusers# Load shiny package library(shiny) shinystan/inst/ShinyStan/html/0000755000176200001440000000000013044376075016144 5ustar liggesusersshinystan/inst/ShinyStan/html/energy.html0000644000176200001440000000166413045141720020316 0ustar liggesusers

energy

Quick definition The energy is the value of the Hamiltonian (up to an additive constant) at each sample.

More details

While divergences can identify light tails and incomplete exploration of the target distribution, the energy diagnostic can identify overly heavy tails that are also challenging for sampling. Informally, the energy diagnostic for HMC quantifies the heaviness of the tails of the posterior distribution. The energy diagostic plot shows overlaid histograms of the (centered) marginal energy distribution and the first-differenced distribution. Keep an eye out for discrepancies between these distributions.

For more details see Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo.
shinystan/inst/ShinyStan/html/neff.html0000644000176200001440000000375013035516434017750 0ustar liggesusers

n_eff (ESS)

Quick definition \(n_{eff}\) is an estimate of the effective number of independent draws from the posterior distribution of the estimand of interest. Because the draws within a chain are not independent if there is autocorrelation, the effective sample size will be smaller than the total number of iterations.

More details

Samples in a Markov chain are only drawn with the marginal distribution \(p(\theta | y,x)\) after the chain has converged to its equilibrium distribution. There are several methods to test whether an MCMC method has failed to converge; unfortunately, passing the tests does not guarantee convergence. The recommended method for Stan is to run multiple Markov chains, initialized randomly with a diffuse set of initial parameter values, discard the warmup/adaptation samples, then split the remainder of each chain in half and compute the potential scale reduction statistic \(\hat{R}\).

If the effective sample size is too low to make inferences with the desired precision, double the number of iterations and start again, including rerunning warmup and everything. Often, a small effective sample size is the result of too few warmup iterations. At most, this rerunning strategy will consume about 50% more cycles than guessing the correct number of iterations at the outset.

The estimation of effective sample size is described in detail in the 'Markov Chain Monte Carlo Sampling' chapter of the Stan Modeling Language User's Guide and Reference Manual.

shinystan/inst/ShinyStan/html/ndivergent.html0000644000176200001440000000372713045142005021171 0ustar liggesusers

divergent

Quick definition 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. The average value of divergent over all iterations is therefore the proportion of iterations with diverging error.

More details

When numerical issues arise during the evaluation of the parameter Jacobians or the model log density, an exception is raised in the underlying code and the current expansion of the Hamiltonian forward and backward in time is halted. This is marked as a divergent transition.

The primary cause of divergent transitions in Euclidean HMC (other than bugs in the model code) is numerical instability in the leapfrog integrator used to simulate the Hamiltonian evaluation. The fundamental problem is that a fixed step size is being multiplied by the gradient at a particular point, to determine the next simulated point. If the stepsize is too large, this can overshoot into ill-defined portions of the posterior.

If there are (post-warmup) divergences then the results may be biased and should not be used.

In some cases, simply lowering the initial step size and increasing the target acceptance rate will keep the step size small enough that sampling can proceed.

The exact cause of each divergent transition is printed as a warning message in the output console. This can be useful in cases where managing the step size is insufficient. In such cases, a reparameterization is often required so that the posterior curvature is more manageable; see the section about Neal's Funnel in the Stan manual for an example.

For more details see Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo.
shinystan/inst/ShinyStan/html/rhat.html0000644000176200001440000000322113035516434017761 0ustar liggesusers

Rhat (\(\hat{R}\))

Quick definition 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 Gelman and Rubin potential scale reduction statistic \(\hat{R}\). The \(\hat{R}\) statistic measures the ratio of the average variance of samples within each chain to the variance of the pooled samples 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.

More details

Gelman and Rubin’s recommendation is that the independent Markov chains be initialized with diffuse starting values for the parameters and sampled until all values for \(\hat{R}\) are below 1.1. Stan allows users to specify initial values for parameters and it is also able to draw diffuse random initializations itself.

Details on the computatation of \(\hat{R}\) and some of its limitations can be found in the 'Markov Chain Monte Carlo Sampling' chapter of the Stan Modeling Language User's Guide and Reference Manual.

shinystan/inst/ShinyStan/html/treedepth.html0000644000176200001440000000270413035516434021014 0ustar liggesusers

treedepth

Quick definition The depth of tree used by NUTS.

More details

Configuring NUTS involves putting a cap on the depth of the trees that it evaluates during each iteration. This is controlled through a maximum depth parameter. n_leapfrog is then bounded by 2 to the power of the maximum depth minus 1.

Tree depth is an important diagnostic tool for NUTS. For example, a treedepth = 0 occurs when the first leapfrog step is immediately rejected and the initial state returned, indicating extreme curvature and poorly-chosen stepsize (at least relative to the current position).

On the other hand, treedepth = max_treedepth equal to the maximum depth indicates that NUTS is taking many leapfrog steps and being terminated prematurely to avoid excessively long execution time.

Taking very many steps may be a sign of poor adaptation, may be due to targeting a very high acceptance rate, or may simply indicate a difficult posterior from which to sample. In the latter case, reparameterization may help with efficiency. But in the rare cases where the model is correctly specified and a large number of steps is necessary, the maximum depth should be increased to ensure that that the NUTS tree can grow as large as necessary.

shinystan/inst/ShinyStan/html/accept_stat.html0000644000176200001440000000302013061050451021301 0ustar liggesusers

accept_stat

Quick definition The acceptance statistic used by NUTS for the Metropolis correction. In the original NUTS implementation a slice sampling step was used to sample a state from each Hamiltonian trajectory and accept_stat was the acceptance probability averaged over samples in the slice. In more recent versions of Stan the NUTS algorithm uses multinomial sampling over the states for each Hamiltonian trajectory. For HMC without NUTS accept_stat is the standard Metropolis acceptance probability.

More details

If the leapfrog integrator were perfect numerically, there would no need to do any more randomization per transition than generating a random momentum vector. Instead, what is done in practice to account for numerical errors during integration is to apply a Metropolis acceptance step. If the proposal is not accepted, the previous parameter value is returned for the next draw and used to initialize the next iteration.

By setting the target acceptance parameter to a value closer to 1 (its value must be strictly less than 1 and its default value is 0.8), adaptation will be forced to use smaller step sizes. This can improve sampling efficiency (effective samples per iteration) at the cost of increased iteration times. Raising the target will also allow some models that would otherwise get stuck to overcome their blockages.

shinystan/inst/ShinyStan/html/citation.html0000644000176200001440000000051213137714552020641 0ustar liggesusers
    @Misc{shinystan-software:2017,
      title = {{shinystan}: Interactive Visual and Numerical Diagnostics and Posterior Analysis for {Bayesian} Models},
      author = {Stan Development Team},
      note = {R package version 2.4.0},
      year = {2017},
      url = {https://mc-stan.org}
    })
shinystan/inst/ShinyStan/html/home_page_links.html0000644000176200001440000000130413035516434022147 0ustar liggesusersshinystan/inst/ShinyStan/html/mcse.html0000644000176200001440000000247613035516434017765 0ustar liggesusers

mcse (se_mean)

Quick definition The standard error of the mean of the posterior draws (not to be confused with the standard deviation of the posterior draws) is the uncertainty associated with the Monte Carlo approximation. This quantity approaches 0 as the sample size goes to infinity, whereas the standard deviation of the posterior draws approaches the standard deviation of the posterior distribution.

More details

When estimating a mean based on a sample of \(M\) independent draws, the estimation error is proportional to \(1/M\). If the draws are positively correlated, as they typically are when drawn using MCMC methods, the error is proportional to \(1/\sqrt{n_{eff}}\) where \(n_{eff}\) is the effective sample size. Thus it is standard practice to also monitor (an estimate of) the effective sample size until it is large enough for the estimation or inference task at hand.

shinystan/inst/ShinyStan/html/nleapfrog.html0000644000176200001440000000101313035516434020775 0ustar liggesusers

n_leapfrog

Quick definition The number of leapfrog steps (calculations) taken during the Hamiltonian simulation.

More details

If n_leapfrog is too small, the trajectory traced out in each iteration will be too short and sampling will devolve to a random walk. If n_leapfrog is too large, the algorithm will do too much work on each iteration.

shinystan/inst/ShinyStan/html/contribs.html0000644000176200001440000000024713035516434020653 0ustar liggesusersMichael Andreae
Michael Betancourt
Bob Carpenter
Yuanjun Gao
Andrew Gelman
Ben Goodrich
Daniel Lee
Dongying Song
Rob Trangucci shinystan/inst/ShinyStan/html/nuts.html0000644000176200001440000000665713061051366020031 0ustar liggesusers

HMC and NUTS (very briefly)

This is a very brief overview. For more details see the Stan manual and Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo.

Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) method that uses the derivatives of the density function being sampled to generate efficient transitions spanning the posterior. It uses an approximate Hamiltonian dynamics simulation based on numerical integration which is then corrected by performing a Metropolis acceptance step.

Algorithm summary The Hamiltonian Monte Carlo algorithm starts at a specified initial set of parameters; in Stan, this value is either user-specified or generated randomly. Then, for a given number of iterations, a new momentum vector is sampled and the current value of the parameters is updated using the leapfrog integrator with discretization time stepsize and number of steps n_leapfrog according to the Hamiltonian dynamics. Then a Metropolis acceptance step is applied, and a decision is made whether to update to the new state or keep the existing state.

No-U-Turn Sampler

The no-U-turn sampler (NUTS) automatically selects an appropriate n_leapfrog in each iteration in order to allow the proposals to traverse the posterior without doing unnecessary work. The motivation is to maximize the expected squared jump distance (see, e.g., Roberts et al. (1997)) at each step and avoid the random-walk behavior that arises in random-walk Metropolis or Gibbs samplers when there is correlation in the posterior. For a precise definition of the NUTS algorithm see Hoffman and Gelman (2011, 2014)

Algorithm summary NUTS generates a proposal by starting at an initial position determined by the parameters drawn in the last iteration. It then generates an independent unit-normal random momentum vector. It then evolves the initial system both forwards and backwards in time to form a balanced binary tree. At each iteration of the NUTS algorithm the treedepth is increased by one, doubling n_leapfrog and effectively doubling the computation time. The algorithm terminates in one of two ways, either
  • the NUTS criterion (i.e., a U-turn in Euclidean space on a subtree) is satisfied for a new subtree or the completed tree, or
  • the depth of the completed tree hits the maximum depth allowed.
Rather than using a standard Metropolis step, the final parameter value is selected via multinomial sampling among the Hamiltonian trajectories.

Configuring the no-U-turn sampler involves putting a cap on the treedepth that it evaluates during each iteration. This is controlled through a maximum depth parameter. The number of leapfrog steps taken is then bounded by 2 to the power of the maximum depth minus 1.

For more details see Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo.

shinystan/inst/ShinyStan/html/stepsize.html0000644000176200001440000000133313035516434020673 0ustar liggesusers

step_size

Quick definition The integrator step size used in the Hamiltonian simulation.

More details

All implementations of HMC use numerical integrators requiring a step size (equivalently, discretization time interval).

If step_size is too large, the leapfrog integrator will be inaccurate and too many proposals will be rejected. If step_size is too small, too many small steps will be taken by the leapfrog integrator leading to long simulation times per interval. Thus the goal is to balance the acceptance rate between these extremes.

shinystan/inst/ShinyStan/text/0000755000176200001440000000000013035516434016157 5ustar liggesusersshinystan/inst/ShinyStan/text/quick_mcse.txt0000644000176200001440000000063613035516434021050 0ustar liggesusersThe standard error of the mean of the posterior draws (not to be confused with the standard deviation of the posterior draws) is the uncertainty associated with the Monte Carlo approximation. This quantity approaches 0 as the sample size goes to infinity, whereas the standard deviation of the posterior draws approaches the standard deviation of the posterior distribution. See the Glossary page for more details.shinystan/inst/ShinyStan/text/quick_neff.txt0000644000176200001440000000051513035516434021033 0ustar liggesusersn_eff (ESS) is an estimate of the effective number of independent draws from the posterior distribution of the estimand of interest. Because the draws within a chain are not independent if there is autocorrelation, the effective sample size will be smaller than the total number of iterations. See the Glossary page for more details.shinystan/inst/ShinyStan/text/quick_rhat.txt0000644000176200001440000000113413035516434021051 0ustar liggesusersOne 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 Gelman and Rubin potential scale reduction statistic Rhat. The Rhat statistic measures the ratio of the average variance of samples within each chain to the variance of the pooled samples across chains; if all chains are at equilibrium, these will be the same and Rhat will be one. If the chains have not converged to a common distribution, the Rhat statistic will be greater than one. See the Glossary page for more details. shinystan/inst/ShinyStan/server_utils.R0000644000176200001440000000024313035516434020043 0ustar liggesusers# function to suppress unnecessary warnings and messages generated by ggplot suppress_and_print <- function(x) { suppressMessages(suppressWarnings(print(x))) } shinystan/inst/doc/0000755000176200001440000000000013271714530014017 5ustar liggesusersshinystan/inst/doc/deploy_shinystan.html0000644000176200001440000003167013271714530020310 0ustar liggesusers Deploying to shinyapps.io

Deploying to shinyapps.io

09/17/2015

Create a ShinyStan app unique to your model and host it online with RStudio’s ShinyApps service (shinyapps.io). Each app you deploy will have a unique url.

Note: R users who don’t use RStudio’s IDE can still deploy ShinyStan apps to shinyapps.io.

Step 1: ShinyApps account

Signup

To deploy your app to RStudio’s shinyapps.io you will need a ShinyApps account. If you don’t already have one you can sign up at http://www.shinyapps.io/.

The only limit to the number of different ShinyStan apps you can deploy is the limit set by RStudio for the type of ShinyApps account you sign up for.

Setup

When you finish signing up for your ShinyApps account there will be instructions for setting up your account on your local system using rsconnect::setAccountInfo. If you have the most recent version of shinystan then you will already have rsconnect installed and you can call

rsconnect::setAccountInfo(name, token, secret)

where name is your ShinyApps account name, and token and secret can be found from your ShinyApps account web page.

Step 2: Use deploy_shinystan to deploy your app to shinyapps.io

The deploy_shinystan function will deploy a ShinyStan app unique to your model to RStudio’s ShinyApps service.

For the example below assume that

  • my_sso is the shinystan object you want to use
  • the name you want to use for the app is MyModel
  • the username for your ShinyApps account (from Step 1) is username.

To deploy the app use the command

deploy_shinystan(my_sso, appName = "MyModel", account = "username")

When to specify the account argument

If you have multiple ShinyApps accounts configured on your local system then the account argument is required. If you only have a single account then you can omit account

deploy_shinystan(my_sso, appName = "MyModel")

Graphical posterior predictive checks: the optional ppcheck_data and ppcheck_yrep arguments

If you have a vector of observations y that you want to use for ShinyStan’s graphical posterior predictive checks then you can also specify the optional ppcheck_data argument

deploy_shinystan(my_sso, appName = "MyModel", 
                 ppcheck_data = y)

If you’ve specified the ppcheck_data argument you can also use the ppcheck_yrep argument to tell ShinyStan the name of the parameter/quantity in your model containing the posterior predictive simulations/replications so that it is preselected as the parameter to use for doing the graphical posterior predictive checks. This is never required because you can also set this manually while using the app, but it is helpful if you want other people to view your model online because it avoids the need to communicate to them which parameter they need to select. If this parameter/quantity containing the posterior predictive replications is named yRep then you would use

deploy_shinystan(my_sso, appName = "MyModel", 
                 ppcheck_data = y, ppcheck_yrep = "yRep")

If the deployment process is successful the url for your app will be printed to the console and it should open in your web browser. You can also view your app by going to http://www.shinyapps.io and logging into your account.

shinystan/inst/doc/shinystan-package.html0000644000176200001440000004547513271714530020335 0ustar liggesusers Getting Started

Getting Started

2018-04-30

Using the ShinyStan app with different types of objects

stanfit objects

If my_stanfit is a stanfit object (the result of fitting a model with rstan), then to launch the ShinyStan app simply use

library(shinystan)
my_sso <- launch_shinystan(my_stanfit)

and ShinyStan will launch. Here my_sso is the name you want to use for the shinystan object that will be returned. If you simply run

launch_shinystan(my_stanfit)

then ShinyStan will launch but no shinystan object will be saved upon quitting the app.

Sometimes keeping only a subset of parameters before can improve performance. This can be done by creating an object with as.shinystan and specifying the pars argument. The resulting shinystan object can then be passed to launch_shinystan().

stanreg and brmsfit objects

The rstanarm and brms packages provide launch_shinystan methods for stanreg and brmsfit objects, respectively. For example, the method for stanreg objects is documented at

http://mc-stan.org/rstanarm/reference/launch_shinystan.stanreg.html

mcmc.list objects

If you have an mcmc.list object called my_mcmc then you can use the as.shinystan function to convert my_mcmc into a shinystan object that can then be used with launch_shinystan:

my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model"))

If, for example, the first 100 iterations in each chain in my_mcmc are warmup iterations, you should add the warmup argument when you call as.shinystan:

my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model", warmup = 100))

However, you should only use the warmup argument if the warmup iterations have been saved and included in my_mcmc.

Other types of objects

3-D array

To convert a 3-D array to a shinystan object make sure that the three dimensions of the array correspond to the number of iterations x number of chains x number of parameters. You can then as.shinystan exactly how it’s used in the examples for mcmc.list objects above (but you don’t need to convert your array to an mcmc.list).

List of matrices

If you have separate chains that are each a matrix (with iterations as rows and parameters as columns) you can combine them in a list to pass to as.shinystan

# Generate some fake data
chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))
chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))
chain_list <- list(chain1, chain2)
my_sso <- launch_shinystan(as.shinystan(X = list(chain1, chain2), model_name = "my_model"))

Other functions in the shinystan package

Generating new quantities

You can add a new parameter/quantity as a function of one or two existing parameters to your shinystan object by using the generate_quantity function. For example, assume sso is a shinystan object and two of the parameters are alpha and beta. We could add a parameter gamma that is the inverse logit of beta using the code

inv_logit <- function(x) 1/(1 + exp(-x))
sso <- generate_quantity(sso, fun = inv_logit, param1 = "beta", new_name = "gamma")

Here, fun is the function we want to use, param1 is the name of the parameter to apply the function to, and new_name is the name to give the new parameter.

Adding a parameter as a function of two parameters just requires specifying the param2 argument and providing a function of two variables. For example, we can add a parameter delta to sso that is the squared difference of alpha and beta like this

sso <- generate_quantity(sso, fun = function(x,y) (x-y)^2,
                       param1 = "alpha", param2 = "beta", new_name = "delta")

Storing your model code in a shinystan object

For models fit using rstan the model code will automatically be stored in the model_code slot of your shinystan object. When ShinyStan is open you can view your model code in the Model Code tab.

