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m : ϻk{gRt x) .}
\item{p}{Vector of probabilities.}
\item{n}{Number of samples to draw from the distribution.}
}
\description{
Density, distribution function, quantile function and random generation
for the Student-t distribution with location \code{mu}, scale \code{sigma},
and degrees of freedom \code{df}.
}
\details{
See \code{vignette("brms_families")} for details
on the parameterization.
}
\seealso{
\code{\link[stats:TDist]{TDist}}
}
brms/man/loo_compare.brmsfit.Rd 0000644 0001762 0000144 00000002510 13601151454 016221 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{loo_compare.brmsfit}
\alias{loo_compare.brmsfit}
\alias{loo_compare}
\title{Model comparison with the \pkg{loo} package}
\usage{
\method{loo_compare}{brmsfit}(x, ..., criterion = c("loo", "waic", "kfold"), model_names = NULL)
}
\arguments{
\item{x}{A \code{brmsfit} object.}
\item{...}{More \code{brmsfit} objects.}
\item{criterion}{The name of the criterion to be extracted
from \code{brmsfit} objects.}
\item{model_names}{If \code{NULL} (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.}
}
\value{
An object of class "\code{compare.loo}".
}
\description{
For more details see \code{\link[loo:loo_compare]{loo_compare}}.
}
\details{
All \code{brmsfit} objects should contain precomputed
criterion objects. See \code{\link{add_criterion}} for more help.
}
\examples{
\dontrun{
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
data = inhaler)
fit1 <- add_criterion(fit1, "waic")
# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
fit2 <- add_criterion(fit2, "waic")
# compare both models
loo_compare(fit1, fit2, criterion = "waic")
}
}
brms/man/theme_default.Rd 0000644 0001762 0000144 00000000777 13565500270 015103 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggplot-themes.R
\name{theme_default}
\alias{theme_default}
\title{Default \pkg{bayesplot} Theme for \pkg{ggplot2} Graphics}
\arguments{
\item{base_size}{base font size}
\item{base_family}{base font family}
}
\value{
A \code{theme} object used in \pkg{ggplot2} graphics.
}
\description{
This theme is imported from the \pkg{bayesplot} package.
See \code{\link[bayesplot:theme_default]{theme_default}}
for a complete documentation.
}
brms/man/posterior_average.brmsfit.Rd 0000644 0001762 0000144 00000006131 13601162150 017440 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model_weights.R
\name{posterior_average.brmsfit}
\alias{posterior_average.brmsfit}
\alias{posterior_average}
\title{Posterior samples of parameters averaged across models}
\usage{
\method{posterior_average}{brmsfit}(
x,
...,
pars = NULL,
weights = "stacking",
nsamples = NULL,
missing = NULL,
model_names = NULL,
control = list(),
seed = NULL
)
posterior_average(x, ...)
}
\arguments{
\item{x}{A \code{brmsfit} object.}
\item{...}{More \code{brmsfit} objects or further arguments
passed to the underlying post-processing functions.
In particular, see \code{\link{extract_draws}} for further
supported arguments.}
\item{pars}{Names of parameters for which to average across models.
Only those parameters can be averaged that appear in every model.
Defaults to all overlapping parameters.}
\item{weights}{Name of the criterion to compute weights from. Should be one
of \code{"loo"}, \code{"waic"}, \code{"kfold"}, \code{"stacking"} (current
default), or \code{"bma"}, \code{"pseudobma"}, For the former three
options, Akaike weights will be computed based on the information criterion
values returned by the respective methods. For \code{"stacking"} and
\code{"pseudobma"} method \code{\link{loo_model_weights}} will be used to
obtain weights. For \code{"bma"}, method \code{\link{post_prob}} will be
used to compute Bayesian model averaging weights based on log marginal
likelihood values (make sure to specify reasonable priors in this case).
Some some method, \code{weights} may also be be a numeric vector of
pre-specified weights.}
\item{nsamples}{Total number of posterior samples to use.}
\item{missing}{An optional numeric value or a named list of numeric values
to use if a model does not contain a parameter for which posterior samples
should be averaged. Defaults to \code{NULL}, in which case only those
parameters can be averaged that are present in all of the models.}
\item{model_names}{If \code{NULL} (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.}
\item{control}{Optional \code{list} of further arguments
passed to the function specified in \code{weights}.}
\item{seed}{A single numeric value passed to \code{\link{set.seed}}
to make results reproducible.}
}
\value{
A \code{data.frame} of posterior samples. Samples are rows
and parameters are columns.
}
\description{
Extract posterior samples of parameters averaged across models.
Weighting can be done in various ways, for instance using
Akaike weights based on information criteria or
marginal likelihoods.
}
\details{
Weights are computed with the \code{\link{model_weights}} method.
}
\examples{
\dontrun{
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
summary(fit1)
# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)
summary(fit2)
# compute model-averaged posteriors of overlapping parameters
posterior_average(fit1, fit2, weights = "waic")
}
}
\seealso{
\code{\link{model_weights}}, \code{\link{pp_average}}
}
brms/man/logit_scaled.Rd 0000644 0001762 0000144 00000000706 13623751614 014723 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/numeric-helpers.R
\name{logit_scaled}
\alias{logit_scaled}
\title{Scaled logit-link}
\usage{
logit_scaled(x, lb = 0, ub = 1)
}
\arguments{
\item{x}{A numeric or complex vector.}
\item{lb}{Lower bound defaulting to \code{0}.}
\item{ub}{Upper bound defaulting to \code{1}.}
}
\value{
A numeric or complex vector.
}
\description{
Computes \code{logit((x - lb) / (ub - lb))}
}
brms/man/residuals.brmsfit.Rd 0000644 0001762 0000144 00000007321 13601120650 015714 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predictive_error.R
\name{residuals.brmsfit}
\alias{residuals.brmsfit}
\title{Posterior Samples of Residuals/Predictive Errors}
\usage{
\method{residuals}{brmsfit}(
object,
newdata = NULL,
re_formula = NULL,
method = "pp_expect",
type = c("ordinary", "pearson"),
resp = NULL,
nsamples = NULL,
subset = NULL,
sort = FALSE,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used.
\code{NA} values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.}
\item{re_formula}{formula containing group-level effects to be considered in
the prediction. If \code{NULL} (default), include all group-level effects;
if \code{NA}, include no group-level effects.}
\item{method}{Method use to obtain predictions. Either
\code{"pp_expect"} (the default) or \code{"posterior_predict"}.
Using \code{"posterior_predict"} is recommended
but \code{"pp_expect"} is the current default for
reasons of backwards compatibility.}
\item{type}{The type of the residuals,
either \code{"ordinary"} or \code{"pearson"}.
More information is provided under 'Details'.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{nsamples}{Positive integer indicating how many posterior samples should
be used. If \code{NULL} (the default) all samples are used. Ignored if
\code{subset} is not \code{NULL}.}
\item{subset}{A numeric vector specifying the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{sort}{Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order (\code{FALSE}; default) or in the order of the
time series (\code{TRUE}).}
\item{summary}{Should summary statistics be returned
instead of the raw values? Default is \code{TRUE}..}
\item{robust}{If \code{FALSE} (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If \code{TRUE}, the median and the
median absolute deviation (MAD) are applied instead.
Only used if \code{summary} is \code{TRUE}.}
\item{probs}{The percentiles to be computed by the \code{quantile}
function. Only used if \code{summary} is \code{TRUE}.}
\item{...}{Further arguments passed to \code{\link{extract_draws}}
that control several aspects of data validation and prediction.}
}
\value{
An \code{array} of predictive error/residual samples. If
\code{summary = FALSE} the output resembles those of
\code{\link{predictive_error.brmsfit}}. If \code{summary = TRUE} the output
is an N x E matrix, where N is the number of observations and E denotes
the summary statistics computed from the samples.