If you did not use rstan fit your model then you can add your model code by using the model_code() function. For example, you may have used Bugs or JAGS or some other software and want to add the following code

  for (i in 1:length(Y)) {
     Y[i] ~ dpois(lambda[i])
     log(lambda[i]) <- inprod(X[i,], theta[])
   }
   for (j in 1:J) {
     theta[j] ~ dt(0.0, 1.0, 1.0)
   }
 }

to your shinystan object. To add that code you can simply include it as the code argument to the model_code function

my_code <- "
 model {
  for (i in 1:length(Y)) {
     Y[i] ~ dpois(lambda[i])
     log(lambda[i]) <- inprod(X[i,], theta[])
   }
   for (j in 1:J) {
     theta[j] ~ dt(0.0, 1.0, 1.0)
   }
 }
"

# Add the code to a shinystan object sso
sso <- model_code(sso, code = my_code)

Renaming a model

On the home page ShinyStan will display the model name associated with the shinystan object being used. This name can be set by adding the model_name argument to as.shinystan when creating a shinystan object. For an existing shinystan object you can use the model_name function like this:

sso <- model_name(sso, "new_model_name")

where "new_model_name" is the new name you want to give your model.

shinystan/inst/doc/deploy_shinystan.Rmd0000644000176200001440000000671413035516434020070 0ustar liggesusers--- title: 'Deploying to shinyapps.io' date: "09/17/2015" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{ShinyStan: Deploying to shinyapps.io} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- Create a ShinyStan app unique to your model and host it online with RStudio's ShinyApps service (shinyapps.io). Each app you deploy will have a unique url. *Note: R users who don't use RStudio's IDE can still deploy ShinyStan apps to shinyapps.io.* ## Step 1: ShinyApps account **Signup** To deploy your app to RStudio's shinyapps.io you will need a ShinyApps account. If you don't already have one you can sign up at http://www.shinyapps.io/. The only limit to the number of different ShinyStan apps you can deploy is the limit set by RStudio for the [type of ShinyApps account](https://www.shinyapps.io/#pricing) you sign up for. **Setup** When you finish signing up for your ShinyApps account there will be instructions for setting up your account on your local system using `rsconnect::setAccountInfo`. If you have the most recent version of **shinystan** then you will already have **rsconnect** installed and you can call ```r rsconnect::setAccountInfo(name, token, secret) ``` where `name` is your ShinyApps account name, and `token` and `secret` can be found from your ShinyApps account web page. ## Step 2: Use `deploy_shinystan` to deploy your app to shinyapps.io The `deploy_shinystan` function will deploy a ShinyStan app unique to your model to RStudio's ShinyApps service. For the example below assume that - `my_sso` is the shinystan object you want to use - the name you want to use for the app is `MyModel` - the username for your ShinyApps account (from Step 1) is `username`. To deploy the app use the command ```r deploy_shinystan(my_sso, appName = "MyModel", account = "username") ``` **When to specify the `account` argument** If you have multiple ShinyApps accounts configured on your local system then the `account` argument is required. If you only have a single account then you can omit `account` ```r deploy_shinystan(my_sso, appName = "MyModel") ``` **Graphical posterior predictive checks: the optional `ppcheck_data` and `ppcheck_yrep` arguments** If you have a vector of observations `y` that you want to use for ShinyStan's graphical posterior predictive checks then you can also specify the optional `ppcheck_data` argument ```r deploy_shinystan(my_sso, appName = "MyModel", ppcheck_data = y) ``` If you've specified the `ppcheck_data` argument you can also use the `ppcheck_yrep` argument to tell ShinyStan the name of the parameter/quantity in your model containing the posterior predictive simulations/replications so that it is preselected as the parameter to use for doing the graphical posterior predictive checks. This is never required because you can also set this manually while using the app, but it is helpful if you want other people to view your model online because it avoids the need to communicate to them which parameter they need to select. If this parameter/quantity containing the posterior predictive replications is named `yRep` then you would use ```r deploy_shinystan(my_sso, appName = "MyModel", ppcheck_data = y, ppcheck_yrep = "yRep") ``` If the deployment process is successful the url for your app will be printed to the console and it should open in your web browser. You can also view your app by going to http://www.shinyapps.io and logging into your account. shinystan/inst/doc/shinystan-package.Rmd0000644000176200001440000001265613271713655020115 0ustar liggesusers--- title: 'Getting Started' date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true vignette: > %\VignetteIndexEntry{Getting Started} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Using the ShinyStan app with different types of objects ## stanfit objects If `my_stanfit` is a stanfit object (the result of fitting a model with [**rstan**](http://mc-stan.org/)), then to launch the ShinyStan app simply use ```r library(shinystan) my_sso <- launch_shinystan(my_stanfit) ``` and ShinyStan will launch. Here `my_sso` is the name you want to use for the shinystan object that will be returned. If you simply run ```r launch_shinystan(my_stanfit) ``` then ShinyStan will launch but no shinystan object will be saved upon quitting the app. Sometimes keeping only a subset of parameters before can improve performance. This can be done by creating an object with `as.shinystan` and specifying the `pars` argument. The resulting shinystan object can then be passed to `launch_shinystan()`. ### stanreg and brmsfit objects The **rstanarm** and **brms** packages provide `launch_shinystan` methods for stanreg and brmsfit objects, respectively. For example, the method for stanreg objects is documented at http://mc-stan.org/rstanarm/reference/launch_shinystan.stanreg.html ## mcmc.list objects If you have an `mcmc.list` object called `my_mcmc` then you can use the `as.shinystan` function to convert `my_mcmc` into a shinystan object that can then be used with `launch_shinystan`: ```r my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model")) ``` If, for example, the first 100 iterations in each chain in `my_mcmc` are warmup iterations, you should add the `warmup` argument when you call `as.shinystan`: ```r my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model", warmup = 100)) ``` However, you should only use the `warmup` argument if the warmup iterations have been saved and included in `my_mcmc`. ## Other types of objects ### 3-D array To convert a 3-D array to a shinystan object make sure that the three dimensions of the array correspond to the *number of iterations* **x** *number of chains* **x** *number of parameters*. You can then `as.shinystan` exactly how it's used in the examples for `mcmc.list` objects above (but you don't need to convert your array to an `mcmc.list`). ### List of matrices If you have separate chains that are each a matrix (with iterations as rows and parameters as columns) you can combine them in a list to pass to `as.shinystan` ```r # Generate some fake data chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) chain_list <- list(chain1, chain2) my_sso <- launch_shinystan(as.shinystan(X = list(chain1, chain2), model_name = "my_model")) ``` ****** # Other functions in the shinystan package ## Generating new quantities You can add a new parameter/quantity as a function of one or two existing parameters to your shinystan object by using the `generate_quantity` function. For example, assume `sso` is a shinystan object and two of the parameters are ```alpha``` and ```beta```. We could add a parameter ```gamma``` that is the inverse logit of ```beta``` using the code ```r inv_logit <- function(x) 1/(1 + exp(-x)) sso <- generate_quantity(sso, fun = inv_logit, param1 = "beta", new_name = "gamma") ``` Here, `fun` is the function we want to use, `param1` is the name of the parameter to apply the function to, and `new_name` is the name to give the new parameter. Adding a parameter as a function of two parameters just requires specifying the `param2` argument and providing a function of two variables. For example, we can add a parameter `delta` to `sso` that is the squared difference of `alpha` and `beta` like this ```r sso <- generate_quantity(sso, fun = function(x,y) (x-y)^2, param1 = "alpha", param2 = "beta", new_name = "delta") ``` ## Storing your model code in a shinystan object For models fit using **rstan** the model code will automatically be stored in the `model_code` slot of your shinystan object. When ShinyStan is open you can view your model code in the *Model Code* tab. If you did not use **rstan** fit your model then you can add your model code by using the `model_code()` function. For example, you may have used Bugs or JAGS or some other software and want to add the following code ``` for (i in 1:length(Y)) { Y[i] ~ dpois(lambda[i]) log(lambda[i]) <- inprod(X[i,], theta[]) } for (j in 1:J) { theta[j] ~ dt(0.0, 1.0, 1.0) } } ``` to your shinystan object. To add that code you can simply include it as the `code` argument to the `model_code` function ```r my_code <- " model { for (i in 1:length(Y)) { Y[i] ~ dpois(lambda[i]) log(lambda[i]) <- inprod(X[i,], theta[]) } for (j in 1:J) { theta[j] ~ dt(0.0, 1.0, 1.0) } } " # Add the code to a shinystan object sso sso <- model_code(sso, code = my_code) ``` ## Renaming a model On the home page ShinyStan will display the model name associated with the shinystan object being used. This name can be set by adding the `model_name` argument to `as.shinystan` when creating a shinystan object. For an existing shinystan object you can use the `model_name` function like this: ```r sso <- model_name(sso, "new_model_name") ``` where `"new_model_name"` is the new name you want to give your model. shinystan/tests/0000755000176200001440000000000013035516434013440 5ustar liggesusersshinystan/tests/testthat.R0000644000176200001440000000007613156543311015424 0ustar liggesuserslibrary(testthat) library(shinystan) test_check("shinystan") shinystan/tests/testthat/0000755000176200001440000000000013271757566015317 5ustar liggesusersshinystan/tests/testthat/test_misc.R0000644000176200001440000000131413271435552017417 0ustar liggesuserslibrary(shinystan) library(rstanarm) context("Misc") test_that("options set when package loads", { expect_false(getOption("shinystan.rstudio")) options(shinystan.rstudio = TRUE) expect_true(getOption("shinystan.rstudio")) options(shinystan.rstudio = FALSE) }) test_that("Can work with CSV files created by RStan", { test_data <- data.frame(Y = rnorm(10)) samples_tmp <- tempfile() fit <- rstanarm::stan_glm(Y ~ 1, data = test_data, sample_file = samples_tmp, chains = 1, iter = 10, sample_file = samples_tmp) fit_csv <- read_stan_csv(samples_tmp) expect_identical(.stan_algorithm(fit_csv), .stan_algorithm(fit$stanfit)) expect_identical(.stan_method(fit_csv), .stan_method(fit$stanfit)) }) shinystan/tests/testthat/test_creating_sso.R0000644000176200001440000001666213044376075021162 0ustar liggesuserslibrary(shinystan) suppressPackageStartupMessages(library(rstan)) suppressPackageStartupMessages(library(rstanarm)) library(coda) sso <- eight_schools array1 <- array(rnorm(300), dim = c(25, 4, 3)) array2 <- array(rnorm(300), dim = c(100, 3)) chains1 <- list(chain1 = cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)), chain2 = cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100))) data(line, package = "coda") mcmc1 <- line mcmc2 <- line[[1L]] suppressWarnings(capture.output( stanreg1 <- stan_glmer(mpg ~ wt + (1 + wt|cyl), data = mtcars, seed = 12345, iter = 200, chains = 2, refresh = 0) )) stanfit1 <- stanreg1$stanfit # load 'old_sso', a shinystan object created by previous shinystan version load("old_sso_for_tests.rda") context("Checking shinystan objects") # sso_check --------------------------------------------------------------- test_that("sso_check throws errors", { expect_error(sso_check(array1)) expect_error(sso_check(chain2)) expect_error(sso_check(chains1)) expect_true(sso_check(sso)) expect_true(sso_check(as.shinystan(array1))) expect_error(sso_check(old_sso), regexp = "use the 'update_sso' function to update your object") }) # is.shinystan ------------------------------------------------------------ test_that("is.shinystan, is.stanfit, is.stanreg work", { expect_true(is.shinystan(sso)) expect_false(is.shinystan(sso@posterior_sample)) expect_true(is.stanfit(stanfit1)) expect_false(is.stanfit(stanreg1)) expect_true(is.stanreg(stanreg1)) expect_false(is.stanreg(stanfit1)) }) context("Creating shinystan objects") # as.shinystan helpers ---------------------------------------------------- test_that("as.shinystan stanfit helpers work", { expect_is(.rstan_max_treedepth(stanfit1), "integer") expect_equal(.rstan_warmup(stanfit1), 0) expect_equal(length(.rstan_sampler_params(stanfit1)), ncol(stanfit1)) expect_is(.rstan_summary(stanfit1), "matrix") expect_identical(.stan_algorithm(stanfit1), "NUTS") expect_false(.used_vb(stanfit1)) expect_false(.from_cmdstan(stanfit1)) expect_is(.stan_args(stanfit1), "list") expect_true(all(c("iter", "seed", "warmup") %in% names(.stan_args(stanfit1)))) stanfit1@stan_args[[1]]$method <- "variational" expect_true(.used_vb(stanfit1)) expect_identical(.rstan_sampler_params(stanfit1), list(NA)) stanfit1@stan_args[[1]]$control$max_treedepth <- NULL expect_equal(.rstan_max_treedepth(stanfit1), 11) }) test_that("deprecation of burnin works properly", { expect_error(as.shinystan(array1, warmup = 2, burnin = 3), "can't both be specified") expect_warning(x <- as.shinystan(array1, burnin = 8), "Use the 'warmup' argument instead") expect_equal(x@n_warmup, 8) expect_silent(x <- as.shinystan(array1, warmup = 7)) expect_equal(x@n_warmup, 7) }) # as.shinystan ------------------------------------------------------------ test_that("as.shinystan (array) creates sso", { expect_s4_class(x <- as.shinystan(array1, model_name = "test", note = "test"), "shinystan") expect_identical(sso_version(x), utils::packageVersion("shinystan")) expect_equal(x@model_name, "test") expect_equal(x@user_model_info, "test") # with sampler_params samp <- sso@posterior_sample sp <- sso@sampler_params x <- as.shinystan(samp, sampler_params = sp, warmup = 759, max_treedepth = 14, algorithm = "NUTS") expect_s4_class(x, "shinystan") expect_equal(x@n_warmup, 759) expect_equal(x@n_chain, dim(samp)[2]) expect_equal(x@n_iter, dim(samp)[1]) expect_equivalent(x@posterior_sample, samp) expect_equal(x@misc$max_td, 14) expect_equal(x@misc$stan_algorithm, "NUTS") }) test_that("as.shinystan (mcmc.list) creates sso", { expect_is(x <- as.shinystan(mcmc1, model_name = "test", note = "test", model_code = "test"), "shinystan") expect_is(as.shinystan(mcmc1[1]), "shinystan") expect_identical(sso_version(x), utils::packageVersion("shinystan")) }) test_that("as.shinystan (list of matrices) creates sso", { expect_is(x <- as.shinystan(chains1, model_code = "test"), "shinystan") expect_is(as.shinystan(chains1[1]), "shinystan") colnames(chains1[[1]]) <- colnames(chains1[[2]]) <- c(paste0("beta[",1:2,"]"), "sigma") sso2 <- as.shinystan(chains1, param_dims = list(beta = 2, sigma = 0)) expect_identical(sso2@param_dims, list(beta = 2, sigma = numeric(0))) expect_identical(sso_version(x), utils::packageVersion("shinystan")) # with sampler_params samp_list <- list() samp <- sso@posterior_sample for (j in 1:ncol(samp)) samp_list[[j]] <- samp[, j, ] sp <- sso@sampler_params x <- as.shinystan(samp_list, sampler_params = sp, warmup = 1000, max_treedepth = 11, algorithm = "NUTS") expect_s4_class(x, "shinystan") }) test_that("as.shinystan (stanreg) creates sso", { expect_message(x <- as.shinystan(stanreg1, model_name = "test"), "preparing graphical posterior predictive checks") expect_is(x, "shinystan") # check that ppc plots created ppc <- x@misc$pp_check_plots expect_type(ppc, "list") expect_s3_class(ppc[[1]], "ggplot") # without ppd x <- as.shinystan(stanreg1, ppd = FALSE) expect_null(x@misc$pp_check_plots) }) test_that("as.shinystan (stanfit) creates sso", { expect_is(x <- as.shinystan(stanfit1, model_name = "test", note = "test"), "shinystan") expect_identical(sso_version(x), utils::packageVersion("shinystan")) }) test_that("as.shinystan throws errors", { expect_error(as.shinystan(array2)) expect_error(as.shinystan(mcmc2)) }) test_that("as.shinystan arguments works with rstanarm example", { sso1 <- as.shinystan(stanreg1) sso2 <- as.shinystan(stanreg1, ppd = FALSE) expect_is(sso1, "shinystan") expect_is(sso2, "shinystan") expect_false(is.null(sso1@misc$pp_check_plots)) expect_null(sso2@misc$pp_check_plots) }) test_that("as.shinystan 'pars' argument works with rstan example", { # load 'stanfit2' saved stanfit object load("stanfit2_for_tests.rda") expect_error(as.shinystan(stanfit2, pars = c("alpha[1,1]", "lp__")), "elements of non-scalar parameters not allowed") sso0 <- as.shinystan(stanfit2) sso1 <- as.shinystan(stanfit2, pars = "alpha") sso2 <- as.shinystan(stanfit2, pars = "beta") sso3 <- as.shinystan(stanfit2, pars = c("alpha", "beta")) expect_identical(sso0, sso3) sso1names <- c("alpha[1,1]", "alpha[2,1]", "alpha[1,2]", "alpha[2,2]", "alpha[1,3]", "alpha[2,3]", "log-posterior") expect_identical(sso1@param_names, sso1names) expect_identical(rownames(sso1@summary), sort(sso1names)) expect_identical(sso2@param_names, c("beta", "log-posterior")) expect_identical(rownames(sso2@summary), c("beta", "log-posterior")) expect_equal(dim(sso1@posterior_sample), c(200, 2, 7)) expect_equal(dim(sso2@posterior_sample), c(200, 2, 2)) }) # update_sso --------------------------------------------------------------- context("Updating shinystan objects") test_that("update_sso errors and messages are correct", { expect_error(update_sso(1234)) expect_message(sso2 <- update_sso(sso), "already up-to-date") expect_is(sso2, "shinystan") expect_message(sso3 <- update_sso(old_sso), "object updated") expect_is(sso3, "shinystan") expect_identical(sso_version(sso3), utils::packageVersion("shinystan")) sso3@misc[["sso_version"]] <- "2.9.5" expect_error(update_sso(sso3), regexp = "was created using a more recent version of shinystan") }) shinystan/tests/testthat/stanfit2_for_tests.rda0000644000176200001440000170353413035516434021627 0ustar liggesusers7zXZi"6!X])TW"nRʟt1L|ۓ̡6_A+ƮϘ|(|`UYڽ2CGR'l,|T ֿvlAIh:YpLvoO>Dk i1TCxQUS\ Prtr'y ݯ$ލ (i^8easTѼ툩1Q'[M u󪭇`b.B;/ p 1SX;R0tQWDWOH9e}tUa]P *%[ dl=ARf`l_Ӏ*uZe o &a'#1X C1.P<^+KtݲGud ҄sc)+6R2dO3m<ΚEqtV諽QMrϕ]VbKF OF aF+J*_.$9~rxsR4!32$ 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5.82967264983337, 10.4250076101431, 8.16827693076258, 6.07598566812009, -18.7547506507413 ), .Names = c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior" )) test_answer_mean <- structure(c(7.61278931524295, 11.4016705206937, 7.66568078208364, 5.67748189168343, 7.33867075036529, 4.43757702342367, 5.61772900604455, 10.9224001073223, 8.35604656671243, 7.32821612535776, -18.3099036670394 ), .Names = c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior" )) test_answer_rhat <- structure(c(1.00193274614794, 1.00452938626942, 1.00125455903399, 1.00127022064874, 1.00165198336501, 1.00372002563809, 1.00307636169776, 1.00300332586848, 1.00086600376535, 1.0135621070749, 1.02233385888746 ), .Names = c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior" )) test_answer_ess <- structure(c(695.050921695149, 692.572280147228, 1209.37138131348, 1297.32046732778, 1115.68142297175, 866.803880031437, 1008.24598387706, 689.500717669359, 1349.69098807226, 271.485295178596, 164.76144165741 ), .Names = c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior" )) test_answer_sd <- structure(c(5.26323739262692, 8.69982993302657, 6.52019611729075, 8.10150161148801, 6.69543707787934, 6.47930101606355, 6.85333063955901, 7.15623795593227, 8.34539836623598, 5.41072834729483, 5.29638545596557 ), .Names = c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior" )) test_answer_accept_stat <- structure(c(0.824390178845497, 0.95133395868425, 0.915935600387729, 0.920407393897412), .Names = c("chain1", "chain2", "chain3", "chain4")) test_answer_divergent <- structure(c(0.006, 0.003, 0, 0.002), .Names = c("chain1", "chain2", "chain3", "chain4")) test_answer_mcse <- structure(c(0.199638660406913, 0.330581244601373, 0.187491168280733, 0.224927153813632, 0.200451279211605, 0.220073491463301, 0.215833291826995, 0.272531942571016, 0.227158973991041, 0.32838444299762, 0.412621442147415 ), .Names = c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior")) test_answer_quantiles <- structure(c(-2.45077906182123, -3.31983983310261, -4.57962616838214, -12.4578126839486, -6.62936539181392, -9.08938973639197, -8.63855924130489, -1.72877568253281, -8.31237064690878, 1.09201252476925, -27.7751080865317, 4.12068194893101, 5.73411025831149, 3.17044970534105, 1.24377962416165, 2.97370388347474, 0.237131301746035, 1.41862522514148, 5.98638086164563, 3.29894150026978, 3.36183993309446, -21.9839223409667, 7.66326915430084, 10.5456377610072, 7.62721910209057, 6.15397157549917, 7.39187255777517, 4.81489509942057, 5.82967264983337, 10.4250076101431, 8.16827693076258, 6.07598566812009, -18.7547506507413, 10.954089111466, 16.0154148818468, 11.8226936192307, 10.783351812759, 11.6861908782556, 9.0714064733997, 10.0979677042868, 14.9916757839673, 13.0025275234939, 9.82027519277038, -14.9290381144071, 18.049454172935, 31.9617117712557, 20.8705874040468, 20.807183632335, 20.5249077599984, 15.7663003355112, 18.3397826218994, 26.8872295016415, 26.4856883459691, 20.7118172250858, -7.05699018362593 ), .Dim = c(11L, 5L), .Dimnames = list(c("mu", "theta[1]", "theta[2]", "theta[3]", "theta[4]", "theta[5]", "theta[6]", "theta[7]", "theta[8]", "tau", "log-posterior"), c("2.5%", "25%", "50%", "75%", "97.5%"))) test_answer_stepsize <- structure(c(0.265382735953881, 0.0917792726985177, 0.18419718916482, 0.110787656205807), .Names = c("chain1", "chain2", "chain3", "chain4")) test_answer_treedepth <- structure(c(6, 7, 6, 6), .Names = c("chain1", "chain2", "chain3", "chain4")) shinystan/tests/testthat/test_deploy_shinystan.R0000644000176200001440000000223613035516434022061 0ustar liggesuserslibrary(shinystan) context("Deploying") sso <- eight_schools # load 'old_sso', a shinystan object created by previous shinystan version load("old_sso_for_tests.rda") old_sso_msg <- "use the 'update_sso' function to update your object" test_that("deploy_shinystan error checking works", { expect_error(deploy_shinystan(old_sso), old_sso_msg) expect_error(deploy_shinystan(sso@posterior_sample), regexp = "specify a shinystan object") expect_error(deploy_shinystan(sso), regexp = "'appName' is required") }) test_that("deploy_shinystan preprocessing doesn't error", { expect_silent(deploy_test <- deploy_shinystan(sso, appName = "test", deploy = FALSE)) expect_true(grepl("shinystan", deploy_test, ignore.case = TRUE)) expect_true(dir.exists(deploy_test)) }) test_that("deploy_shinystan pp_check processing functions ok", { x <- "123454321" expect_output(cat(.ppc_lines(x, x)), x) deploy_pp_test <- deploy_shinystan(sso, appName = "test", deploy = FALSE, ppcheck_data = rep(1, 8), ppcheck_yrep = "yrep") expect_true(dir.exists(deploy_pp_test)) }) shinystan/NAMESPACE0000644000176200001440000000201513137716247013522 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(launch_shinystan,default) S3method(launch_shinystan,shinystan) export(as.shinystan) export(deploy_shinystan) export(drop_parameters) export(generate_quantity) export(is.shinystan) export(launch_shinystan) export(launch_shinystan_demo) export(model_code) export(model_name) export(notes) export(rename_model) export(retrieve) export(sso_info) export(update_sso) import(bayesplot) import(dygraphs) import(ggplot2) import(methods) import(shiny) import(threejs) import(utils) import(xts) importFrom(DT,datatable) importFrom(colourpicker,colourInput) importFrom(gridExtra,arrangeGrob) importFrom(gridExtra,grid.arrange) importFrom(gtools,mixedsort) importFrom(markdown,markdownToHTML) importFrom(reshape2,melt) importFrom(rsconnect,deployApp) importFrom(shinyjs,useShinyjs) importFrom(shinythemes,shinytheme) importFrom(stats,acf) importFrom(stats,na.omit) importFrom(stats,quantile) importFrom(stats,time) importFrom(stats,var) importFrom(xtable,print.xtable) importFrom(xtable,xtable) shinystan/NEWS.md0000644000176200001440000000764513271452733013413 0ustar liggesusers# shinystan 2.5.0 * New website with online documentation: http://mc-stan.org/shinystan * Fixed problem with extracting info from fits reconstructed from CSV files (#128,#158), thanks to @martinmodrak # shinystan 2.4.0 * `launch_shinystan` is now an S3 generic with methods. This allows developers of packages that use __shinystan__ to create their own `launch_shinystan` methods instead of using a different function name or creating a naming conflict. # shinystan 2.3.0 ### Fixes * Fix various issues resulting in errors for models fit using static HMC (thanks to Cole Monnahan). * Deprecate `burnin` argument to `as.shinystan`. Use `warmup` instead. Only relevant for models _not_ fit using Stan. ### New features * Add NUTS energy diagnostic plots to Diagnose page * Allowing passing `sampler_params` to `as.shinystan`. This makes it possible to display sampler diagnositcs for HMC/NUTS even if not using Stan's implementation of those algorithms (thanks to Cole Monnahan). # shinystan 2.2.1 * Better compatibility with latest releases of 'shinyjs' and 'DT' packages * `shinystan::launch_shinystan_demo()` now works without first having to load the package with a call to `library` * Unless running on a server, the app now stops running when browser tab is closed # shinystan 2.2.0-1 * Fix bug in `deploy_shinystan` preventing some ShinyStan apps from being deployed # shinystan 2.2.0 * Add optional argument `pars` to the `as.shinystan` method for stanfit objects, allowing a subset of parameters to be selected for inclusion in the resulting shinystan object. * Introduce `drop_parameters` function for removing parameters from a shinystan object (useful for very large objects when you only want to look at a subset of parameters). * Add **rstanarm** to Suggests (in the DESCRIPTION file) so `rstanarm::pp_check` can be called. * Allow selecting `yrep` from global environment for PPcheck * Change `as.shinystan` to S4 generic with methods * Add **rsconnect** to `Imports` in `DESCRIPTION`. * Rename some of the slots in shinystan objects. The `update_sso` function can be used to ensure that old shinystan objects have an internal structure compatible with this release. * Improve line coverage of api tests # shinystan 2.1.0 * Compatibility with recent ggplot2 update * Select parameters via regular expressions in parameters plot ('Estimate' page) * Fix error when selecting an entire parameter group in parameters plot ('Estimate' page) # shinystan 2.0.1 * Fixes small bugs and typos * Adds support for stanreg objects * Moves ggplot2 from Depends to Imports # shinystan 2.0.0 (major update, initial CRAN release) Version 2.0.0 has a new look, a new(ish) name, and a lot of new functionality. Many bugs have also been fixed (see GitHub issue tracker). ### New names * The name of the R package is **shinystan** and the app/GUI is **ShinyStan**. ### Deploy ShinyStan apps to shinyapps.io (RStudio's ShinyApps service) * The `deploy_shinystan` function lets you easily deploy ShinyStan apps to RStudio's shinyapps.io for any of your models. Each of your apps (i.e. each of your models) will have a unique url. ### Some of the new features in ShinyStan app * Rebranding (new look to reflect changes to Stan logo and website) * HMC/NUTS diagnostic plots ('Diagnose' page, 'HMC/NUTS (plots)' tab) * Specify transformations (e.g. log, logit, sqrt, etc.) for density, histogram, bivariate, trivariate plots (on 'Explore' page) and HMC/NUTS diagnostics plots. * Many plots can now also be saved as pdf * Bivariate scatterplot plot also shows divergent transitions and max treedepth saturation (on 'Explore' page) * More detailed glossary entries * (Experimental) Introduce basic graphical posterior predictive checking ('Diagnose' page, 'PPcheck' tab) for limited class of models * Option to show partial autocorrelations ('Diagnose' page, 'Autocorrelation' tab) * Better customization of of posterior summary statistics table * Many improvements to GUI design shinystan/data/0000755000176200001440000000000013044376075013214 5ustar liggesusersshinystan/data/eight_schools.rda0000644000176200001440000310746013271452360016543 0ustar liggesusersBZh91AY&SY8VVϟ|.޾}Bv8%N_>ft}3ס톨ZPׯC|tl)#9:@gޏwq䖾@:vh[osȮ 7cݭ_z}BG^޼RU()[ﵸ=lw}aݷ. hZ4O/`cA\{ϭU٩׫y:{_pǠ ^@(n}(V{p@:{Ww( wT@@Р(Np_@A@}@GP:6O{] yoQ>Sw){݀ }I _x]o@y4;{;ϼ>:4w}`}w R47ܠ췼W=뾞*@>;vPۀ@=}ЫtZx}G(Cl}0;_yU$*vQW(zԤ$D}E>W\޾;MiסG[kz }rhlȽﻛ]4Rnioo}_^ޏlwƧ^{jם]6ڠqέsovoZ=6;fMw{{wvGOovow.mzmuZ{Zfݎ{W﻽nuʰVڻ{{vn.D{ˬN[w[/YwNbvњfnn*v=uc ݻu-]MQmpLR3]m}կsS;uWu|[PۮMv:[cvwN{v&b'yۛezt:wi|ٶu]w2׌כQ˹ګݝ^nbs.dmλuέSwZk6o^wgmw{חsS\vŰus7wvskf]Ys9=yc;;wQuvmwemIۯ^u@W]lcgcs]f޽ٹom^7$zN]tv3m(ͳïGZZ+5z{{xwson^;Om5˗3;6.{r]*tvwejTww3m=׻o^=[b滥^k^ys^sn<;7mhշۺrsu7=v2[ڷvۗzٹ&onjw=kݏIvRWK;{۞wEͳsMT]mfruۗmέRӶݛq3{]5wijw5=șh֮vq]:w޽^ ۻsZw[7o\Z]cٵdz^դz޺mӧwNulumQwW}b׻mاtwI+k]sjjۮt][S[7]wfs7skS9wu٥'3[ͮ΋\ݷwzc4תwwo{\]]]썻kmҭ{ޞכ9mͮww;KbN.mcswE[wSۥvַ&γܺ+7}=M:sW씵{ko[v5G5f{e]۝ugvݝݳ]ejݻZm 'kdt˙Wgkn۹rnnW{{vOm{g;{uv]ݷwt;ۇ[9 5YZ^{kusK5ݕ]HZ;]׹[;l|}{nc=w{8ZwO6<c{*'.z^_sj\xͮ,^ܼ[W-swns U#n֓K;\6ܚ5ոA׬gNWz\[;lٝuNTvzh{m޼ݴշ\۶GvnezNw۲[6];nyYu\m7qۧ׽zQfQmXyZqͮׯ{zkݭ8MU۷ۺ.]vݮmmgm^ۼo;j뤖{.y2v{}}{jRX[g]{Oۭu=K[{{\n靺s](mvsy]mwg{fxnG{ugv۹ͼzݙm{Kׯs=7nU{攻mZ<և\m;<{=zv՞;9^{R^{ݍmӧv޶ޏunv'we&Ꝟvvۯww{m{ni^/vm^ڴ-suymMwVΛLٽݽz{2uglm5n׫9{ݎөk.sǸ^9}wLnnggw;ݻwKۚ*{gs{fm׋ v^ӷ]k+Zy׽w][O]j[ݺzo7udx=}^vכ{{ݴݽ=kwywޯ9v{{U-/wywWlkV/Yum:2uvV^;ݞcս{źtnP[ם{myw ѵ'^06uwOn=(;y{vfv{6*׽nws+]l{wnof{2{EO==y[^M@ ;ۮ9=zuz`uֻgTkq뽝m4{SET`L`AbiBd`0 1MhhPU*&&L0 xOȩMU  &LOؘ0)TbL`&M4ɀL 4ɓѐVDh(VB*!K#K  *e JXJ9E/FX aa^Ť1~C 1PP@ B JBT)(HR  (J$8@RDDx 1&+=`q  (%!QQL"0$ D%6/2 ~ "$+UB =+" I<ÀB z, =iQjOJ$I"(Q+iUaT&†P(eB QxuA"f0)@_ "@Pgh|@s "E ! @'s;Pq 2STJbl S$qH nNCԂ@\/ !$@dr[eab7 q3z׳j<ً< c/QK) \%Q3obD@(J0 (?V-9^eV$?#=u3pnn9;jA& L\~ ʓ_?tWPw6iK$7`WAq D|2,~B5GNAO)=mor" a >c+ޛW`1"{ёOiށ֤Roɀ6Թsdm{r߄_4Cȯ{73Q h-fm6S5P$g<3lfq1˂g`Ðґ('$lun|JifOCVJ!s=n!!Np?N4K>L4AX+Eԧ0FfcsTy&o4_E2[Oͮd̷+o ý ſ 4H]O`"n˕(GO Qq#>o ja7խQ9h"EiAkx]قIۭm/XSC72]ۣp8Qz TnF2ԳyeA_Hl׼/[#+pn čn"?M&"pVUKomonW]M]H:8'``[.G!dћԭemsH*OclnGkbH1U3MjU3:hUSy&뭤=:PA Z-+o]w5.|-X~z0 ~E7 GJAw5"zU)%K,r֢"GKG*0׿w}R6|zb=hH_sAtijȡ /L-i6H=t"yqau=%h␤_}`ԅx>ߦφ6)G/ZJ., {7e]=oN$'f5:٧7msusK?'[&5 i+}vVo3q?' _$ȁQR vжXK妕)= ib=Ñ[~V uч1dڑOAV=LR!a/.]xoѐ_w8kC2Չ2=?5Y{PH쯝ڙZr,_F52X ΀CP"^Ea+w=hY3"vk?!'br Ki'Bi})Մk2y12UQ|lcDg 9xOv 8 qo-eS}P仺%]:=+`C0d1sXfADPD/dP~܎iY!{иarIhE@.GTs"}M6RgΌZb5o y7٨`m;_T4S1OKB$i՘Ӝg2@3o\ZU""V̭~`_xj; Z7(2iUu3軄 fs.g_Xjs kIX2Xlo(s=Wn8@eL@EaB~V1iV;k[#ٕ?PZhAc lcU&&WｏȯTZ~$4b0; FB_LR$c_W>-AsWXb5ҨĒI.*<@ QOn-a b ?#?tpIv+Rw cM!im\ !hB1$n.TϜ{T1oX| 0e% t[= vt=~K@ِr̢CTbS>N$:# \]$_?WG;3Hأvh_odQ]5 vzöUꆶTv)wsgocubɆ6DX@"& O?R8 2ޅNA0 ͹mi*}#nv<Ik0rdbGPy;Xú&dM!҄jFm)FԕuWnzHk<%oDMHy({EeyS$S #h-Y>ѕ>D /Gbe"M(Q|p8P` -Tƃ<I 41<^.OJy"IQI^*8$uܠ/ xS>rD>d6Te_P=d㍱ܬJR9L6XVo(KH=К~mW't}nN `HyO+@4Heƞp~U9Qעi~0XjeR XHFN ^m~d; 'WשFi4L*?n#CCoK=mH*9ȮS}N"=AC۵|Cok/|&6oE 9A=&[53lX 0A``q<:uvʻ[msϮ9i#JJk?9FQST40a6KJVJFﻂ1Q\SPWH9G.)] ?lrئGOˈ 7L)5uxMY"d: dNn.~b e"M: W煏'A4m.4e~]l`'Y/P6)~wgݼZ")COlP5AM 꺉F /4s&NxH %—ZнTQ1KD[q;%EqٸŜL @vߒX cOo0N<˒ %Sku;t bT= ز28ԆE; j-3lO5 E  6nxí}@;F^_D-{Ǒީ2}nM$8H >!q}D!rNTDnCmSe7s!;puhi~óGv~4t>@ġy!%{U'԰]@:,y*EUFg`HrDxsU@ yc%O +Ǯ1[Jq@06t,n + n?~;B Ylj8iW7)cZk @ߚ*B0 vІ޷ nSy/Q{KJƢˆw>oF%14RDpgzIO!BLf,ND:"+ ǀ>k[Mw]amQ@!pP*^"$ DjA)9{ؐ;QCIʌuAt6o"4yG QD R`J7AKT ?> 0ՠUnJT6HIIaF T{ܫO#^ Oyď*~uIZ榣.@C1|>͜&q "/S֨ o$ևjJTORDboGj9U_#cxJ_2T$nq[XgM锊r M.lyĈgLo6<ƃ |({v7H{΄`P'0ǏmDz X_"G:Mu:ݔ4G!c@J=“QY$w6h/I|GyMi=W3@O aQS8PHd|ҝ^o2GpB. 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See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' Get summary statistics from shinystan object #' #' From a shinystan object get rhat, effective sample size, posterior #' quantiles, means, standard deviations, sampler diagnostics, etc. #' #' @export #' @template args-sso #' @param what What do you want to get? See Details, below. #' @param ... Optional arguments, in particular \code{pars} to specify parameter #' names (by default all parameters will be used). For NUTS sampler parameters #' only (e.g. stepsize, treedepth) \code{inc_warmup} can also be specified to #' include/exclude warmup iterations (the default is \code{FALSE}). See #' Details, below. #' #' @details The argument \code{what} can take on the values below. 'Args: #' \code{arg}' means that \code{arg} can be specified in \code{...} for this #' value of \code{what}. #' \describe{ #' \item{\code{"rhat"}, \code{"Rhat"}, \code{"r_hat"}, or \code{"R_hat"}}{returns: Rhat statistics. Args: \code{pars}} #' \item{\code{"N_eff"}, \code{"n_eff"}, \code{"neff"}, \code{"Neff"}, \code{"ess"}, or \code{"ESS"}}{returns: Effective sample sizes. Args: \code{pars}} #' \item{\code{"mean"}}{returns: Posterior means. Args: \code{pars}} #' \item{\code{"sd"}}{returns: Posterior standard deviations. Args: \code{pars}} #' \item{\code{"se_mean"} or \code{"mcse"}}{returns: Monte Carlo standard error. Args: \code{pars}} #' \item{\code{"median"}}{returns: Posterior medians. Args: \code{pars}.} #' \item{\code{"quantiles"} or any string with \code{"quant"} in it (not case sensitive)}{returns: 2.5\%, 25\%, 50\%, 75\%, 97.5\% posterior quantiles. Args: \code{pars}.} #' \item{\code{"avg_accept_stat"} or any string with \code{"accept"} in it (not case sensitive)}{returns: Average value of "accept_stat" (which itself is the average acceptance probability over the NUTS subtree). Args: \code{inc_warmup}} #' \item{\code{"prop_divergent"} or any string with \code{"diverg"} in it (not case sensitive)}{returns: Proportion of divergent iterations for each chain. Args: \code{inc_warmup}} #' \item{\code{"max_treedepth"} or any string with \code{"tree"} or \code{"depth"} in it (not case sensitive)}{returns: Maximum treedepth for each chain. Args: \code{inc_warmup}} #' \item{\code{"avg_stepsize"} or any string with \code{"step"} in it (not case sensitive)}{returns: Average stepsize for each chain. Args: \code{inc_warmup}} #' } #' #' @note Sampler diagnostics (e.g. \code{"avg_accept_stat"}) only available for #' models originally fit using Stan. #' #' @examples #' # Using example shinystan object 'eight_schools' #' sso <- eight_schools #' retrieve(sso, "rhat") #' retrieve(sso, "mean", pars = c('theta[1]', 'mu')) #' retrieve(sso, "quantiles") #' retrieve(sso, "max_treedepth") # equivalent to retrieve(sso, "depth"), retrieve(sso, "tree"), etc. #' retrieve(sso, "prop_divergent") #' retrieve(sso, "prop_divergent", inc_warmup = TRUE) #' retrieve <- function(sso, what, ...) { sso_check(sso) .retrieve(sso, what, ...) } # retrieve helpers .retrieve <- function(sso, what, ...) { if (what %in% c("rhat", "rhats", "Rhat", "Rhats", "r_hat", "R_hat")) return(retrieve_rhat(sso, ...)) if (what %in% c("N_eff", "n_eff", "neff", "Neff", "ess", "ESS")) return(retrieve_neff(sso, ...)) if (grepl_ic("mean", what)) return(retrieve_mean(sso, ...)) if (grepl_ic("sd", what)) return(retrieve_sd(sso, ...)) if (what %in% c("se_mean", "mcse")) return(retrieve_mcse(sso, ...)) if (grepl_ic("quant", what)) return(retrieve_quant(sso, ...)) if (grepl_ic("median", what)) return(retrieve_median(sso, ...)) if (grepl_ic("tree", what) | grepl_ic("depth", what)) return(retrieve_max_treedepth(sso, ...)) if (grepl_ic("step", what)) return(retrieve_avg_stepsize(sso, ...)) if (grepl_ic("diverg", what)) return(retrieve_prop_divergent(sso, ...)) if (grepl_ic("accept", what)) return(retrieve_avg_accept(sso, ...)) } retrieve_rhat <- function(sso, pars) { if (missing(pars)) return(slot(sso, "summary")[, "Rhat"]) slot(sso, "summary")[pars, "Rhat"] } retrieve_neff <- function(sso, pars) { if (missing(pars)) return(slot(sso, "summary")[, "n_eff"]) slot(sso, "summary")[pars, "n_eff"] } retrieve_mcse <- function(sso, pars) { if (missing(pars)) return(slot(sso, "summary")[, "se_mean"]) slot(sso, "summary")[pars, "se_mean"] } retrieve_quant <- function(sso, pars) { cols <- paste0(100 * c(0.025, 0.25, 0.5, 0.75, 0.975), "%") if (missing(pars)) return(slot(sso, "summary")[, cols]) slot(sso, "summary")[pars, cols] } retrieve_median <- function(sso, pars) { if (missing(pars)) return(retrieve_quant(sso)[, "50%"]) retrieve_quant(sso, pars)[, "50%"] } retrieve_mean <- function(sso, pars) { if (missing(pars)) return(slot(sso, "summary")[, "mean"]) slot(sso, "summary")[pars, "mean"] } retrieve_sd <- function(sso, pars) { if (missing(pars)) return(slot(sso, "summary")[, "sd"]) slot(sso, "summary")[pars, "sd"] } .sp_check <- function(sso) { if (identical(slot(sso, "sampler_params"), list(NA))) stop("No sampler parameters found", call. = FALSE) } .which_rows <- function(sso, inc_warmup) { if (inc_warmup) { seq_len(slot(sso, "n_iter")) } else { seq(from = 1 + slot(sso, "n_warmup"), to = slot(sso, "n_iter")) } } retrieve_max_treedepth <- function(sso, inc_warmup = FALSE) { .sp_check(sso) rows <- .which_rows(sso, inc_warmup) max_td <- sapply(slot(sso, "sampler_params"), function(x) max(x[rows, "treedepth__"])) names(max_td) <- paste0("chain", 1:length(max_td)) max_td } retrieve_prop_divergent <- function(sso, inc_warmup = FALSE) { .sp_check(sso) rows <- .which_rows(sso, inc_warmup) prop_div <- sapply(slot(sso, "sampler_params"), function(x) mean(x[rows, "divergent__"])) names(prop_div) <- paste0("chain", 1:length(prop_div)) prop_div } retrieve_avg_stepsize <- function(sso, inc_warmup = FALSE) { .sp_check(sso) rows <- .which_rows(sso, inc_warmup) avg_ss <- sapply(slot(sso, "sampler_params"), function(x) mean(x[rows, "stepsize__"])) names(avg_ss) <- paste0("chain", 1:length(avg_ss)) avg_ss } retrieve_avg_accept <- function(sso, inc_warmup = FALSE) { .sp_check(sso) rows <- .which_rows(sso, inc_warmup) avg_accept <- sapply(slot(sso, "sampler_params"), function(x) mean(x[rows, "accept_stat__"])) names(avg_accept) <- paste0("chain", 1:length(avg_accept)) avg_accept } shinystan/R/shinystan-objects.R0000644000176200001440000007040013271441212016263 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . # shinystan class definition ------------------------------------------ #' S4 \code{shinystan} objects #' #' @aliases shinystan-class #' @description See \code{\link{as.shinystan}} for documentation on creating #' \code{shinystan} objects and \code{\link{eight_schools}} for an example #' object. #' #' @slot model_name (\code{"character"}) Model name. #' @slot param_names (\code{"character"}) Parameter names. #' @slot param_dims (\code{"list"}) Parameter dimensions. #' @slot posterior_sample (\code{"array"}) MCMC sample. #' @slot summary (\code{"matrix"}) Summary stats for \code{posterior_sample}. #' @slot sampler_params (\code{"list"}) Sampler parameters (for certain Stan #' models only). #' @slot n_chain (\code{"integer"}) Number of chains. #' @slot n_iter (\code{"integer"}) Number of iterations per chain. #' @slot n_warmup (\code{"integer"}) Number of warmup iterations per chain. #' @slot user_model_info (\code{"character"}) Notes to display on the #' \strong{Notepad} page in the 'ShinyStan' GUI. #' @slot model_code (\code{"character"}) Model code to display on the #' \strong{Model Code} page in the 'ShinyStan' GUI. #' @slot misc (\code{"list"}) Miscellaneous, for internal use. #' #' @template seealso-as.shinystan #' @template seealso-drop_parameters #' @template seealso-generate_quantity #' @seealso \code{\link{shinystan-metadata}} to view or change metadata #' associated with a \code{shinystan} object. #' #' @template reference-muth #' shinystan <- setClass( Class = "shinystan", slots = list( model_name = "character", param_names = "character", param_dims = "list", posterior_sample = "array", summary = "matrix", sampler_params = "list", n_chain = "numeric", n_iter = "numeric", n_warmup = "numeric", user_model_info = "character", model_code = "character", misc = "list" ), prototype = list( model_name = "No name", param_names = "", param_dims = list(), posterior_sample = array(NA, c(1, 1)), summary = matrix(NA, nr = 1, nc =1), sampler_params = list(NA), n_chain = 0, n_iter = 0, n_warmup = 0, user_model_info = "Use this space to store notes about your model", model_code = "Use this space to store your model code", misc = list(sso_version = utils::packageVersion("shinystan")) ) ) # create shinystan objects ------------------------------------------------ # as.shinystan (generic) -------------------------------------------------- #' Create and test \code{shinystan} objects #' #' @description The \code{as.shinystan} function creates \code{shinystan} #' objects that can be used with \code{\link{launch_shinystan}} and various #' other functions in the \pkg{shinystan} package. \code{as.shinystan} is a #' generic for which the \pkg{shinystan} package provides several methods. #' Currently methods are provided for creating \code{shinystan} objects from #' arrays, lists of matrices, \code{stanfit} objects (\pkg{rstan}), #' \code{stanreg} objects (\pkg{rstanarm}), and \code{mcmc.list} objects #' (\pkg{coda}). #' #' \code{is.shinystan} tests if an object is a \code{shinystan} object. #' #' @name as.shinystan #' @export #' @param X For \code{as.shinystan}, an object to be converted to a #' \code{shinystan} object. See the Methods section below. For #' \code{is.shinystan}, an object to check. #' @param ... Arguments passed to the individual methods. #' #' @return \code{as.shinystan} returns a \code{shinystan} object, which is an #' instance of S4 class \code{"shinystan"}. #' #' \code{is.shinystan} returns \code{TRUE} if the tested object is a #' \code{shinystan} object and \code{FALSE} otherwise. #' #' @template seealso-launch #' @template seealso-drop_parameters #' @template seealso-generate_quantity #' setGeneric("as.shinystan", function(X, ...) { if (inherits(X, "shinystan")) stop("Already a shinystan object.") standardGeneric("as.shinystan") }) #' @export #' @rdname as.shinystan is.shinystan <- function(X) inherits(X, "shinystan") # as.shinystan (array) --------------------------------------------------- #' @describeIn as.shinystan Create a \code{shinystan} object from a 3-D #' \code{\link{array}} of simulations. The array should have dimensions #' corresponding to iterations, chains, and parameters, in that order. #' #' @param model_name A string giving a name for the model. #' @param burnin Deprecated. Use \code{warmup} instead. The \code{burnin} #' argument will be removed in a future release. #' @param warmup The number of iterations to treat as warmup. Should be #' \code{0} if warmup iterations are not included in \code{X}. #' @param param_dims Rarely used and never necessary. A named list giving the #' dimensions for all parameters. For scalar parameters use \code{0} as the #' dimension. See Examples. #' @param model_code Optionally, a character string with the code used to run #' the model. This can also be added to your \code{shinystan} object later #' using the \code{\link[shinystan]{model_code}} function. See #' \code{\link[shinystan]{model_code}} for additional formatting instructions. #' After launching the app the code will be viewable in the \strong{Model #' Code} tab. For \code{stanfit} (\pkg{rstan}) and \code{stanreg} #' (\pkg{rstanarm}) objects the model code is automatically retrieved from the #' object. #' @param note Optionally, text to display on the \strong{Notepad} page in the #' 'ShinyStan' GUI (stored in \code{user_model_info} slot of the #' \code{shinystan} object). #' @param sampler_params,algorithm,max_treedepth Rarely used and never #' necessary. If using the \code{as.shinystan} method for arrays or lists, #' these arguments can be used to manually provide information that is #' automatically retrieved from a stanfit object when using the #' \code{as.shinystan} method for stanfit objects. If specified, #' \code{sampler_params} must have the same structure as an object returned by #' \code{\link[rstan]{get_sampler_params}} (\pkg{rstan}), which is a list of #' matrices, with one matrix per chain. \code{algorithm}, if specified, must #' be either \code{"NUTS"} or \code{"HMC"} (static HMC). If \code{algorithm} #' is \code{"NUTS"} then \code{max_treedepth} (an integer indicating the #' maximum allowed treedepth when the model was fit) must also be provided. #' #' @examples #' \dontrun{ #' sso <- as.shinystan(X, ...) # replace ... with optional arguments or omit it #' launch_shinystan(sso) #' } #' setMethod( "as.shinystan", signature = "array", definition = function(X, model_name = "unnamed model", warmup = 0, burnin = 0, param_dims = list(), model_code = NULL, note = NULL, sampler_params = NULL, algorithm = NULL, max_treedepth = NULL, ...) { validate_model_code(model_code) is3D <- isTRUE(length(dim(X)) == 3) if (!is3D) stop ("'X' must have 3 dimensions.") if (is.null(dimnames(X)[[3]])) dimnames(X)[[3]] <- paste0("V", seq_len(dim(X)[3])) param_names <- dimnames(X)[[3]] dimnames(X) <- list( iterations = seq_len(nrow(X)), chains = paste0("chain:", seq_len(ncol(X))), parameters = param_names ) sp <- .validate_sampler_params( sampler_params, n_chain = ncol(X), n_iter = nrow(X), algorithm = algorithm ) n_warmup <- .deprecate_burnin(burnin, warmup) sso <- shinystan( model_name = model_name, param_names = param_names, param_dims = .set_param_dims(param_dims, param_names), posterior_sample = X, sampler_params = sp, summary = shinystan_monitor(X, warmup = n_warmup), n_chain = ncol(X), n_iter = nrow(X), n_warmup = n_warmup ) if (!is.null(sampler_params)) { if (is.null(algorithm)) { stop("If 'sampler_params' is specified then 'algorithm' can't be NULL.") } else { algorithm <- match.arg(algorithm, choices = c("HMC", "NUTS")) if (algorithm == "NUTS" && is.null(max_treedepth)) stop("If 'algorithm' is 'NUTS' then 'max_treedepth' must be provided.") } slot(sso, "misc") <- list( max_td = max_treedepth, stan_method = "sampling", stan_algorithm = algorithm, sso_version = utils::packageVersion("shinystan") ) } if (!is.null(note)) sso <- suppressMessages(notes(sso, note = note, replace = TRUE)) if (!is.null(model_code)) sso <- suppressMessages(model_code(sso, code = model_code)) sso <- .rename_scalar(sso, oldname = "lp__", newname = "log-posterior") return(sso) } ) # FIXME: remove this when 'burnin' arg is removed .deprecate_burnin <- function(burnin = 0, warmup = 0) { if (warmup == 0) { if (burnin == 0) { return(0) } else { warning("The 'burnin' argument is deprecated and will be removed ", "in a future release. Use the 'warmup' argument instead.", call. = FALSE) return(burnin) } } else if (burnin == 0) { return(warmup) } else { stop("'burnin' and 'warmup' can't both be specified. ", "'burnin' is deprecated. Please use 'warmup' instead.", call. = FALSE) } } .validate_sampler_params <- function(x, n_chain, n_iter, algorithm = c("NUTS", "HMC")) { if (is.null(x)) return(list(NA)) if (!is.list(x) || length(x) != n_chain || !all(sapply(x, is.matrix))) stop("'sampler_params' must be a list of matrices with one matrix per chain.") if (!all(sapply(x, function(xj) nrow(xj) == n_iter))) stop("Each matrix in 'sampler_params' must have number of rows ", "equal to number of iterations in 'X'.") nms <- sapply(x, colnames) if (!is.character(nms)) stop("Matrices in 'sampler_params' must have column names.") for (j in seq_along(x)) { if (!all.equal(nms[, 1], nms[, j])) stop("All matrices in 'sampler_params' must have the same column names.") } alg <- match.arg(algorithm) if (alg == "NUTS") { nuts_nms <- c( "accept_stat__", "stepsize__", "treedepth__", "n_leapfrog__", "divergent__", "energy__" ) if (!all(nms[, 1] %in% nuts_nms)) stop("For NUTS algorithm the following parameters must be included ", "in 'sampler_params': ", paste(nuts_nms, collapse = ", ")) } return(x) } .set_param_dims <- function(param_dims = list(), param_names = character(length(param_dims))) { if (!length(param_dims)) { param_dims <- list() param_dims[seq_along(param_names)] <- NA names(param_dims) <- param_names for (i in seq_along(param_names)) param_dims[[i]] <- numeric(0) } else { zeros <- sapply(seq_along(param_dims), function(i) 0 %in% param_dims[[i]]) for (i in which(zeros)) param_dims[[i]] <- numeric(0) } param_dims } # as.shinystan (list) --------------------------------------------------- #' @describeIn as.shinystan Create a \code{shinystan} object from a #' \code{\link{list}} of matrices. Each \code{\link{matrix}} (or 2-D array) #' should contain the simulations for an individual chain and all of the #' matrices should have the same number of iterations (rows) and parameters #' (columns). Parameters should have the same names and be in the same order. #' #' @examples #' \dontrun{ #' ######################## #' ### list of matrices ### #' ######################## #' #' # Generate some fake data #' chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) #' chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) #' sso <- as.shinystan(list(chain1, chain2)) #' launch_shinystan(sso) #' #' # We can also specify some or all of the optional arguments #' # note: in order to use param_dims we need to rename 'beta1' and 'beta2' #' # to 'beta[1]' and 'beta[2]' #' colnames(chain1) <- colnames(chain2) <- c(paste0("beta[",1:2,"]"), "sigma") #' sso2 <- as.shinystan(list(chain1, chain2), #' model_name = "Example", warmup = 0, #' param_dims = list(beta = 2, sigma = 0)) #' launch_shinystan(sso2) #' } #' setMethod( "as.shinystan", signature = "list", definition = function(X, model_name = "unnamed model", warmup = 0, burnin = 0, param_dims = list(), model_code = NULL, note = NULL, sampler_params = NULL, algorithm = NULL, max_treedepth = NULL, ...) { validate_model_code(model_code) if (!length(X)) stop('List is empty.') dims <- sapply(X, function(x) length(dim(x))) if (!isTRUE(all(dims == 2))) stop("All elements of X should be matrices / 2-D arrays.") nChain <- length(X) for (i in seq_len(nChain)) { nms <- colnames(as.matrix(X[[i]])) if (is.null(nms) || !all(nzchar(nms))) stop( "Some parameters are missing names. ", "Check the column names for the matrices in your list of chains." ) } if (nChain == 1) { n_iter <- nrow(X[[1]]) param_names <- colnames(X[[1]]) } else { n_iter <- sapply(X, nrow) same_iters <- length(unique(n_iter)) == 1 if (!same_iters) stop("Each chain should contain the same number of iterations.") cnames <- sapply(X, colnames) if (is.array(cnames)) { same_params <- identical(cnames[, 1], cnames[, 2]) param_names <- cnames[, 1] } else { same_params <- length(unique(cnames)) == 1 param_names <- cnames } if (!same_params) stop("The parameters for each chain should be in the same order ", "and have the same names.") n_iter <- n_iter[1] } param_names <- unique(param_names) nParam <- length(param_names) out <- array(NA, dim = c(n_iter, nChain, nParam)) for (i in seq_len(nChain)) out[, i,] <- X[[i]] dimnames(out) <- list( iterations = NULL, chains = paste0("chain:", seq_len(nChain)), parameters = param_names ) as.shinystan( out, model_name = model_name, warmup = .deprecate_burnin(burnin, warmup), param_dims = param_dims, model_code = model_code, note = note, sampler_params = sampler_params, algorithm = algorithm, max_treedepth = max_treedepth, ... ) } ) # as.shinystan (mcmc.list) ----------------------------------------------- setOldClass("mcmc.list") #' @describeIn as.shinystan Create a \code{shinystan} object from an #' \code{mcmc.list} object (\pkg{coda}). #' setMethod( "as.shinystan", signature = "mcmc.list", definition = function(X, model_name = "unnamed model", warmup = 0, burnin = 0, param_dims = list(), model_code = NULL, note = NULL, ...) { check_suggests("coda") validate_model_code(model_code) if (length(X) == 1) { return( as.shinystan( X = list(.mcmclist2matrix(X)), model_name = model_name, warmup = .deprecate_burnin(burnin, warmup), param_dims = param_dims, model_code = model_code, note = note, ... ) ) } posterior <- array( NA, dim = c(coda::niter(X), coda::nvar(X), coda::nchain(X)), dimnames = list( iter = time(X), var = coda::varnames(X), chain = coda::chanames(X) ) ) for (c in seq_len(coda::nchain(X))) posterior[, , c] <- X[[c]] posterior <- aperm(drop(posterior), c(1, 3, 2)) dimnames(posterior) <- list( iterations = seq_len(nrow(posterior)), chains = paste0("chain:", seq_len(ncol(posterior))), parameters = dimnames(posterior)[[3]] ) param_names <- dimnames(X[[1]])[[2]] sso <- shinystan( model_name = model_name, param_names = param_names, param_dims = .set_param_dims(param_dims, param_names), posterior_sample = posterior, summary = shinystan_monitor(posterior, warmup = burnin), n_chain = ncol(posterior), n_iter = nrow(posterior), n_warmup = burnin ) if (!is.null(note)) sso <- suppressMessages(notes(sso, note = note, replace = TRUE)) if (!is.null(model_code)) sso <- suppressMessages(model_code(sso, code = model_code)) return(sso) } ) .mcmclist2matrix <- function(x) { # adapted from Coda package out <- matrix(nrow = coda::niter(x) * coda::nchain(x), ncol = coda::nvar(x)) cols <- seq_len(coda::nvar(x)) for (i in seq_len(coda::nchain(x))) { rows <- (i-1)*coda::niter(x) + seq_len(coda::niter(x)) out[rows, cols] <- x[[i]] } rownames <- character(ncol(out)) rownames[cols] <- coda::varnames(x, allow.null = FALSE) dimnames(out) <- list(NULL, rownames) out } # as.shinystan (stanfit) ------------------------------------------------- setClass("stanfit", getClass("stanfit", where = getNamespace("rstan"))) #' @describeIn as.shinystan Create a \code{shinystan} object from a #' \code{stanfit} object (\pkg{\link[rstan]{rstan}}). Fewer optional arguments #' are available for this method because all important information can be #' taken automatically from the \code{stanfit} object. #' #' @param pars For stanfit objects (\pkg{rstan}), an optional character vector #' specifying which parameters should be included in the \code{shinystan} #' object. #' #' @examples #' \dontrun{ #' ###################### #' ### stanfit object ### #' ###################### #' library("rstan") #' fit <- stan_demo("eight_schools") #' sso <- as.shinystan(fit, model_name = "example") #' } #' setMethod( "as.shinystan", signature = "stanfit", definition = function(X, pars, model_name = X@model_name, note = NULL, ...) { check_suggests("rstan") if (!missing(pars)) { any_indiv_els <- any(grepl("[", pars, fixed = TRUE)) if (any_indiv_els) stop("Individual elements of non-scalar parameters not allowed in 'pars'.") if (!"lp__" %in% pars) pars <- c(pars, "lp__") } posterior <- rstan::extract(X, pars = pars, permuted = FALSE, inc_warmup = TRUE) param_dims <- X@sim$dims_oi if (!missing(pars)) { pd <- which(names(param_dims) %in% pars) if (length(pd)) param_dims <- param_dims[pd] } sso <- shinystan( model_name = model_name, param_names = dimnames(posterior)[[3L]], param_dims = param_dims, posterior_sample = posterior, summary = .rstan_summary(X, pars = pars), sampler_params = .rstan_sampler_params(X), n_chain = ncol(X), n_iter = nrow(posterior), n_warmup = .rstan_warmup(X), model_code = rstan::get_stancode(X), misc = list( max_td = .rstan_max_treedepth(X), stan_method = .stan_method(X), stan_algorithm = .stan_algorithm(X), sso_version = utils::packageVersion("shinystan") ) ) sso <- .rename_scalar(sso, oldname = "lp__", newname = "log-posterior") if (!is.null(note)) sso <- suppressMessages(notes(sso, note, replace = TRUE)) return(sso) } ) # rename a scalar parameter in a shinystan object .rename_scalar <- function(sso, oldname = "lp__", newname = "log-posterior") { p <- which(sso@param_names == oldname) if (identical(integer(0), p)) return(sso) sso@param_names[p] <- dimnames(sso@posterior_sample)$parameters[p] <- names(sso@param_dims)[which(names(sso@param_dims) == oldname)] <- rownames(sso@summary)[p] <- newname return(sso) } # Get stan_args from stanfit object # @param x stanfit object # @param which which of the entries in x@stan_args[[1]] is of interest? If NULL # the full list x@stan_args is returned .stan_args <- function(x, which = NULL) { stan_args <- x@stan_args[[1L]] if (!is.null(which)) return(stan_args[[which]]) stan_args } # Check if model was fit using cmdstan rather than rstan # @param x stanfit object .from_cmdstan <- function(x) { isTRUE("engine" %in% names(.stan_args(x))) } # Check if model was restored from a CSV file generated by rstan # @param x stanfit object .from_rstan_csv <- function(x) { isTRUE("sampler_t" %in% names(.stan_args(x))) } #Get the stan method (variatinal, sampling,optimization) .stan_method <- function(x) { if(.from_rstan_csv(x)) { "sampling" #I assume there is no way to generate sample file withou sampling } else { .stan_args(x, "method") } } # Check if model fit using variational algorithm # @param x stanfit object .used_vb <- function(x) { isTRUE(.stan_args(x, "method") == "variational") } # Check which algorithm was used to fit model # @param x stanfit object .stan_algorithm <- function(x) { algo <- if (.from_cmdstan(x)) { toupper(.stan_args(x, "engine")) } else if (.from_rstan_csv(x)) { #When reading the CSV, the engine is of the form NUTS(diag_e) - getting rid of the brackets here gsub("\\([^)]*\\)","",.stan_args(x, "sampler_t")) } else { .stan_args(x, "algorithm") } if (.used_vb(x) || !(algo %in% c("NUTS", "HMC"))) warning("Many features are only available for models fit using algorithm NUTS or algorithm HMC.", call. = FALSE) algo } # Get summary stats from a stanfit object # @param x stanfit object # @param pars optional vector of parameter names .rstan_summary <- function(x, pars) { stan_summary <- rstan::summary(x, pars = pars)$summary if (!.used_vb(x)) return(stan_summary) cbind(stan_summary, Rhat = NA, n_eff = NA, se_mean = NA) } # Get sampler params from a stanfit object # @param x stanfit object .rstan_sampler_params <- function(x) { if (.used_vb(x)) return(list(NA)) sp <- suppressWarnings(rstan::get_sampler_params(x)) sp <- .rename_sampler_param(sp, oldname = "n_divergent__", newname = "divergent__") sp } # @param x list of sampler param arrays .rename_sampler_param <- function(x, oldname, newname) { if (!identical(x, list(NA))) { for (j in seq_along(x)) { sel <- which(colnames(x[[j]]) == oldname) if (length(sel)) colnames(x[[j]])[sel] <- newname } } return(x) } # Calculate correct value for number of warmup iterations # @param x stanfit object .rstan_warmup <- function(x) { warmup <- if (.from_cmdstan(x)) x@sim$warmup2[1L] else x@sim$warmup saved <- .stan_args(x, "save_warmup") if (!is.null(saved) && !saved) warmup <- 0 if (.from_cmdstan(x)) return(warmup) floor(warmup / x@sim$thin) } # Get value of max_treedepth parameter from stanfit object # @param x stanfit object .rstan_max_treedepth <- function(x) { cntrl <- .stan_args(x, "control") if (is.null(cntrl)) { max_td <- 11 } else { max_td <- cntrl$max_treedepth if (is.null(max_td)) max_td <- 11 } max_td } # as.shinystan (stanreg) ------------------------------------------------- setOldClass("stanreg") #' @describeIn as.shinystan Create a \code{shinystan} object from a #' \code{stanreg} object (\pkg{\link[rstanarm]{rstanarm}}). #' #' @param ppd For \code{stanreg} objects (\pkg{rstanarm}), \code{ppd} #' (logical) indicates whether to draw from the posterior predictive #' distribution before launching the app. The default is \code{TRUE}, #' although for very large objects it can be convenient to set it to #' \code{FALSE} as drawing from the posterior predictive distribution can be #' time consuming. If \code{ppd} is \code{TRUE} then graphical posterior #' predictive checks are available when 'ShinyStan' is launched. #' @param seed Passed to \code{\link[rstanarm]{pp_check}} (\pkg{rstanarm}) if #' \code{ppd} is \code{TRUE}. #' #' @examples #' \dontrun{ #' ###################### #' ### stanreg object ### #' ###################### #' library("rstanarm") #' example("example_model") #' sso <- as.shinystan(example_model) #' launch_shinystan(sso) #' } #' setMethod( "as.shinystan", signature = "stanreg", definition = function(X, ppd = TRUE, seed = 1234, model_name = NULL, note = NULL, ...) { check_suggests("rstanarm") sso <- as.shinystan(X$stanfit, ...) mname <- if (!is.null(model_name)) model_name else paste0("rstanarm model (", sso@model_name, ")") sso <- suppressMessages(model_name(sso, mname)) if (!is.null(note)) sso <- suppressMessages(notes(sso, note, replace = TRUE)) param_names <- slot(sso, "param_names") sel <- grep(":_NEW_", dimnames(slot(sso, "posterior_sample"))[[3L]], fixed = TRUE) if (length(sel)) { param_names <- param_names[-sel] slot(sso, "posterior_sample") <- slot(sso, "posterior_sample")[, , -sel, drop = FALSE] slot(sso, "summary") <- slot(sso, "summary")[-sel, , drop = FALSE] } param_dims <- rep(list(numeric(0)), length(param_names)) names(param_dims) <- param_names slot(sso, "param_names") <- param_names slot(sso, "param_dims") <- param_dims slot(sso, "misc")[["stanreg"]] <- TRUE if (isTRUE(ppd)) slot(sso, "misc")[["pp_check_plots"]] <- .rstanarm_pp_checks(X, seed) return(sso) } ) .rstanarm_pp_checks <- function(X, seed, ...) { message( "\nHang on... preparing graphical posterior predictive checks for rstanarm model.", "\nSee help('shinystan', 'rstanarm') for how to disable this feature." ) ppc <- rstanarm::pp_check pp_check_plots <- list() pp_check_plots[["pp_check_hist"]] <- do.call("ppc", list( object = X, plotfun = "hist", nreps = 8, seed = seed )) pp_check_plots[["pp_check_dens"]] <- do.call("ppc", list( object = X, plotfun = "dens_overlay", nreps = 50, seed = seed )) pp_check_plots[["pp_check_resid"]] <- do.call("ppc", list( object = X, plotfun = "error_hist", nreps = 8, seed = seed )) pp_check_plots[["pp_check_scatter"]] <- do.call("ppc", list( object = X, plotfun = "scatter_avg", nreps = NULL, seed = seed )) pp_check_plots[["pp_check_stat_mean"]] <- do.call("ppc", list( object = X, plotfun = "stat", stat = "mean", seed = seed )) pp_check_plots[["pp_check_stat_sd"]] <- do.call("ppc", list( object = X, plotfun = "stat", stat = "sd", seed = seed )) pp_check_plots[["pp_check_stat_min"]] <- do.call("ppc", list( object = X, plotfun = "stat", stat = "min", seed = seed )) pp_check_plots[["pp_check_stat_max"]] <- do.call("ppc", list( object = X, plotfun = "stat", stat = "max", seed = seed )) pp_check_plots } shinystan/R/update_sso.R0000644000176200001440000000566213035516434015001 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' Update an object created by the previous version of shinystan #' #' If you encounter any errors when using a shinystan object (\code{sso}) #' created by a previous version of \pkg{shinystan}, you might need to run #' \code{update_sso}. If \code{update_sso} does not resolve the problem and #' you still have the object (e.g. stanfit, stanreg, mcmc.list) from which #' \code{sso} was originally created, you can create a new shinystan object #' using \code{\link{as.shinystan}}. #' #' @export #' @template args-sso #' @return If \code{sso} is already compatible with your version of #' \pkg{shinystan} then \code{sso} itself is returned and a message is printed #' indicating that \code{sso} is already up-to-date. Otherwise an updated #' version of \code{sso} is returned unless an error is encountered. #' #' @template seealso-as.shinystan #' #' @examples #' \dontrun{ #' sso_new <- update_sso(sso) #' } #' update_sso <- function(sso) { stopifnot(is.shinystan(sso)) sso_ver <- sso_version(sso) shinystan_ver <- utils::packageVersion("shinystan") if (sso_ver == shinystan_ver) { message(deparse(substitute(sso)), " already up-to-date.") return(sso) } else if (sso_ver > shinystan_ver) { stop( deparse(substitute(sso)), " was created using a more recent version ", "of shinystan than the one you are currently using. ", "Please update your version of the shinystan package." ) } slot(sso, "sampler_params") <- .rename_sampler_param(slot(sso, "sampler_params"), oldname = "n_divergent__", newname = "divergent__") sso_new <- shinystan() for (sn in slotNames(sso_new)) { if (.hasSlot(sso, sn)) { slot(sso_new, sn) <- slot(sso, sn) } else { new_slots <- c("posterior_sample", "n_chain", "n_iter", "n_warmup") old_slots <- c("samps_all", "nChains", "nIter", "nWarmup") j <- which(new_slots == sn) if (!length(j)) stop("Bug found. Slot ", sn, " can't be updated.") if (.hasSlot(sso, old_slots[j])) { slot(sso_new, sn) <- slot(sso, old_slots[j]) } else { stop("slot ", sn, " not found in ", deparse(substitute(sso))) } } } sso_new@misc[["sso_version"]] <- utils::packageVersion("shinystan") message("shinystan object updated.") sso_new } shinystan/R/sso-metadata.R0000644000176200001440000001632413140211075015200 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' View or change metadata associated with a \code{shinystan} object #' #' @name shinystan-metadata #' @template args-sso #' #' @template seealso-as.shinystan #' @template seealso-drop_parameters #' @template seealso-generate_quantity #' #' @examples #' # use eight_schools example object #' sso <- eight_schools #' NULL # sso_info ---------------------------------------------------------------- #' @rdname shinystan-metadata #' @export #' #' @return \code{sso_info} prints basic metadata including number of parameters, #' chains, iterations, warmup iterations, etc. It does not return anything. #' #' @examples #' ################ #' ### sso_info ### #' ################ #' #' sso_info(sso) #' sso_info <- function(sso) { sso_check(sso) sso_name <- deparse(substitute(sso)) has_notes <- sso@user_model_info != "Use this space to store notes about your model" has_code <- sso@model_code != "Use this space to store your model code" cat( sso_name, "---------------------", paste("Model name:", sso@model_name), paste("Parameters:", length(sso@param_names)), paste("Parameter groups:", length(names(sso@param_dims))), paste("Chains:", sso@n_chain), paste("Iterations:", sso@n_iter), paste("Warmup:", sso@n_warmup), paste("Has model code:", has_code), paste("Has user notes:", has_notes), sep = "\n" ) } # model_code -------------------------------------------------------------- #' @rdname shinystan-metadata #' @export #' @param code A string, containing model code to be added, that can be #' used as an argument to \code{\link{cat}}. See \strong{Examples}. #' #' @return \code{model_code} returns or replaces model code stored in a #' \code{shinystan} object. If \code{code} is \code{NULL} then any existing #' model code stored in \code{sso} is returned as a character string. If #' \code{code} is specified then an updated \code{shinystan} object is #' returned with \code{code} added. For \code{shinystan} objects created from #' stanfit (\pkg{rstan}) and stanreg (\pkg{rstanarm}) objects, model code is #' automatically taken from that object and does not need to be added #' manually. From within the 'ShinyStan' interface model code can be viewed on #' the \strong{Model Code} page. #' #' @examples #' ################## #' ### model_code ### #' ################## #' #' # view model code in example shinystan object 'eight_schools' #' cat(model_code(sso)) #' #' # change the model code in sso #' # some jags style code #' my_code <- " #' model { #' for (i in 1:length(Y)) { #' Y[i] ~ dpois(lambda[i]) #' log(lambda[i]) <- inprod(X[i,], theta[]) #' } #' for (j in 1:J) { #' theta[j] ~ dt(0.0, 1.0, 1.0) #' } #' } #' " #' sso <- model_code(sso, my_code) #' cat(model_code(sso)) #' model_code <- function(sso, code = NULL) { sso_check(sso) validate_model_code(code) if (is.null(code)) return(slot(sso, "model_code")) slot(sso, "model_code") <- code message( paste0( "Successfully added code.", "\nYou can view the code in the", "ShinyStan GUI on the 'Model Code' page." ) ) sso } validate_model_code <- function(code) { if (is.null(code) || is.character(code)) { invisible(TRUE) } else { stop("Model code should be NULL or a string", call. = FALSE) } } # notes ------------------------------------------------------------------- #' @rdname shinystan-metadata #' @export #' @param note A string containing a note to add to any existing notes #' or replace existing notes, depending on the value of \code{replace}. #' @param replace If \code{TRUE} the existing notes are overwritten by #' \code{note} if \code{note} is specified. If \code{FALSE} (the default) #' if \code{note} is specified then its content is appended to the existing #' notes. #' #' @return \code{notes} returns, amends, or replaces notes stored in a #' \code{shinystan} object. If \code{note} is \code{NULL} then any existing #' notes stored in \code{sso} are returned as a character string. If #' \code{note} is specified then an updated \code{shinystan} object is #' returned with either \code{note} added to the previous notes (if #' \code{replace=FALSE}) or overwritten by \code{note} (if \code{replace = #' TRUE}). From within the 'ShinyStan' interface, notes are viewable on the #' \strong{Notepad} page. #' #' @examples #' ############# #' ### notes ### #' ############# #' #' # view existing notes #' notes(sso) #' #' # add a note to the existing notes #' sso <- notes(sso, "New note") #' notes(sso) #' cat(notes(sso)) #' #' # replace existing notes #' sso <- notes(sso, "replacement note", replace = TRUE) #' notes(sso) #' notes <- function(sso, note = NULL, replace = FALSE) { sso_check(sso) if (is.null(note)) return(slot(sso, "user_model_info")) if (!is.character(note) || !isTRUE(length(note) == 1)) stop("'note' should be