}
\description{
This method is an alias of \code{\link{predictive_error.brmsfit}}
with additional arguments for obtaining summaries of the computed samples.
}
\details{
Residuals of type \code{'ordinary'} are of the form \eqn{R = Y -
Yrep}, where \eqn{Y} is the observed and \eqn{Yrep} is the predicted response.
Residuals of type \code{pearson} are of the form \eqn{R = (Y - Yrep) /
SD(Y)}, where \eqn{SD(Y)} is an estimation of the standard deviation of
\eqn{Y}.
}
\examples{
\dontrun{
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler, cores = 2)
## extract residuals/predictive errors
res <- residuals(fit)
head(res)
}
}
brms/man/prior_samples.brmsfit.Rd 0000644 0001762 0000144 00000003255 13601201144 016600 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior_samples.R
\name{prior_samples.brmsfit}
\alias{prior_samples.brmsfit}
\alias{prior_samples}
\title{Extract prior samples}
\usage{
\method{prior_samples}{brmsfit}(x, pars = NA, ...)
prior_samples(x, pars = NA, ...)
}
\arguments{
\item{x}{An \code{R} object typically of class \code{brmsfit}}
\item{pars}{Names of parameters for which prior samples should be returned,
as given by a character vector or regular expressions.
By default, all prior samples are extracted}
\item{...}{Currently ignored}
}
\value{
A data frame containing the prior samples.
}
\description{
Extract prior samples of specified parameters
}
\details{
To make use of this function, the model must contain samples of
prior distributions. This can be ensured by setting \code{sample_prior =
TRUE} in function \code{brm}. Priors of certain parameters cannot be saved
for technical reasons. For instance, this is the case for the
population-level intercept, which is only computed after fitting the model
by default. If you want to treat the intercept as part of all the other
regression coefficients, so that sampling from its prior becomes possible,
use \code{... ~ 0 + Intercept + ...} in the formulas.
}
\examples{
\dontrun{
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "cumulative",
prior = set_prior("normal(0,2)", class = "b"),
sample_prior = TRUE)
# extract all prior samples
samples1 <- prior_samples(fit)
head(samples1)
# extract prior samples for the population-level effects of 'treat'
samples2 <- prior_samples(fit, "b_treat")
head(samples2)
}
}
brms/man/InvGaussian.Rd 0000644 0001762 0000144 00000002067 13565500267 014524 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{InvGaussian}
\alias{InvGaussian}
\alias{dinv_gaussian}
\alias{pinv_gaussian}
\alias{rinv_gaussian}
\title{The Inverse Gaussian Distribution}
\usage{
dinv_gaussian(x, mu = 1, shape = 1, log = FALSE)
pinv_gaussian(q, mu = 1, shape = 1, lower.tail = TRUE, log.p = FALSE)
rinv_gaussian(n, mu = 1, shape = 1)
}
\arguments{
\item{x, q}{Vector of quantiles.}
\item{mu}{Vector of locations.}
\item{shape}{Vector of shapes.}
\item{log}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{lower.tail}{Logical; If \code{TRUE} (default), return P(X <= x).
Else, return P(X > x) .}
\item{log.p}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{n}{Number of samples to draw from the distribution.}
}
\description{
Density, distribution function, and random generation
for the inverse Gaussian distribution with location \code{mu},
and shape \code{shape}.
}
\details{
See \code{vignette("brms_families")} for details
on the parameterization.
}
brms/man/pp_average.brmsfit.Rd 0000644 0001762 0000144 00000006752 13601162150 016042 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model_weights.R
\name{pp_average.brmsfit}
\alias{pp_average.brmsfit}
\alias{pp_average}
\title{Posterior predictive samples averaged across models}
\usage{
\method{pp_average}{brmsfit}(
x,
...,
weights = "stacking",
method = "posterior_predict",
nsamples = NULL,
summary = TRUE,
probs = c(0.025, 0.975),
robust = FALSE,
model_names = NULL,
control = list(),
seed = NULL
)
pp_average(x, ...)