a single string") slot(sso, "user_model_info") <- if (replace) note else c(slot(sso, "user_model_info"), paste0("\n\n", note)) message( paste( "Successfully added note.", "\nYou can view the notes in the", "ShinyStan GUI on the 'Notepad' page." ) ) sso } # model_name (renaming) -----------------------------------------------------#' #' @rdname shinystan-metadata #' @export #' @param name A string giving the new model name to use. #' #' @return \code{model_name} returns or replaces the model name associated with #' a \code{shinystan} object. If \code{name} is \code{NULL} then the current #' model name is returned. If \code{name} is specified then \code{sso} is #' returned with an updated model name. #' #' @examples #' ################## #' ### model_name ### #' ################## #' #' # view model name #' model_name(sso) #' #' # change model name #' sso <- model_name(sso, "some other name") #' identical(model_name(sso), "some other name") #' model_name <- function(sso, name = NULL) { sso_check(sso) if (is.null(name)) return(slot(sso, "model_name")) if (!is.character(name) || !isTRUE(length(name) == 1)) stop("'name' should be a single string") slot(sso, "model_name") <- name message(paste("Successfully changed model name to", name)) sso } # nocov start #' rename_model (deprecated) #' #' This function is deprecated and will be removed in a future release. Please #' use the \code{\link{model_name}} function instead. #' #' @export #' @keywords internal #' @param sso,new_model_name Use the \code{\link{model_name}} function instead. #' rename_model <- function(sso, new_model_name) { .Deprecated("model_name()") model_name(sso, new_model_name) } # nocov end shinystan/R/rhat_ess_monitor_fns.R0000644000176200001440000000643513035516434017057 0ustar liggesusers### Functions in this file ### # shinystan_monitor # ess_rfun # split_rhat_rfun # shinystan_monitor ------------------------------------------------------- # Slightly modified version of monitor from RStan package shinystan_monitor <- function(sims, warmup = floor(dim(sims)[1]/2), probs = c(0.025, 0.25, 0.5, 0.75, 0.975), digits_summary = 1) { dim_sims <- dim(sims) dimnames_sims <- dimnames(sims) parnames <- dimnames_sims[[3]] if (length(dim_sims) != 3) stop("'sims' is not a 3-d array") if (warmup > dim_sims[1]) stop("warmup is larger than the total number of iterations") num_par <- dim_sims[3] if (is.null(parnames)) parnames <- paste0("V", 1:num_par) sims_wow <- if (warmup >= 1) apply(sims, c(2, 3), FUN = function(x) x[-(1:warmup)]) else sims m <- apply(sims_wow, 3, mean) sd <- sapply(1:num_par, FUN = function(i) sd(as.vector(sims_wow[, , i]))) quan <- lapply(1:num_par, FUN = function(i) quantile(sims_wow[, , i], probs = probs)) probs_str <- names(quan[[1]]) quan <- do.call(rbind, quan) rhat <- sapply(1:num_par, FUN = function(i) split_rhat_rfun(sims_wow[, , i])) ess <- sapply(1:num_par, FUN = function(i) ess_rfun(sims_wow[, , i])) sem <- sd/sqrt(ess) summary <- cbind(m, sem, sd, quan, ess, rhat) colnames(summary) <- c("mean", "se_mean", "sd", probs_str, "n_eff", "Rhat") rownames(summary) <- parnames invisible(summary) } # From RStan package # ess_rfun ---------------------------------------------------------------- ess_rfun <- function (sims) { if (is.vector(sims)) dim(sims) <- c(length(sims), 1) chains <- ncol(sims) n_samples <- nrow(sims) acov <- lapply(1:chains, FUN = function(i) { cov <- acf(sims[, i], lag.max = n_samples - 1, plot = FALSE, type = c("covariance")) cov$acf[, , 1] }) acov <- do.call(cbind, acov) chain_mean <- apply(sims, 2, mean) mean_var <- mean(acov[1, ]) * n_samples/(n_samples - 1) var_plus <- mean_var * (n_samples - 1)/n_samples if (chains > 1) var_plus <- var_plus + var(chain_mean) rho_hat_sum <- 0 for (t in 2:nrow(acov)) { rho_hat <- 1 - (mean_var - mean(acov[t, ]))/var_plus if (is.nan(rho_hat)) rho_hat <- 0 if (rho_hat < 0) break rho_hat_sum <- rho_hat_sum + rho_hat } ess <- chains * n_samples if (rho_hat_sum > 0) ess <- ess/(1 + 2 * rho_hat_sum) ess } # From RStan package # split_rhat_rfun --------------------------------------------------------- split_rhat_rfun <- function (sims) { if (is.vector(sims)) dim(sims) <- c(length(sims), 1) chains <- ncol(sims) n_samples <- nrow(sims) half_n <- floor(n_samples/2) idx_2nd <- n_samples - half_n + 1 split_chain_mean <- numeric(chains * 2) split_chain_var <- numeric(chains * 2) for (i in 1:chains) { split_chain_mean[i] <- mean(sims[1:half_n, i]) split_chain_var[i] <- var(sims[1:half_n, i]) split_chain_mean[chains + i] <- mean(sims[idx_2nd:n_samples, i]) split_chain_var[chains + i] <- var(sims[idx_2nd:n_samples, i]) } var_between <- half_n * var(split_chain_mean) var_within <- mean(split_chain_var) sqrt((var_between/var_within + half_n - 1)/half_n) } shinystan/R/drop_parameters.R0000644000176200001440000000665313140210525016010 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' Drop parameters from a \code{shinystan} object #' #' Remove selected parameters from a \code{shinystan} object. This is useful if #' you have a very large \code{shinystan} object when you only want to look at a #' subset of parameters. With a smaller \code{shinystan} object, #' \code{\link{launch_shinystan}} will be faster and you should experience #' better performance (responsiveness) after launching when using the #' 'ShinyStan' app. #' #' @export #' @template args-sso #' @param pars A character vector of parameter names. If the name of a #' non-scalar (e.g. vector, matrix) parameter is included in \code{pars} all #' of its elements will be removed. Currently it is not possible to remove #' only a subset of the elements of a non-scalar parameter. #' @return \code{sso}, with \code{pars} dropped. #' #' @template seealso-generate_quantity #' #' @examples #' # Using example shinystan object 'eight_schools' #' print(eight_schools@param_names) #' #' # Remove the scalar parameters mu and tau #' sso <- drop_parameters(eight_schools, pars = c("mu", "tau")) #' print(sso@param_names) #' #' # Remove all elements of the parameter vector theta #' sso <- drop_parameters(sso, pars = "theta") #' print(sso@param_names) #' drop_parameters <- function(sso, pars) { sso_check(sso) stopifnot(is.character(pars)) if (any(c("log-posterior", "lp__") %in% pars)) stop("log-posterior can't be dropped.") any_indiv_els <- any(grepl("[", pars, fixed = TRUE)) if (any_indiv_els) stop("Currently, individual elements of non-scalar parameters can't be removed.") any_dimnames_in_pars <- any(names(sso@param_dims) %in% pars) if (any_dimnames_in_pars) { param_dims <- slot(sso, "param_dims") param_names <- slot(sso, "param_names") pd <- which(names(param_dims) %in% pars) nms <- names(param_dims[pd]) for (j in seq_along(nms)) { if (!nms[j] %in% param_names) { pars <- pars[pars != nms[j]] tmp <- grep(paste0(nms[j], "["), param_names, fixed = TRUE, value = TRUE) pars <- c(pars, tmp) } } slot(sso, "param_dims") <- slot(sso, "param_dims")[-pd] } sel <- match(pars, slot(sso, "param_names")) if (!any_dimnames_in_pars && all(is.na(sel))) { stop("No matches for 'pars' were found.", call. = FALSE) } else if (any(is.na(sel))) { warning(paste( "Some 'pars' not found and ignored:", paste(pars[is.na(sel)], collapse = ", ") )) } .drop_parameters(sso, na.omit(sel)) } # @param rmv A vector of indices indicating the positions of parameters to be # removed .drop_parameters <- function(sso, rmv) { slot(sso, "param_names") <- slot(sso, "param_names")[-rmv] slot(sso, "posterior_sample") <- slot(sso, "posterior_sample")[, , -rmv, drop = FALSE] slot(sso, "summary") <- slot(sso, "summary")[-rmv, , drop = FALSE] sso } shinystan/R/generate_quantity.R0000644000176200001440000000577613140206760016364 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' Add new quantity to shinystan object #' #' Add to shinystan object a new parameter as a function of one or two existing #' parameters. #' #' @export #' @template args-sso #' @param fun Function to call, i.e. \code{function(param1)} or #' \code{function(param1,param2)}. See Examples, below. #' @param param1 Name of first parameter as character string. #' @param param2 Optional. Name of second parameter as character string. #' @param new_name Name for the new parameter as character string. #' #' @return sso, updated. See Examples. #' #' @template seealso-drop_parameters #' #' @examples #' # Using example shinystan object 'eight_schools' #' sso <- eight_schools #' sso <- generate_quantity(sso, fun = function(x) x^2, #' param1 = "tau", new_name = "tau_sq") #' sso <- generate_quantity(sso, fun = "-", #' param1 = "theta[1]", param2 = "theta[2]", #' new_name = "theta1minus2") #' generate_quantity <- function(sso, param1, param2, fun, new_name) { sso_check(sso) if (isTRUE(new_name %in% slot(sso, "param_names"))) stop(paste("There is already a parameter named", new_name)) message("\nThis might take a moment for large shinystan objects...") two_params <- !missing(param2) posterior <- slot(sso, "posterior_sample") dims <- dim(posterior) ndim <- length(dims) if (ndim == 3) { # i.e. multiple chains x_samp <- posterior[, , param1] if (two_params) y_samp <- posterior[, , param2] } arglist <- if (two_params) list(x_samp, y_samp) else list(x_samp) temp <- do.call(fun, args = arglist) new_dim <- dims new_dim[[ndim]] <- new_dim[[ndim]] + 1 new_dim_names <- dimnames(posterior) new_dim_names[[ndim]] <- c(new_dim_names[[ndim]], new_name) posterior <- array(data = c(posterior, temp), dim = new_dim, dimnames = new_dim_names) param_dims_new <- slot(sso, "param_dims") param_dims_new[[new_name]] <- numeric(0) sso_new <- as.shinystan( posterior, model_name = slot(sso, "model_name"), warmup = slot(sso, "n_warmup"), param_dims = param_dims_new ) slot(sso_new, "summary") <- shinystan_monitor(posterior, warmup = slot(sso, "n_warmup")) slot_names <- c("sampler_params", "model_code", "user_model_info", "misc") for (sn in slot_names) slot(sso_new, sn) <- slot(sso, sn) sso_new } shinystan/R/launch_shinystan.R0000644000176200001440000001320113271435552016174 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' Launch the 'ShinyStan' app #' #' Launch the 'ShinyStan' app in the default web browser. 'RStudio' users also #' have the option of launching the app in the pop-up Viewer. #' #' @export #' @param object The object to use. For the default method this can be an object #' of class \code{"shinystan"}, \code{"stanfit"}, or \code{"stanreg"}. To use #' other types of objects first create a shinystan object using #' \code{\link{as.shinystan}}. #' @param rstudio Only relevant for 'RStudio' users. The default (\code{FALSE}) #' is to launch the app in the user's default web browser rather than the #' pop-up Viewer provided by 'RStudio'. Users can change the default to #' \code{TRUE} by setting the global option \code{options(shinystan.rstudio = #' TRUE)}. #' @param ... Optional arguments passed to \code{\link[shiny]{runApp}}. #' #' @return The \code{launch_shinystan} function is used for the side effect of #' starting the 'ShinyStan' app, but it also returns a \code{shinystan} #' object, an instance of S4 class \code{"shinystan"}. #' #' @template seealso-as.shinystan #' @template seealso-update_sso #' @template seealso-demo #' #' @template reference-muth #' @template reference-bayesvis #' #' @examples #' \dontrun{ #' ####################################### #' # Example 1: 'sso' is a shinystan object #' ####################################### #' #' # Just launch shinystan #' launch_shinystan(sso) #' #' # Launch shinystan and replace sso with an updated version of itself #' # if any changes are made to sso while using the app #' sso <- launch_shinystan(sso) #' #' # Launch shinystan but save any changes made to sso while running the app #' # in a new shinystan object sso2. sso will remained unchanged. #' sso2 <- launch_shinystan(sso) #' #' ####################################### #' # Example 2: 'sf' is a stanfit object #' ####################################### #' #' # Just launch shinystan #' launch_shinystan(sf) #' #' # Launch shinystan and save the resulting shinystan object #' sf_sso <- launch_shinystan(sf) #' #' # Now sf_sso is a shinystan object and so Example 1 (above) applies when #' # using sf_sso. #' #' ####################################### #' # Example 3: 'fit' is an mcmc.list, array or list of matrices #' ####################################### #' #' # First create shinystan object (see ?as.shinystan) for full details) #' } #' launch_shinystan <- function(object, ...) { UseMethod("launch_shinystan") } #' @rdname launch_shinystan #' @export launch_shinystan.default <- function(object, ..., rstudio = getOption("shinystan.rstudio")) { if (!is.shinystan(object) && !is.stanfit(object) && !is.stanreg(object)) { stop("object not compatible with 'launch_shinystan'. ", "Try converting to a shinystan object first using 'as.shinystan'.") } object <- as.shinystan(object) message("\nLaunching ShinyStan interface... ", "for large models this may take some time.") invisible(launch(object, rstudio, ...)) } #' @rdname launch_shinystan #' @export launch_shinystan.shinystan <- function(object, ..., rstudio = getOption("shinystan.rstudio")) { sso_check(object) message("\nLaunching ShinyStan interface... ", "for large models this may take some time.") invisible(launch(object, rstudio, ...)) } #' 'ShinyStan' demo #' #' @aliases eight_schools #' @export #' @inheritParams launch_shinystan #' @param demo_name The name of the demo. Currently \code{"eight_schools"} is #' the only option, but additional demos may be available in future releases. #' \describe{ #' \item{\code{eight_schools}}{Hierarchical meta-analysis model. See #' \emph{Meta Analysis} chapter of the 'Stan' manual #' \url{http://mc-stan.org/users/documentation/}.} #' } #' @return An S4 shinystan object. #' #' @template seealso-launch #' @template seealso-as.shinystan #' #' @examples #' \dontrun{ #' # launch demo but don't save a shinystan object #' launch_shinystan_demo() #' #' # launch demo and save the shinystan object for the demo #' sso_demo <- launch_shinystan_demo() #' } #' launch_shinystan_demo <- function(demo_name = "eight_schools", rstudio = getOption("shinystan.rstudio"), ...) { demo_name <- match.arg(demo_name) data(list = demo_name, package = "shinystan", envir = environment()) invisible(launch(get(demo_name, inherits = FALSE), rstudio = rstudio, ...)) } # Internal launch function # @param sso shinystan object # @param rstudio launch in rstudio viewer instead of web browser? # @param ... passed to shiny::runApp launch <- function(sso, rstudio = FALSE, ...) { launch.browser <- if (!rstudio) TRUE else getOption("shiny.launch.browser", interactive()) .sso_env$.SHINYSTAN_OBJECT <- sso # see zzz.R for .sso_env on.exit(.sso_env$.SHINYSTAN_OBJECT <- NULL, add = TRUE) shiny::runApp(system.file("ShinyStan", package = "shinystan"), launch.browser = launch.browser, ...) } shinystan/R/misc.R0000644000176200001440000000411113044376075013557 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . # check which shinystan created a shinystan object sso_version <- function(sso) { ver <- sso@misc[["sso_version"]] if (!is.null(ver)) { package_version(ver) } else { package_version("2.0") } } # check object types sso_check <- function(sso) { if (!is.shinystan(sso)) { stop("Please specify a shinystan object.", call. = FALSE) } else if (sso_version(sso) < utils::packageVersion("shinystan")) { stop( "Your shinystan object was created with a previous version of shinystan. ", "Please use the 'update_sso' function to update your object.", call. = FALSE ) } invisible(TRUE) } is.stanfit <- function(x) inherits(x, "stanfit") is.stanreg <- function(x) inherits(x, "stanreg") # check for suggested (not required) packages check_suggests <- function(pkg) { if (!requireNamespace(pkg, quietly = TRUE)) stop( "You need to have the ", pkg, " package installed to use this option.", call. = FALSE ) } # grepl with ignore.case defaulting to TRUE grepl_ic <- function(pattern, x, ignore.case = TRUE) { grepl(pattern = pattern, x = x, ignore.case = ignore.case) } # nocov start # release reminders (for devtools) release_questions <- function() { c( "Have you updated version numbers in inst/CITATION?", "Have you updated version numbers in the citation on the About page?", "Have you updated version numbers in the citation on the Stan website?", "Have you updated NEWS.md?" ) } # nocov end shinystan/R/zzz.R0000644000176200001440000000205213035516434013456 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . .sso_env <- new.env(parent=emptyenv()) .onAttach <- function(...) { ver <- utils::packageVersion("shinystan") msg <- paste0("\nThis is shinystan version ", ver,"\n") packageStartupMessage(msg) } .onLoad <- function(libname, pkgname) { op <- options() op.shinystan <- list( shinystan.rstudio = FALSE ) set_ops <- !(names(op.shinystan) %in% names(op)) if (any(set_ops)) options(op.shinystan[set_ops]) invisible() } shinystan/R/deploy_shinystan.R0000644000176200001440000001472713140210431016212 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' Deploy a 'ShinyStan' app on the web using 'shinyapps.io' by 'RStudio' #' #' Requires a (free or paid) 'ShinyApps' account. Visit #' \url{http://www.shinyapps.io/} to sign up. #' #' @export #' @template args-sso #' @param appName The name to use for the application. Application names must be #' at least four characters long and may only contain letters, numbers, dashes #' and underscores. #' @param account shinyapps.io account username. Only required if more than one #' account is configured on the system. #' @param ... Optional arguments. See Details. #' @param deploy Should the app be deployed? The only reason for this to be #' \code{FALSE} is if you just want to check that the preprocessing before #' deployment is successful. #' #' @return \link[=invisible]{Invisibly}, \code{TRUE} if deployment succeeded #' (did not encounter an error) or, if \code{deploy} argument is set to #' \code{FALSE}, the path to the temporary directory containing the app ready #' for deployment (also invisibly). #' #' @details In \code{...}, the arguments \code{ppcheck_data} and #' \code{ppcheck_yrep} can be specified. \code{ppcheck_data} should be a #' vector of observations to use for graphical posterior predictive checking #' and \code{ppcheck_yrep} should be a character string naming the parameter #' in \code{sso} containing the posterior predictive simulations/replications. #' The value of \code{ppcheck_yrep} is only used to preselect the appropriate #' parameter/generated quantity to use for the posterior predictive checking. #' \code{ppcheck_yrep} (but not \code{ppcheck_data}) can also be set #' interactively on shinyapps.io when using the app. #' #' @seealso The example in the \emph{Deploying to shinyapps.io} vignette that #' comes with this package. #' #' \url{http://www.shinyapps.io/} to sign up for a free or paid 'ShinyApps' #' account and for details on how to configure your account on your local #' system using the \pkg{\link[rsconnect]{rsconnect}} package from 'RStudio'. #' #' @examples #' \dontrun{ #' # For this example assume sso is the name of the \code{shinystan} object for #' # the model you want to use. Assume also that you want to name your app #' # 'my-model' and that your shinyapps.io username is 'username'. #' #' deploy_shinystan(sso, appName = "my-model", account = "username") #' #' # If you only have one ShinyApps account configured then you can also omit #' # the 'account' argument. #' #' deploy_shinystan(sso, appName = "my-model") #' } #' #' @importFrom rsconnect deployApp #' deploy_shinystan <- function(sso, appName, account = NULL, ..., deploy = TRUE) { sso_check(sso) if (missing(appName)) stop("'appName' is required.") # copy contents to temporary directory and write necessary additional lines to # ui, server, and global appDir <- tempdir() deployDir <- file.path(appDir, "ShinyStan") contents <- system.file("ShinyStan", package = "shinystan") file.copy(from = contents, to = appDir, recursive = TRUE) server_pkgs <- c( "shiny", "shinyjs", "colourpicker", "markdown", "shinythemes" ) ui_pkgs <- c( server_pkgs, "ggplot2", "bayesplot", "gtools", "reshape2", "dygraphs", "xts", "xtable", "gridExtra", "DT", "threejs" ) server_lines <- paste0("library(", server_pkgs, ");") ui_lines <- paste0("library(", ui_pkgs, ");") global_lines <- paste( "load('sso.RData');", "if (file.exists('y.RData')) load('y.RData')" ) for (ff in c("ui", "server", "global")) { file_name <- file.path(deployDir, paste0(ff, ".R")) fconn <- file(file_name, 'r+') original_content <- readLines(fconn) new_lines <- get(paste0(ff, "_lines")) writeLines(c(new_lines, original_content), con = fconn) close(fconn) } # save sso to deployDir .SHINYSTAN_OBJECT <- sso save(.SHINYSTAN_OBJECT, file = file.path(deployDir, "sso.RData")) # save ppcheck_data and set ppcheck defaults pp <- list(...) if ("ppcheck_data" %in% names(pp)) { y <- pp$ppcheck_data save(y, file = file.path(deployDir, "y.RData")) if ("ppcheck_yrep" %in% names(pp)) set_ppcheck_defaults( appDir = deployDir, yrep_name = pp$ppcheck_yrep, y_name = "y" ) } if (!deploy) return(invisible(deployDir)) rsconnect::deployApp( appDir = deployDir, appName = appName, account = account, lint = TRUE ) } # functions to set defaults for ppcheck shiny::selectInput for y and y_rep set_ppcheck_defaults <- function(appDir, yrep_name, y_name = "y") { stopifnot(is.character(yrep_name), is.character(y_name), length(yrep_name) == 1, length(y_name) == 1) fileDir <- file.path(appDir, "server_files", "pages", "diagnose", "ppcheck", "ui") ppc_file <- file.path(fileDir, "pp_get_y_and_yrep.R") if (file.exists(ppc_file)) { file.remove(ppc_file) file.create(ppc_file) } .write_files(files = ppc_file, lines = .ppc_lines(y_name, yrep_name)) } .write_files <- function(files, lines) { stopifnot(length(files) == length(lines)) for (f in seq_along(files)) { fileConn <- file(files[f]) writeLines(lines[f], fileConn) close(fileConn) } } .ppc_lines <- function(y_name = "y", yrep_name) { paste0( "output$ui_pp_get_y <- renderUI({ choices <- objects(envir = .GlobalEnv) selectizeInput('y_name', label = span(style = 'color: #337ab7;', 'y, a vector of observations'), choices = c('', choices), selected = '", y_name,"') }) output$ui_pp_get_yrep <- renderUI({ choices <- PARAM_NAMES choices <- strsplit(choices, split = '[', fixed = TRUE) choices <- lapply(choices, function(i) return(i[1])) choices <- unique(unlist(choices)) selectizeInput('yrep_name', label = span(style = 'color: #337ab7;', 'y_rep, posterior predictive replications'), choices = c('', choices), selected = '", yrep_name,"') })" ) } shinystan/R/shinystan-package.R0000644000176200001440000001040213271435552016233 0ustar liggesusers# shinystan is free software; you can redistribute it and/or modify it under the # terms of the GNU General Public License as published by the Free Software # Foundation; either version 3 of the License, or (at your option) any later # version. # # shinystan is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR # A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with # this program; if not, see . #' \pkg{shinystan} R package ('ShinyStan' graphical user interface) #' #' @docType