}
\arguments{
\item{x}{A \code{brmsfit} object.}
\item{...}{More \code{brmsfit} objects or further arguments
passed to the underlying post-processing functions.
In particular, see \code{\link{extract_draws}} for further
supported arguments.}
\item{weights}{Name of the criterion to compute weights from. Should be one
of \code{"loo"}, \code{"waic"}, \code{"kfold"}, \code{"stacking"} (current
default), or \code{"bma"}, \code{"pseudobma"}, For the former three
options, Akaike weights will be computed based on the information criterion
values returned by the respective methods. For \code{"stacking"} and
\code{"pseudobma"} method \code{\link{loo_model_weights}} will be used to
obtain weights. For \code{"bma"}, method \code{\link{post_prob}} will be
used to compute Bayesian model averaging weights based on log marginal
likelihood values (make sure to specify reasonable priors in this case).
Some some method, \code{weights} may also be be a numeric vector of
pre-specified weights.}
\item{method}{Method used to obtain predictions to average over. Should be
one of \code{"posterior_predict"} (default), \code{"pp_expect"}, or
\code{"predictive_error"}.}
\item{nsamples}{Total number of posterior samples to use.}
\item{summary}{Should summary statistics
(i.e. means, sds, and 95\% intervals) be returned
instead of the raw values? Default is \code{TRUE}.}
\item{probs}{The percentiles to be computed by the \code{quantile}
function. Only used if \code{summary} is \code{TRUE}.}
\item{robust}{If \code{FALSE} (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If \code{TRUE}, the median and the
median absolute deviation (MAD) are applied instead.
Only used if \code{summary} is \code{TRUE}.}
\item{model_names}{If \code{NULL} (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.}
\item{control}{Optional \code{list} of further arguments
passed to the function specified in \code{weights}.}
\item{seed}{A single numeric value passed to \code{\link{set.seed}}
to make results reproducible.}
}
\value{
Same as the output of the method specified
in argument \code{method}.
}
\description{
Compute posterior predictive samples averaged across models.
Weighting can be done in various ways, for instance using
Akaike weights based on information criteria or
marginal likelihoods.
}
\details{
Weights are computed with the \code{\link{model_weights}} method.
}
\examples{
\dontrun{
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
summary(fit1)
# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)
summary(fit2)
# compute model-averaged predicted values
(df <- unique(inhaler[, c("treat", "period", "carry")]))
pp_average(fit1, fit2, newdata = df)
# compute model-averaged fitted values
pp_average(fit1, fit2, method = "fitted", newdata = df)
}
}
\seealso{
\code{\link{model_weights}}, \code{\link{posterior_average}}
}
brms/man/cor_bsts.Rd 0000644 0001762 0000144 00000001576 13567776477 014143 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autocor.R
\name{cor_bsts}
\alias{cor_bsts}
\title{(Defunct) Basic Bayesian Structural Time Series}
\usage{
cor_bsts(formula = ~1)
}
\arguments{
\item{formula}{A one sided formula of the form \code{~ t}, or \code{~ t | g},
specifying a time covariate \code{t} and, optionally, a grouping factor
\code{g}. A covariate for this correlation structure must be integer
valued. When a grouping factor is present in \code{formula}, the
correlation structure is assumed to apply only to observations within the
same grouping level; observations with different grouping levels are
assumed to be uncorrelated. Defaults to \code{~ 1}, which corresponds to
using the order of the observations in the data as a covariate, and no
groups.}
}
\description{
The BSTS correlation structure is no longer supported.
}
\keyword{internal}
brms/man/VarCorr.brmsfit.Rd 0000644 0001762 0000144 00000003302 13601201144 015270 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsfit-methods.R
\name{VarCorr.brmsfit}
\alias{VarCorr.brmsfit}
\alias{VarCorr}
\title{Extract Variance and Correlation Components}
\usage{
\method{VarCorr}{brmsfit}(
x,
sigma = 1,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
}
\arguments{
\item{x}{An object of class \code{brmsfit}.}
\item{sigma}{Ignored (included for compatibility with
\code{\link[nlme:VarCorr]{VarCorr}}).}
\item{summary}{Should summary statistics be returned
instead of the raw values? Default is \code{TRUE}.}
\item{robust}{If \code{FALSE} (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If \code{TRUE}, the median and the
median absolute deviation (MAD) are applied instead.