package #' @name shinystan-package #' #' @description #' \if{html}{ #' \figure{stanlogo.png}{options: width="50px" alt="mc-stan.org"} #' \emph{Stan Development Team} #' } #' #' Applied Bayesian data analysis is primarily implemented through the Markov #' chain Monte Carlo (MCMC) algorithms offered by various software packages. #' When analyzing a posterior sample obtained by one of these algorithms the #' first step is to check for signs that the chains have converged to the target #' distribution and and also for signs that the algorithm might require tuning #' or might be ill-suited for the given model. There may also be theoretical #' problems or practical inefficiencies with the specification of the model. The #' ShinyStan app provides interactive plots and tables helpful for analyzing a #' posterior sample, with particular attention to identifying potential problems #' with the performance of the MCMC algorithm or the specification of the model. #' ShinyStan is powered by the Shiny web application framework by RStudio #' (\url{http://shiny.rstudio.com/}) and works with the output of MCMC programs #' written in any programming language (and has extended functionality for #' models fit using the \pkg{rstan} package and the No-U-Turn sampler). #' #' @section ShinyStan has extended functionality for Stan models: #' #' Stan (\url{http://mc-stan.org}) models can be run in \R using the #' \pkg{\link[rstan]{rstan}} package. Other packages like #' \pkg{\link[rstanarm]{rstanarm}} and \pkg{\link[brms]{brms}} provide #' higher-level interfaces to Stan that use \pkg{rstan} internally. #' #' @section Saving and sharing: #' #' The \pkg{shinystan} package allows you to store the basic components of an #' entire project (code, posterior samples, graphs, tables, notes) in a single #' object, a \code{\link[=as.shinystan]{shinystan object}} (sso, for short). #' Users can save many of the plots as \pkg{ggplot2} objects for further #' customization and easy integration in reports or post-processing for #' publication. #' #' The \code{\link{deploy_shinystan}} function lets you easily deploy your own #' ShinyStan apps online for any of your models using the shinyapps.io #' service from 'RStudio'. Each of your apps (each of your models) will have a #' unique url and will be compatible with most web browsers. #' #' @section License: #' #' The \pkg{shinystan} package is open source licensed under the GNU Public #' License, version 3 (GPLv3). #' #' @section Demo: #' #' Check out the demo using \code{\link{launch_shinystan_demo}} or try it with #' one of your own models using \code{\link{launch_shinystan}}. #' #' @section Resources: #' \itemize{ #' \item Web page with online documentation #' (\url{http://mc-stan.org/shinystan}) #' \item Stan Forums on Discourse (\url{http://discourse.mc-stan.org}) #' \item GitHub issue tracker (\url{https://github.com/stan-dev/shinystan/issues}) #' } #' #' @template seealso-as.shinystan #' @template seealso-demo #' @template seealso-launch #' #' @template reference-muth #' @template reference-bayesvis #' #' @import ggplot2 #' @import methods #' @import shiny #' @import dygraphs #' @import bayesplot #' @import threejs #' @import utils #' @import xts #' @importFrom stats acf quantile time var na.omit #' @importFrom shinythemes shinytheme #' @importFrom colourpicker colourInput #' @importFrom shinyjs useShinyjs #' @importFrom DT datatable #' @importFrom gridExtra arrangeGrob grid.arrange #' @importFrom gtools mixedsort #' @importFrom markdown markdownToHTML #' @importFrom reshape2 melt #' @importFrom xtable xtable print.xtable #' NULL shinystan/vignettes/0000755000176200001440000000000013271714530014305 5ustar liggesusersshinystan/vignettes/deploy_shinystan.Rmd0000644000176200001440000000671413035516434020356 0ustar liggesusers--- title: 'Deploying to shinyapps.io' date: "09/17/2015" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{ShinyStan: Deploying to shinyapps.io} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- Create a ShinyStan app unique to your model and host it online with RStudio's ShinyApps service (shinyapps.io). Each app you deploy will have a unique url. *Note: R users who don't use RStudio's IDE can still deploy ShinyStan apps to shinyapps.io.* ## Step 1: ShinyApps account **Signup** To deploy your app to RStudio's shinyapps.io you will need a ShinyApps account. If you don't already have one you can sign up at http://www.shinyapps.io/. The only limit to the number of different ShinyStan apps you can deploy is the limit set by RStudio for the [type of ShinyApps account](https://www.shinyapps.io/#pricing) you sign up for. **Setup** When you finish signing up for your ShinyApps account there will be instructions for setting up your account on your local system using `rsconnect::setAccountInfo`. If you have the most recent version of **shinystan** then you will already have **rsconnect** installed and you can call ```r rsconnect::setAccountInfo(name, token, secret) ``` where `name` is your ShinyApps account name, and `token` and `secret` can be found from your ShinyApps account web page. ## Step 2: Use `deploy_shinystan` to deploy your app to shinyapps.io The `deploy_shinystan` function will deploy a ShinyStan app unique to your model to RStudio's ShinyApps service. For the example below assume that - `my_sso` is the shinystan object you want to use - the name you want to use for the app is `MyModel` - the username for your ShinyApps account (from Step 1) is `username`. To deploy the app use the command ```r deploy_shinystan(my_sso, appName = "MyModel", account = "username") ``` **When to specify the `account` argument** If you have multiple ShinyApps accounts configured on your local system then the `account` argument is required. If you only have a single account then you can omit `account` ```r deploy_shinystan(my_sso, appName = "MyModel") ``` **Graphical posterior predictive checks: the optional `ppcheck_data` and `ppcheck_yrep` arguments** If you have a vector of observations `y` that you want to use for ShinyStan's graphical posterior predictive checks then you can also specify the optional `ppcheck_data` argument ```r deploy_shinystan(my_sso, appName = "MyModel", ppcheck_data = y) ``` If you've specified the `ppcheck_data` argument you can also use the `ppcheck_yrep` argument to tell ShinyStan the name of the parameter/quantity in your model containing the posterior predictive simulations/replications so that it is preselected as the parameter to use for doing the graphical posterior predictive checks. This is never required because you can also set this manually while using the app, but it is helpful if you want other people to view your model online because it avoids the need to communicate to them which parameter they need to select. If this parameter/quantity containing the posterior predictive replications is named `yRep` then you would use ```r deploy_shinystan(my_sso, appName = "MyModel", ppcheck_data = y, ppcheck_yrep = "yRep") ``` If the deployment process is successful the url for your app will be printed to the console and it should open in your web browser. You can also view your app by going to http://www.shinyapps.io and logging into your account. shinystan/vignettes/shinystan-package.Rmd0000644000176200001440000001265613271713655020403 0ustar liggesusers--- title: 'Getting Started' date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true vignette: > %\VignetteIndexEntry{Getting Started} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Using the ShinyStan app with different types of objects ## stanfit objects If `my_stanfit` is a stanfit object (the result of fitting a model with [**rstan**](http://mc-stan.org/)), then to launch the ShinyStan app simply use ```r library(shinystan) my_sso <- launch_shinystan(my_stanfit) ``` and ShinyStan will launch. Here `my_sso` is the name you want to use for the shinystan object that will be returned. If you simply run ```r launch_shinystan(my_stanfit) ``` then ShinyStan will launch but no shinystan object will be saved upon quitting the app. Sometimes keeping only a subset of parameters before can improve performance. This can be done by creating an object with `as.shinystan` and specifying the `pars` argument. The resulting shinystan object can then be passed to `launch_shinystan()`. ### stanreg and brmsfit objects The **rstanarm** and **brms** packages provide `launch_shinystan` methods for stanreg and brmsfit objects, respectively. For example, the method for stanreg objects is documented at http://mc-stan.org/rstanarm/reference/launch_shinystan.stanreg.html ## mcmc.list objects If you have an `mcmc.list` object called `my_mcmc` then you can use the `as.shinystan` function to convert `my_mcmc` into a shinystan object that can then be used with `launch_shinystan`: ```r my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model")) ``` If, for example, the first 100 iterations in each chain in `my_mcmc` are warmup iterations, you should add the `warmup` argument when you call `as.shinystan`: ```r my_sso <- launch_shinystan(as.shinystan(my_mcmc, model_name = "my_model", warmup = 100)) ``` However, you should only use the `warmup` argument if the warmup iterations have been saved and included in `my_mcmc`. ## Other types of objects ### 3-D array To convert a 3-D array to a shinystan object make sure that the three dimensions of the array correspond to the *number of iterations* **x** *number of chains* **x** *number of parameters*. You can then `as.shinystan` exactly how it's used in the examples for `mcmc.list` objects above (but you don't need to convert your array to an `mcmc.list`). ### List of matrices If you have separate chains that are each a matrix (with iterations as rows and parameters as columns) you can combine them in a list to pass to `as.shinystan` ```r # Generate some fake data chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) chain_list <- list(chain1, chain2) my_sso <- launch_shinystan(as.shinystan(X = list(chain1, chain2), model_name = "my_model")) ``` ****** # Other functions in the shinystan package ## Generating new quantities You can add a new parameter/quantity as a function of one or two existing parameters to your shinystan object by using the `generate_quantity` function. For example, assume `sso` is a shinystan object and two of the parameters are ```alpha``` and ```beta```. We could add a parameter ```gamma``` that is the inverse logit of ```beta``` using the code ```r inv_logit <- function(x) 1/(1 + exp(-x)) sso <- generate_quantity(sso, fun = inv_logit, param1 = "beta", new_name = "gamma") ``` Here, `fun` is the function we want to use, `param1` is the name of the parameter to apply the function to, and `new_name` is the name to give the new parameter. Adding a parameter as a function of two parameters just requires specifying the `param2` argument and providing a function of two variables. For example, we can add a parameter `delta` to `sso` that is the squared difference of `alpha` and `beta` like this ```r sso <- generate_quantity(sso, fun = function(x,y) (x-y)^2, param1 = "alpha", param2 = "beta", new_name = "delta") ``` ## Storing your model code in a shinystan object For models fit using **rstan** the model code will automatically be stored in the `model_code` slot of your shinystan object. When ShinyStan is open you can view your model code in the *Model Code* tab. If you did not use **rstan** fit your model then you can add your model code by using the `model_code()` function. For example, you may have used Bugs or JAGS or some other software and want to add the following code ``` for (i in 1:length(Y)) { Y[i] ~ dpois(lambda[i]) log(lambda[i]) <- inprod(X[i,], theta[]) } for (j in 1:J) { theta[j] ~ dt(0.0, 1.0, 1.0) } } ``` to your shinystan object. To add that code you can simply include it as the `code` argument to the `model_code` function ```r my_code <- " model { for (i in 1:length(Y)) { Y[i] ~ dpois(lambda[i]) log(lambda[i]) <- inprod(X[i,], theta[]) } for (j in 1:J) { theta[j] ~ dt(0.0, 1.0, 1.0) } } " # Add the code to a shinystan object sso sso <- model_code(sso, code = my_code) ``` ## Renaming a model On the home page ShinyStan will display the model name associated with the shinystan object being used. 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person("Yuanjun", "Gao", role = "ctb"), person("Andrew", "Gelman", role = "ctb"), person("Ben", "Goodrich", role = "ctb"), person("Daniel", "Lee", role = "ctb"), person("Dongying", "Song", role = "ctb"), person("Rob", "Trangucci", role = "ctb")) Maintainer: Jonah Gabry Description: A graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the 'Shiny' web application framework from 'RStudio' and works with the output of MCMC programs written in any programming language (and has extended functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' packages). URL: http://mc-stan.org/, http://discourse.mc-stan.org BugReports: https://github.com/stan-dev/shinystan/issues/ License: GPL (>= 3) Depends: R (>= 3.1.0), shiny (>= 1.0.3) Imports: bayesplot (>= 1.5.0), colourpicker, DT (>= 0.2), dygraphs (>= 1.1.1.2), ggplot2 (>= 2.1.1), gridExtra, gtools, markdown (>= 0.7.4), methods, reshape2, rsconnect (>= 0.4.2), rstan (>= 2.17.1), stats, shinyjs (>= 0.6.0), shinythemes (>= 1.0.1), threejs (>= 0.2.1), utils, xtable, xts (>= 0.9-7) Suggests: coda, knitr (>= 1.9), rmarkdown (>= 0.8.1), rstanarm (>= 2.17.4), testthat LazyData: true VignetteBuilder: knitr RoxygenNote: 6.0.1 NeedsCompilation: no Packaged: 2018-04-30 22:31:52 UTC; jgabry Author: Jonah Gabry [aut, cre], Stan Development Team [ctb], Michael Andreae [ctb], Michael Betancourt [ctb], Bob Carpenter [ctb], Yuanjun Gao [ctb], Andrew Gelman [ctb], Ben Goodrich [ctb], Daniel Lee [ctb], Dongying Song [ctb], Rob Trangucci [ctb] Repository: CRAN Date/Publication: 2018-05-01 03:31:02 UTC 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Vf>?+}piܱ++G0YS 4^~әnD r{gC8F05M`U~k'yZ&>00<)Ky#bڴkKup\!S9 Hq;<,VfV14wC8y +Vp{.fzUU ޠ*X|X5j9W/ׯyAEy qK鱽oA )9 3"2p_}x}qOq$sCuO+o}LJNDÖ+w'F CdFg98ɶ)fAy6K|Yk++G 7*=`GPy7/z\lerj5*0MHLf|gˣx^yOͦe 9pQEeˍ#N ԝae4`F,\|Y[jd (0O1.`+dUׯW%z<{5wzΗs6lS]os$7^%LI4!@&&1F(:BHō t3f 3c}f]yJX9Q4Gn@~NERCI;D8'WݴsByK)j0~D%P! d޹EQpm,Fi*RڂX U`$j2G1l?Πɡ"^@p_zNƙa$FF:u5ȏ2IENDB`shinystan/man/as.shinystan.Rd0000644000176200001440000001671113140211700015751 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinystan-objects.R \docType{methods} \name{as.shinystan} \alias{as.shinystan} \alias{is.shinystan} \alias{as.shinystan,array-method} \alias{as.shinystan,list-method} \alias{as.shinystan,mcmc.list-method} \alias{as.shinystan,stanfit-method} \alias{as.shinystan,stanreg-method} \title{Create and test \code{shinystan} objects} \usage{ as.shinystan(X, ...) is.shinystan(X) \S4method{as.shinystan}{array}(X, model_name = "unnamed model", warmup = 0, burnin = 0, param_dims = list(), model_code = NULL, note = NULL, sampler_params = NULL, algorithm = NULL, max_treedepth = NULL, ...) \S4method{as.shinystan}{list}(X, model_name = "unnamed model", warmup = 0, burnin = 0, param_dims = list(), model_code = NULL, note = NULL, sampler_params = NULL, algorithm = NULL, max_treedepth = NULL, ...) \S4method{as.shinystan}{mcmc.list}(X, model_name = "unnamed model", warmup = 0, burnin = 0, param_dims = list(), model_code = NULL, note = NULL, ...) \S4method{as.shinystan}{stanfit}(X, pars, model_name = X@model_name, note = NULL, ...) \S4method{as.shinystan}{stanreg}(X, ppd = TRUE, seed = 1234, model_name = NULL, note = NULL, ...) } \arguments{ \item{X}{For \code{as.shinystan}, an object to be converted to a \code{shinystan} object. See the Methods section below. For \code{is.shinystan}, an object to check.} \item{...}{Arguments passed to the individual methods.} \item{model_name}{A string giving a name for the model.} \item{warmup}{The number of iterations to treat as warmup. Should be \code{0} if warmup iterations are not included in \code{X}.} \item{burnin}{Deprecated. Use \code{warmup} instead. The \code{burnin} argument will be removed in a future release.} \item{param_dims}{Rarely used and never necessary. A named list giving the dimensions for all parameters. For scalar parameters use \code{0} as the dimension. See Examples.} \item{model_code}{Optionally, a character string with the code used to run the model. This can also be added to your \code{shinystan} object later using the \code{\link[shinystan]{model_code}} function. See \code{\link[shinystan]{model_code}} for additional formatting instructions. After launching the app the code will be viewable in the \strong{Model Code} tab. For \code{stanfit} (\pkg{rstan}) and \code{stanreg} (\pkg{rstanarm}) objects the model code is automatically retrieved from the object.} \item{note}{Optionally, text to display on the \strong{Notepad} page in the 'ShinyStan' GUI (stored in \code{user_model_info} slot of the \code{shinystan} object).} \item{sampler_params, algorithm, max_treedepth}{Rarely used and never necessary. If using the \code{as.shinystan} method for arrays or lists, these arguments can be used to manually provide information that is automatically retrieved from a stanfit object when using the \code{as.shinystan} method for stanfit objects. If specified, \code{sampler_params} must have the same structure as an object returned by \code{\link[rstan]{get_sampler_params}} (\pkg{rstan}), which is a list of matrices, with one matrix per chain. \code{algorithm}, if specified, must be either \code{"NUTS"} or \code{"HMC"} (static HMC). If \code{algorithm} is \code{"NUTS"} then \code{max_treedepth} (an integer indicating the maximum allowed treedepth when the model was fit) must also be provided.} \item{pars}{For stanfit objects (\pkg{rstan}), an optional character vector specifying which parameters should be included in the \code{shinystan} object.} \item{ppd}{For \code{stanreg} objects (\pkg{rstanarm}), \code{ppd} (logical) indicates whether to draw from the posterior predictive distribution before launching the app. The default is \code{TRUE}, although for very large objects it can be convenient to set it to \code{FALSE} as drawing from the posterior predictive distribution can be time consuming. If \code{ppd} is \code{TRUE} then graphical posterior predictive checks are available when 'ShinyStan' is launched.} \item{seed}{Passed to \code{\link[rstanarm]{pp_check}} (\pkg{rstanarm}) if \code{ppd} is \code{TRUE}.} } \value{ \code{as.shinystan} returns a \code{shinystan} object, which is an instance of S4 class \code{"shinystan"}. \code{is.shinystan} returns \code{TRUE} if the tested object is a \code{shinystan} object and \code{FALSE} otherwise. } \description{ The \code{as.shinystan} function creates \code{shinystan} objects that can be used with \code{\link{launch_shinystan}} and various other functions in the \pkg{shinystan} package. \code{as.shinystan} is a generic for which the \pkg{shinystan} package provides several methods. Currently methods are provided for creating \code{shinystan} objects from arrays, lists of matrices, \code{stanfit} objects (\pkg{rstan}), \code{stanreg} objects (\pkg{rstanarm}), and \code{mcmc.list} objects (\pkg{coda}). \code{is.shinystan} tests if an object is a \code{shinystan} object. } \section{Methods (by class)}{ \itemize{ \item \code{array}: Create a \code{shinystan} object from a 3-D \code{\link{array}} of simulations. The array should have dimensions corresponding to iterations, chains, and parameters, in that order. \item \code{list}: Create a \code{shinystan} object from a \code{\link{list}} of matrices. Each \code{\link{matrix}} (or 2-D array) should contain the simulations for an individual chain and all of the matrices should have the same number of iterations (rows) and parameters (columns). Parameters should have the same names and be in the same order. \item \code{mcmc.list}: Create a \code{shinystan} object from an \code{mcmc.list} object (\pkg{coda}). \item \code{stanfit}: Create a \code{shinystan} object from a \code{stanfit} object (\pkg{\link[rstan]{rstan}}). Fewer optional arguments are available for this method because all important information can be taken automatically from the \code{stanfit} object. \item \code{stanreg}: Create a \code{shinystan} object from a \code{stanreg} object (\pkg{\link[rstanarm]{rstanarm}}). }} \examples{ \dontrun{ sso <- as.shinystan(X, ...) # replace ... with optional arguments or omit it launch_shinystan(sso) } \dontrun{ ######################## ### list of matrices ### ######################## # Generate some fake data chain1 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) chain2 <- cbind(beta1 = rnorm(100), beta2 = rnorm(100), sigma = rexp(100)) sso <- as.shinystan(list(chain1, chain2)) launch_shinystan(sso) # We can also specify some or all of the optional arguments # note: in order to use param_dims we need to rename 'beta1' and 'beta2' # to 'beta[1]' and 'beta[2]' colnames(chain1) <- colnames(chain2) <- c(paste0("beta[",1:2,"]"), "sigma") sso2 <- as.shinystan(list(chain1, chain2), model_name = "Example", warmup = 0, param_dims = list(beta = 2, sigma = 0)) launch_shinystan(sso2) } \dontrun{ ###################### ### stanfit object ### ###################### library("rstan") fit <- stan_demo("eight_schools") sso <- as.shinystan(fit, model_name = "example") } \dontrun{ ###################### ### stanreg object ### ###################### library("rstanarm") example("example_model") sso <- as.shinystan(example_model) launch_shinystan(sso) } } \seealso{ \code{\link{launch_shinystan}} to launch the 'ShinyStan' interface using a particular \code{shinystan} object. \code{\link{drop_parameters}} to remove parameters from a \code{shinystan} object. \code{\link{generate_quantity}} to add a new quantity to a \code{shinystan} object. } shinystan/man/launch_shinystan.Rd0000644000176200001440000000635613271435552016727 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/launch_shinystan.R \name{launch_shinystan} \alias{launch_shinystan} \alias{launch_shinystan.default} \alias{launch_shinystan.shinystan} \title{Launch the 'ShinyStan' app} \usage{ launch_shinystan(object, ...) \method{launch_shinystan}{default}(object, ..., rstudio = getOption("shinystan.rstudio")) \method{launch_shinystan}{shinystan}(object, ..., rstudio = getOption("shinystan.rstudio")) } \arguments{ \item{object}{The object to use. For the default method this can be an object of class \code{"shinystan"}, \code{"stanfit"}, or \code{"stanreg"}. To use other types of objects first create a shinystan object using \code{\link{as.shinystan}}.} \item{...}{Optional arguments passed to \code{\link[shiny]{runApp}}.} \item{rstudio}{Only relevant for 'RStudio' users. The default (\code{FALSE}) is to launch the app in the user's default web browser rather than the pop-up Viewer provided by 'RStudio'. Users can change the default to \code{TRUE} by setting the global option \code{options(shinystan.rstudio = TRUE)}.} } \value{ The \code{launch_shinystan} function is used for the side effect of starting the 'ShinyStan' app, but it also returns a \code{shinystan} object, an instance of S4 class \code{"shinystan"}. } \description{ Launch the 'ShinyStan' app in the default web browser. 'RStudio' users also have the option of launching the app in the pop-up Viewer. } \examples{ \dontrun{ ####################################### # Example 1: 'sso' is a shinystan object ####################################### # Just launch shinystan launch_shinystan(sso) # Launch shinystan and replace sso with an updated version of itself # if any changes are made to sso while using the app sso <- launch_shinystan(sso) # Launch shinystan but save any changes made to sso while running the app # in a new shinystan object sso2. sso will remained unchanged. sso2 <- launch_shinystan(sso) ####################################### # Example 2: 'sf' is a stanfit object ####################################### # Just launch shinystan launch_shinystan(sf) # Launch shinystan and save the resulting shinystan object sf_sso <- launch_shinystan(sf) # Now sf_sso is a shinystan object and so Example 1 (above) applies when # using sf_sso. ####################################### # Example 3: 'fit' is an mcmc.list, array or list of matrices ####################################### # First create shinystan object (see ?as.shinystan) for full details) } } \references{ Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. \emph{The Quantitative Methods for Psychology}. 14(2), 99--119. \url{https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf} Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2018). Visualization in Bayesian workflow. \emph{Journal of the Royal Statistical Society Series A}, accepted for publication. arXiv preprint: \url{http://arxiv.org/abs/1709.01449}. } \seealso{ \code{\link{as.shinystan}} for creating \code{shinystan} objects. \code{\link{update_sso}} to update a \code{shinystan} object created by a previous version of the package. \code{\link{launch_shinystan_demo}} to try a demo. } shinystan/man/update_sso.Rd0000644000176200001440000000215413140211435015476 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/update_sso.R \name{update_sso} \alias{update_sso} \title{Update an object created by the previous version of shinystan} \usage{ update_sso(sso) } \arguments{ \item{sso}{A \code{\link[=as.shinystan]{shinystan object}}.} } \value{ If \code{sso} is already compatible with your version of \pkg{shinystan} then \code{sso} itself is returned and a message is printed indicating that \code{sso} is already up-to-date. Otherwise an updated version of \code{sso} is returned unless an error is encountered. } \description{ If you encounter any errors when using a shinystan object (\code{sso}) created by a previous version of \pkg{shinystan}, you might need to run \code{update_sso}. If \code{update_sso} does not resolve the problem and you still have the object (e.g. stanfit, stanreg, mcmc.list) from which \code{sso} was originally created, you can create a new shinystan object using \code{\link{as.shinystan}}. } \examples{ \dontrun{ sso_new <- update_sso(sso) } } \seealso{ \code{\link{as.shinystan}} for creating \code{shinystan} objects. } shinystan/man/launch_shinystan_demo.Rd0000644000176200001440000000272013140211435017705 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/launch_shinystan.R \name{launch_shinystan_demo} \alias{launch_shinystan_demo} \alias{eight_schools} \title{'ShinyStan' demo} \usage{ launch_shinystan_demo(demo_name = "eight_schools", rstudio = getOption("shinystan.rstudio"), ...) } \arguments{ \item{demo_name}{The name of the demo. Currently \code{"eight_schools"} is the only option, but additional demos may be available in future releases. \describe{ \item{\code{eight_schools}}{Hierarchical meta-analysis model. See \emph{Meta Analysis} chapter of the 'Stan' manual \url{http://mc-stan.org/users/documentation/}.} }} \item{rstudio}{Only relevant for 'RStudio' users. The default (\code{FALSE}) is to launch the app in the user's default web browser rather than the pop-up Viewer provided by 'RStudio'. Users can change the default to \code{TRUE} by setting the global option \code{options(shinystan.rstudio = TRUE)}.} \item{...}{Optional arguments passed to \code{\link[shiny]{runApp}}.} } \value{ An S4 shinystan object. } \description{ 'ShinyStan' demo } \examples{ \dontrun{ # launch demo but don't save a shinystan object launch_shinystan_demo() # launch demo and save the shinystan object for the demo sso_demo <- launch_shinystan_demo() } } \seealso{ \code{\link{launch_shinystan}} to launch the 'ShinyStan' interface using a particular \code{shinystan} object. \code{\link{as.shinystan}} for creating \code{shinystan} objects. } shinystan/man/retrieve.Rd0000644000176200001440000000566113140207155015167 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/retrieve.R \name{retrieve} \alias{retrieve} \title{Get summary statistics from shinystan object} \usage{ retrieve(sso, what, ...) } \arguments{ \item{sso}{A \code{\link[=as.shinystan]{shinystan object}}.} \item{what}{What do you want to get? See Details, below.} \item{...}{Optional arguments, in particular \code{pars} to specify parameter names (by default all parameters will be used). For NUTS sampler parameters only (e.g. stepsize, treedepth) \code{inc_warmup} can also be specified to include/exclude warmup iterations (the default is \code{FALSE}). See Details, below.} } \description{ From a shinystan object get rhat, effective sample size, posterior quantiles, means, standard deviations, sampler diagnostics, etc. } \details{ The argument \code{what} can take on the values below. 'Args: \code{arg}' means that \code{arg} can be specified in \code{...} for this value of \code{what}. \describe{ \item{\code{"rhat"}, \code{"Rhat"}, \code{"r_hat"}, or \code{"R_hat"}}{returns: Rhat statistics. Args: \code{pars}} \item{\code{"N_eff"}, \code{"n_eff"}, \code{"neff"}, \code{"Neff"}, \code{"ess"}, or \code{"ESS"}}{returns: Effective sample sizes. Args: \code{pars}} \item{\code{"mean"}}{returns: Posterior means. Args: \code{pars}} \item{\code{"sd"}}{returns: Posterior standard deviations. Args: \code{pars}} \item{\code{"se_mean"} or \code{"mcse"}}{returns: Monte Carlo standard error. Args: \code{pars}} \item{\code{"median"}}{returns: Posterior medians. Args: \code{pars}.} \item{\code{"quantiles"} or any string with \code{"quant"} in it (not case sensitive)}{returns: 2.5\%, 25\%, 50\%, 75\%, 97.5\% posterior quantiles. Args: \code{pars}.} \item{\code{"avg_accept_stat"} or any string with \code{"accept"} in it (not case sensitive)}{returns: Average value of "accept_stat" (which itself is the average acceptance probability over the NUTS subtree). Args: \code{inc_warmup}} \item{\code{"prop_divergent"} or any string with \code{"diverg"} in it (not case sensitive)}{returns: Proportion of divergent iterations for each chain. Args: \code{inc_warmup}} \item{\code{"max_treedepth"} or any string with \code{"tree"} or \code{"depth"} in it (not case sensitive)}{returns: Maximum treedepth for each chain. Args: \code{inc_warmup}} \item{\code{"avg_stepsize"} or any string with \code{"step"} in it (not case sensitive)}{returns: Average stepsize for each chain. Args: \code{inc_warmup}} } } \note{ Sampler diagnostics (e.g. \code{"avg_accept_stat"}) only available for models originally fit using Stan. } \examples{ # Using example shinystan object 'eight_schools' sso <- eight_schools retrieve(sso, "rhat") retrieve(sso, "mean", pars = c('theta[1]', 'mu')) retrieve(sso, "quantiles") retrieve(sso, "max_treedepth") # equivalent to retrieve(sso, "depth"), retrieve(sso, "tree"), etc. retrieve(sso, "prop_divergent") retrieve(sso, "prop_divergent", inc_warmup = TRUE) } shinystan/man/rename_model.Rd0000644000176200001440000000072413136203136015764 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sso-metadata.R \name{rename_model} \alias{rename_model} \title{rename_model (deprecated)} \usage{ rename_model(sso, new_model_name) } \arguments{ \item{sso, new_model_name}{Use the \code{\link{model_name}} function instead.} } \description{ This function is deprecated and will be removed in a future release. Please use the \code{\link{model_name}} function instead. } \keyword{internal} shinystan/man/drop_parameters.Rd0000644000176200001440000000267313140211700016520 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/drop_parameters.R \name{drop_parameters} \alias{drop_parameters} \title{Drop parameters from a \code{shinystan} object} \usage{ drop_parameters(sso, pars) } \arguments{ \item{sso}{A \code{\link[=as.shinystan]{shinystan object}}.} \item{pars}{A character vector of parameter names. If the name of a non-scalar (e.g. vector, matrix) parameter is included in \code{pars} all of its elements will be removed. Currently it is not possible to remove only a subset of the elements of a non-scalar parameter.} } \value{ \code{sso}, with \code{pars} dropped. } \description{ Remove selected parameters from a \code{shinystan} object. This is useful if you have a very large \code{shinystan} object when you only want to look at a subset of parameters. With a smaller \code{shinystan} object, \code{\link{launch_shinystan}} will be faster and you should experience better performance (responsiveness) after launching when using the 'ShinyStan' app. } \examples{ # Using example shinystan object 'eight_schools' print(eight_schools@param_names) # Remove the scalar parameters mu and tau sso <- drop_parameters(eight_schools, pars = c("mu", "tau")) print(sso@param_names) # Remove all elements of the parameter vector theta sso <- drop_parameters(sso, pars = "theta") print(sso@param_names) } \seealso{ \code{\link{generate_quantity}} to add a new quantity to a \code{shinystan} object. } shinystan/man/deploy_shinystan.Rd0000644000176200001440000000517113140211331016721 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deploy_shinystan.R \name{deploy_shinystan} \alias{deploy_shinystan} \title{Deploy a 'ShinyStan' app on the web using 'shinyapps.io' by 'RStudio'} \usage{ deploy_shinystan(sso, appName, account = NULL, ..., deploy = TRUE) } \arguments{ \item{sso}{A \code{\link[=as.shinystan]{shinystan object}}.} \item{appName}{The name to use for the application. Application names must be at least four characters long and may only contain letters, numbers, dashes and underscores.} \item{account}{shinyapps.io account username. Only required if more than one account is configured on the system.} \item{...}{Optional arguments. See Details.} \item{deploy}{Should the app be deployed? The only reason for this to be \code{FALSE} is if you just want to check that the preprocessing before deployment is successful.} } \value{ \link[=invisible]{Invisibly}, \code{TRUE} if deployment succeeded (did not encounter an error) or, if \code{deploy} argument is set to \code{FALSE}, the path to the temporary directory containing the app ready for deployment (also invisibly). } \description{ Requires a (free or paid) 'ShinyApps' account. Visit \url{http://www.shinyapps.io/} to sign up. } \details{ In \code{...}, the arguments \code{ppcheck_data} and \code{ppcheck_yrep} can be specified. \code{ppcheck_data} should be a vector of observations to use for graphical posterior predictive checking and \code{ppcheck_yrep} should be a character string naming the parameter in \code{sso} containing the posterior predictive simulations/replications. The value of \code{ppcheck_yrep} is only used to preselect the appropriate parameter/generated quantity to use for the posterior predictive checking. \code{ppcheck_yrep} (but not \code{ppcheck_data}) can also be set interactively on shinyapps.io when using the app. } \examples{ \dontrun{ # For this example assume sso is the name of the \\code{shinystan} object for # the model you want to use. Assume also that you want to name your app # 'my-model' and that your shinyapps.io username is 'username'. deploy_shinystan(sso, appName = "my-model", account = "username") # If you only have one ShinyApps account configured then you can also omit # the 'account' argument. deploy_shinystan(sso, appName = "my-model") } } \seealso{ The example in the \emph{Deploying to shinyapps.io} vignette that comes with this package. \url{http://www.shinyapps.io/} to sign up for a free or paid 'ShinyApps' account and for details on how to configure your account on your local system using the \pkg{\link[rsconnect]{rsconnect}} package from 'RStudio'. } shinystan/man/shinystan-class.Rd0000644000176200001440000000373413271435552016475 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinystan-objects.R \docType{class} \name{shinystan-class} \alias{shinystan-class} \alias{shinystan} \title{S4 \code{shinystan} objects} \description{ See \code{\link{as.shinystan}} for documentation on creating \code{shinystan} objects and \code{\link{eight_schools}} for an example object. } \section{Slots}{ \describe{ \item{\code{model_name}}{(\code{"character"}) Model name.} \item{\code{param_names}}{(\code{"character"}) Parameter names.} \item{\code{param_dims}}{(\code{"list"}) Parameter dimensions.} \item{\code{posterior_sample}}{(\code{"array"}) MCMC sample.} \item{\code{summary}}{(\code{"matrix"}) Summary stats for \code{posterior_sample}.} \item{\code{sampler_params}}{(\code{"list"}) Sampler parameters (for certain Stan models only).} \item{\code{n_chain}}{(\code{"integer"}) Number of chains.} \item{\code{n_iter}}{(\code{"integer"}) Number of iterations per chain.} \item{\code{n_warmup}}{(\code{"integer"}) Number of warmup iterations per chain.} \item{\code{user_model_info}}{(\code{"character"}) Notes to display on the \strong{Notepad} page in the 'ShinyStan' GUI.} \item{\code{model_code}}{(\code{"character"}) Model code to display on the \strong{Model Code} page in the 'ShinyStan' GUI.} \item{\code{misc}}{(\code{"list"}) Miscellaneous, for internal use.} }} \references{ Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. \emph{The Quantitative Methods for Psychology}. 14(2), 99--119. \url{https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf} } \seealso{ \code{\link{as.shinystan}} for creating \code{shinystan} objects. \code{\link{drop_parameters}} to remove parameters from a \code{shinystan} object. \code{\link{generate_quantity}} to add a new quantity to a \code{shinystan} object. \code{\link{shinystan-metadata}} to view or change metadata associated with a \code{shinystan} object. } shinystan/man/shinystan-metadata.Rd0000644000176200001440000000760413140211700017126 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sso-metadata.R \name{shinystan-metadata} \alias{shinystan-metadata} \alias{sso_info} \alias{model_code} \alias{notes} \alias{model_name} \title{View or change metadata associated with a \code{shinystan} object} \usage{ sso_info(sso) model_code(sso, code = NULL) notes(sso, note = NULL, replace = FALSE) model_name(sso, name = NULL) } \arguments{ \item{sso}{A \code{\link[=as.shinystan]{shinystan object}}.} \item{code}{A string, containing model code to be added, that can be used as an argument to \code{\link{cat}}. See \strong{Examples}.} \item{note}{A string containing a note to add to any existing notes or replace existing notes, depending on the value of \code{replace}.} \item{replace}{If \code{TRUE} the existing notes are overwritten by \code{note} if \code{note} is specified. If \code{FALSE} (the default) if \code{note} is specified then its content is appended to the existing notes.} \item{name}{A string giving the new model name to use.} } \value{ \code{sso_info} prints basic metadata including number of parameters, chains, iterations, warmup iterations, etc. It does not return anything. \code{model_code} returns or replaces model code stored in a \code{shinystan} object. If \code{code} is \code{NULL} then any existing model code stored in \code{sso} is returned as a character string. If \code{code} is specified then an updated \code{shinystan} object is returned with \code{code} added. For \code{shinystan} objects created from stanfit (\pkg{rstan}) and stanreg (\pkg{rstanarm}) objects, model code is automatically taken from that object and does not need to be added manually. From within the 'ShinyStan' interface model code can be viewed on the \strong{Model Code} page. \code{notes} returns, amends, or replaces notes stored in a \code{shinystan} object. If \code{note} is \code{NULL} then any existing notes stored in \code{sso} are returned as a character string. If \code{note} is specified then an updated \code{shinystan} object is returned with either \code{note} added to the previous notes (if \code{replace=FALSE}) or overwritten by \code{note} (if \code{replace = TRUE}). From within the 'ShinyStan' interface, notes are viewable on the \strong{Notepad} page. \code{model_name} returns or replaces the model name associated with a \code{shinystan} object. If \code{name} is \code{NULL} then the current model name is returned. If \code{name} is specified then \code{sso} is returned with an updated model name. } \description{ View or change metadata associated with a \code{shinystan} object } \examples{ # use eight_schools example object sso <- eight_schools ################ ### sso_info ### ################ sso_info(sso) ################## ### model_code ### ################## # view model code in example shinystan object 'eight_schools' cat(model_code(sso)) # change the model code in sso # some jags style code my_code <- " model { for (i in 1:length(Y)) { Y[i] ~ dpois(lambda[i]) log(lambda[i]) <- inprod(X[i,], theta[]) } for (j in 1:J) { theta[j] ~ dt(0.0, 1.0, 1.0) } } " sso <- model_code(sso, my_code) cat(model_code(sso)) ############# ### notes ### ############# # view existing notes notes(sso) # add a note to the existing notes sso <- notes(sso, "New note") notes(sso) cat(notes(sso)) # replace existing notes sso <- notes(sso, "replacement note", replace = TRUE) notes(sso) ################## ### model_name ### ################## # view model name model_name(sso) # change model name sso <- model_name(sso, "some other name") identical(model_name(sso), "some other name") } \seealso{ \code{\link{as.shinystan}} for creating \code{shinystan} objects. \code{\link{drop_parameters}} to remove parameters from a \code{shinystan} object. \code{\link{generate_quantity}} to add a new quantity to a \code{shinystan} object. } shinystan/man/shinystan-package.Rd0000644000176200001440000000730613271435552016762 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinystan-package.R \docType{package} \name{shinystan-package} \alias{shinystan-package} \title{\pkg{shinystan} R package ('ShinyStan' graphical user interface)} \description{ \if{html}{ \figure{stanlogo.png}{options: width="50px" alt="mc-stan.org"} \emph{Stan Development Team} } Applied Bayesian data analysis is primarily implemented through the Markov chain Monte Carlo (MCMC) algorithms offered by various software packages. When analyzing a posterior sample obtained by one of these algorithms the first step is to check for signs that the chains have converged to the target distribution and and also for signs that the algorithm might require tuning or might be ill-suited for the given model. There may also be theoretical problems or practical inefficiencies with the specification of the model. The ShinyStan app provides interactive plots and tables helpful for analyzing a posterior sample, with particular attention to identifying potential problems with the performance of the MCMC algorithm or the specification of the model. ShinyStan is powered by the Shiny web application framework by RStudio (\url{http://shiny.rstudio.com/}) and works with the output of MCMC programs written in any programming language (and has extended functionality for models fit using the \pkg{rstan} package and the No-U-Turn sampler). } \section{ShinyStan has extended functionality for Stan models}{ Stan (\url{http://mc-stan.org}) models can be run in \R using the \pkg{\link[rstan]{rstan}} package. Other packages like \pkg{\link[rstanarm]{rstanarm}} and \pkg{\link[brms]{brms}} provide higher-level interfaces to Stan that use \pkg{rstan} internally. } \section{Saving and sharing}{ The \pkg{shinystan} package allows you to store the basic components of an entire project (code, posterior samples, graphs, tables, notes) in a single object, a \code{\link[=as.shinystan]{shinystan object}} (sso, for short). Users can save many of the plots as \pkg{ggplot2} objects for further customization and easy integration in reports or post-processing for publication. The \code{\link{deploy_shinystan}} function lets you easily deploy your own ShinyStan apps online for any of your models using the shinyapps.io service from 'RStudio'. Each of your apps (each of your models) will have a unique url and will be compatible with most web browsers. } \section{License}{ The \pkg{shinystan} package is open source licensed under the GNU Public License, version 3 (GPLv3). } \section{Demo}{ Check out the demo using \code{\link{launch_shinystan_demo}} or try it with one of your own models using \code{\link{launch_shinystan}}. } \section{Resources}{ \itemize{ \item Web page with online documentation (\url{http://mc-stan.org/shinystan}) \item Stan Forums on Discourse (\url{http://discourse.mc-stan.org}) \item GitHub issue tracker (\url{https://github.com/stan-dev/shinystan/issues}) } } \references{ Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. \emph{The Quantitative Methods for Psychology}. 14(2), 99--119. \url{https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf} Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2018). Visualization in Bayesian workflow. \emph{Journal of the Royal Statistical Society Series A}, accepted for publication. arXiv preprint: \url{http://arxiv.org/abs/1709.01449}. } \seealso{ \code{\link{as.shinystan}} for creating \code{shinystan} objects. \code{\link{launch_shinystan_demo}} to try a demo. \code{\link{launch_shinystan}} to launch the 'ShinyStan' interface using a particular \code{shinystan} object. } shinystan/man/generate_quantity.Rd0000644000176200001440000000237413140211435017064 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/generate_quantity.R \name{generate_quantity} \alias{generate_quantity} \title{Add new quantity to shinystan object} \usage{ generate_quantity(sso, param1, param2, fun, new_name) } \arguments{ \item{sso}{A \code{\link[=as.shinystan]{shinystan object}}.} \item{param1}{Name of first parameter as character string.} \item{param2}{Optional. Name of second parameter as character string.} \item{fun}{Function to call, i.e. \code{function(param1)} or \code{function(param1,param2)}. See Examples, below.} \item{new_name}{Name for the new parameter as character string.} } \value{ sso, updated. See Examples. } \description{ Add to shinystan object a new parameter as a function of one or two existing parameters. } \examples{ # Using example shinystan object 'eight_schools' sso <- eight_schools sso <- generate_quantity(sso, fun = function(x) x^2, param1 = "tau", new_name = "tau_sq") sso <- generate_quantity(sso, fun = "-", param1 = "theta[1]", param2 = "theta[2]", new_name = "theta1minus2") } \seealso{ \code{\link{drop_parameters}} to remove parameters from a \code{shinystan} object. }

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load 'old_sso', a shinystan object created by previous shinystan version load("old_sso_for_tests.rda") old_sso_msg <- "use the 'update_sso' function to update your object" # launch_shinystan -------------------------------------------------------- test_that("launch_shinystan throws appropriate errors", { expect_error(launch_shinystan(sso@summary), "object not compatible") expect_error(launch_shinystan(old_sso), old_sso_msg) }) # model_name -------------------------------------------------------------- test_that("model_name works", { expect_error(model_name(old_sso), old_sso_msg) expect_error(model_name(not_sso), not_sso_msg) sso2 <- model_name(sso, "test_rename") expect_identical(model_name(sso2), "test_rename") expect_error(model_name(sso, 1234), "should be a single string") expect_error(model_name(sso, c("a", "b")), "should be a single string") }) # model_code -------------------------------------------------------------- test_that("model_code works", { expect_error(model_code(old_sso), old_sso_msg) expect_error(model_code(not_sso), not_sso_msg) sso2 <- model_code(sso, "test_code") expect_identical(model_code(sso2), "test_code") expect_identical(model_code(sso2), slot(sso2, "model_code")) expect_error(model_code(sso, 1234), "should be NULL or a string") }) # notes ------------------------------------------------------------------- test_that("notes works", { expect_error(notes(old_sso), old_sso_msg) expect_error(notes(not_sso), not_sso_msg) sso2 <- notes(sso, "test_notes_replace", replace = TRUE) expect_identical(slot(sso2, "user_model_info"), "test_notes_replace") sso2 <- notes(sso2, "test_notes_keep", replace = FALSE) expect_identical(slot(sso2, "user_model_info"), notes(sso2)) expect_error(notes(sso, 1234), "should be a single string") expect_error(notes(sso, c("a", "b")), "should be a single string") }) # retrieve ---------------------------------------------------------------- test_that("retrieve works", { expect_error(retrieve(old_sso), old_sso_msg) expect_error(retrieve(not_sso), not_sso_msg) expect_error(retrieve(not_sso, what = "mean"), not_sso_msg) stats1 <- c("median", "mean", "rhat", "ess", "sd", "mcse") whats <- c(stats1, "quantiles", "divergent", "treedepth", "stepsize", "accept_stat") for (what in whats) expect_equal_to_reference( retrieve(sso, what), file = file.path("retrieve_sso_answers", paste0("test_answer_", what, ".rds")) ) for (what in stats1) expect_equal(names(retrieve(sso, what, pars = c("mu", "tau"))), c("mu", "tau")) expect_equal(rownames(retrieve(sso, what = "quantiles", pars = c("mu", "theta[2]"))), c("mu", "theta[2]")) }) # generate_quantity ------------------------------------------------------- test_that("generate_quantity works", { expect_error(generate_quantity(old_sso), old_sso_msg) expect_error(generate_quantity(not_sso), not_sso_msg) sso2 <- generate_quantity(sso, fun = function(x) x^2, param1 = "tau", new_name = "tau_sq") expect_equivalent(sso2@posterior_sample[,, "tau_sq", drop=FALSE], sso@posterior_sample[,, "tau", drop=FALSE]^2) sso2 <- generate_quantity(sso, fun = "-", param1 = "theta[1]", param2 = "theta[2]", new_name = "theta1minus2") expect_equivalent(sso2@posterior_sample[,, "theta1minus2", drop=FALSE], sso@posterior_sample[,, "theta[1]", drop=FALSE] - sso@posterior_sample[,, "theta[2]", drop=FALSE]) # test when sso only has one chain sso3 <- as.shinystan(list(cbind(b1 = rnorm(100), b2 = rnorm(100), s = rexp(100)))) sso3 <- generate_quantity(sso3, param1 = "b1", param2 = "b2", fun = "+", new_name = "b1plusb2") expect_equivalent(sso3@posterior_sample[,, "b1plusb2", drop=FALSE], sso3@posterior_sample[,, "b1", drop=FALSE] + sso3@posterior_sample[,, "b2", drop=FALSE]) }) # drop_parameters --------------------------------------------------------- test_that("drop_parameters throws correct warnings/errors", { expect_error(drop_parameters(old_sso, pars = "mu"), old_sso_msg) expect_error(drop_parameters(not_sso, pars = "mu"), not_sso_msg) expect_error(drop_parameters(sso, pars = "log-posterior"), "log-posterior can't be dropped") expect_error(drop_parameters(sso, pars = c("theta[1]", "mu")), regexp = "individual elements") expect_error(drop_parameters(sso, pars = "omega"), regexp = "No matches") expect_warning(drop_parameters(sso, pars = c("mu", "omega")), regexp = "not found and ignored: omega") }) test_that("drop_parameters works", { pn <- sso@param_names pd <- sso@param_dims s <- sso@summary samp <- sso@posterior_sample sso2 <- drop_parameters(sso, pars = "mu") expect_identical(sso2@param_names, pn[pn != "mu"]) expect_identical(sso2@param_dims, pd[names(pd) != "mu"]) expect_identical(sso2@summary, s[rownames(s) != "mu", ]) expect_identical(sso2@posterior_sample, samp[,, dimnames(samp)[[3]] != "mu"]) sso2 <- drop_parameters(sso, pars = "theta") expect_identical(sso2@param_names, grep("theta", pn, value = TRUE, invert = TRUE)) expect_identical(sso2@param_dims, pd[names(pd) != "theta"]) tmp <- s[grep("theta", rownames(s), value = TRUE, invert = TRUE), ] expect_identical(sso2@summary, tmp) tmp <- samp[,, grep("theta", dimnames(samp)[[3]], value = TRUE, invert = TRUE)] expect_identical(sso2@posterior_sample, tmp) sso2 <- drop_parameters(sso, pars = c("theta", "tau")) tmp <- grep("theta|tau", pn, value = TRUE, invert = TRUE) expect_identical(sso2@param_names, tmp) tmp <- pd[grep("theta|tau", names(pd), value = TRUE, invert = TRUE)] expect_identical(sso2@param_dims, tmp) tmp <- s[grep("theta|tau", rownames(s), value = TRUE, invert = TRUE), ] expect_identical(sso2@summary, tmp) tmp <- samp[,, grep("theta|tau", dimnames(samp)[[3]], value = TRUE, invert = TRUE)] expect_identical(sso2@posterior_sample, tmp) }) # sso_info ---------------------------------------------------------------- test_that("sso_info error checking", { expect_error(sso_info(old_sso), old_sso_msg) expect_error(sso_info(sso@posterior_sample), "specify a shinystan object") }) test_that("sso_info prints output", { expect_output(sso_info(sso), "sso") expect_output(sso_info(sso), "Model name: Demo") expect_output(sso_info(sso), "Parameters: 11") expect_output(sso_info(sso), "Chains: 4") expect_output(sso_info(sso), "Has model code: TRUE") expect_output(sso_info(sso), "Has user notes: FALSE") }) shinystan/tests/testthat/retrieve_sso_answers/0000755000176200001440000000000013044376075021560 5ustar liggesusersshinystan/tests/testthat/retrieve_sso_answers/test_answer_quantiles.rds0000644000176200001440000000116213044376075026715 0ustar liggesusersb```b`ffb`b1jI\0m^YKhxsI")9%EEPB%YLUazLS8.jiRT shinystan/tests/testthat/retrieve_sso_answers/test_answer_accept_stat.rds0000644000176200001440000000016313044376075027202 0ustar liggesusersb```b`ffb`b1 }|b^{߼[c;'0120piּb C*Ȗg3BL4Oshinystan/tests/testthat/retrieve_sso_answers/test_answer_median.rds0000644000176200001440000000032513044376075026145 0ustar liggesusersb```b`ffb`b1BEDN59g8( ru QAAmE"/8n yF(j5m;_ L',Ʈl&100gd`aҬy@R Sn)QZm7BMh|349g.I97'?] $(3(>K9shinystan/tests/testthat/retrieve_sso_answers/test_answer_treedepth.rds0000644000176200001440000000013213044376075026670 0ustar liggesusersb```b`ffb`b18HPq& YsS  T-9#13g3FYjrshinystan/tests/testthat/retrieve_sso_answers/test_answer_mcse.rds0000644000176200001440000000032613044376075025640 0ustar liggesusersb```b`ffb`b1ѥ> .j?koX7\?:o$7g'Ynbkoԛn_\& |FN ͚Z d, 2BY%%ц|#41o7C㛣-`|DSxsu KR2%9shinystan/tests/testthat/retrieve_sso_answers/test_answer_divergent.rds0000644000176200001440000000015713044376075026702 0ustar liggesusersb```b`ffb`b1=Yu?o$^g%~0120piּb C*Ȗg3BLJ5shinystan/tests/testthat/retrieve_sso_answers/test_answer_ess.rds0000644000176200001440000000032613044376075025503 0ustar liggesusersb```b`ffb`b11_?5ws{Fd)vXTyŷf;>jZcޙ $n\kp|ٖNf300gd`aҬy@R Sn)QZm7BMh|349g.I97'?] $(3(%U9shinystan/tests/testthat/retrieve_sso_answers/test_answer_rhat.rds0000644000176200001440000000031713044376075025647 0ustar liggesusersb```b`ffb`b1շۓ?0Yrc-lR0/GscRFP=y?yf`f-N?DZfa , @5/17Y d-8J2RK cQFh|c4 o7G[%0eӹ9shinystan/tests/testthat/retrieve_sso_answers/test_answer_sd.rds0000644000176200001440000000032513044376075025316 0ustar liggesusersb```b`ffb`b1j38H]PxAak{K%%&W:HNTAQ#4luPXw^dsn+Wm4H% KLPGIFjIba,*o7A㛢h| $ޜt݂Ԣ"?׌9shinystan/tests/testthat/retrieve_sso_answers/test_answer_stepsize.rds0000644000176200001440000000016313044376075026556 0ustar liggesusersb```b`ffb`b1slnwׂGuםt_s9 H% PAAAwNOfcVX320piּb Cd)T)(H-I6E&h|S4o3$œ›[_\Z_`}9shinystan/tests/testthat/old_sso_for_tests.rda0000644000176200001440000263253613035516434021543 0ustar liggesusers 4?yyo<~Q(2dHTJIiUnTDd(2D{wwκUn~_z>9Yk*+G/(GX^~zH,&64,:pCPLJ{D9,&pL xDF׳7m .N$!2,!h-򿮭k^dtlMQLhT χ!?]o_rR<>lV7ˎV=K)uzA|q3(3ʅW&tꑕQ~,y{Ж5}"[SV@/6YukP{h b$ %͇\el=FފLs2P.w+ߤ "2 o| w̄z`dͣPKYctơӰbn1Lf`4Eu %`xݽaX0S1ƖmSGwF19Z ‚@%ݽti(NlѻG/߽'>g!F+!h\xl:}Ĉ=eZf RHg6 4BW:+ͮ[+R,=YP?ʼUrǪO ~ BBVj䵮z/]'xX/ M' `O; 94U+$dawixбG }'O i)ڦ ~>&*di8]f}Z*ή. 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