Only used if \code{summary} is \code{TRUE}.}
\item{probs}{The percentiles to be computed by the \code{quantile}
function. Only used if \code{summary} is \code{TRUE}.}
\item{...}{Currently ignored.}
}
\value{
A list of lists (one per grouping factor), each with
three elements: a matrix containing the standard deviations,
an array containing the correlation matrix, and an array
containing the covariance matrix with variances on the diagonal.
}
\description{
This function calculates the estimated standard deviations,
correlations and covariances of the group-level terms
in a multilevel model of class \code{brmsfit}.
For linear models, the residual standard deviations,
correlations and covariances are also returned.
}
\examples{
\dontrun{
fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit),
data = epilepsy, family = gaussian(), chains = 2)
VarCorr(fit)
}
}
brms/man/arma.Rd 0000644 0001762 0000144 00000003534 13611651307 013207 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-ac.R
\name{arma}
\alias{arma}
\title{Set up ARMA(p,q) correlation structures}
\usage{
arma(time = NA, gr = NA, p = 1, q = 1, cov = FALSE)
}
\arguments{
\item{time}{An optional time variable specifying the time ordering
of the observations. By default, the existing order of the observations
in the data is used.}
\item{gr}{An optional grouping variable. If specified, the correlation
structure is assumed to apply only to observations within the same grouping
level.}
\item{p}{A non-negative integer specifying the autoregressive (AR)
order of the ARMA structure. Default is \code{1}.}
\item{q}{A non-negative integer specifying the moving average (MA)
order of the ARMA structure. Default is \code{1}.}
\item{cov}{A flag indicating whether ARMA effects should be estimated by
means of residual covariance matrices. This is currently only possible for
stationary ARMA effects of order 1. If the model family does not have
natural residuals, latent residuals are added automatically. If
\code{FALSE} (the default), a regression formulation is used that is
considerably faster and allows for ARMA effects of order higher than 1 but
is only available for \code{gaussian} models and some of its
generalizations.}
}
\value{
An object of class \code{'arma_term'}, which is a list
of arguments to be interpreted by the formula
parsing functions of \pkg{brms}.
}
\description{
Set up an autoregressive moving average (ARMA) term of order (p, q) in
\pkg{brms}. The function does not evaluate its arguments -- it exists purely
to help set up a model with ARMA terms.
}
\examples{
\dontrun{
data("LakeHuron")
LakeHuron <- as.data.frame(LakeHuron)
fit <- brm(x ~ arma(p = 2, q = 1), data = LakeHuron)
summary(fit)
}
}
\seealso{
\code{\link{autocor-terms}}, \code{\link{ar}}, \code{\link{ma}},
}
brms/man/mm.Rd 0000644 0001762 0000144 00000003326 13606623217 012702 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-re.R
\name{mm}
\alias{mm}
\title{Set up multi-membership grouping terms in \pkg{brms}}
\usage{
mm(..., weights = NULL, scale = TRUE, dist = "gaussian")
}
\arguments{
\item{...}{One or more terms containing grouping factors.}
\item{weights}{A matrix specifying the weights of each member.
It should have as many columns as grouping terms specified in \code{...}.
If \code{NULL} (the default), equally weights are used.}
\item{scale}{Logical; if \code{TRUE} (the default),
weights are standardized in order to sum to one per row.
If negative weights are specified, \code{scale} needs
to be set to \code{FALSE}.}
\item{dist}{Name of the distribution of the group-level effects.
Currently \code{"gaussian"} is the only option.}
}
\description{
Function to set up a multi-membership grouping term in \pkg{brms}.
The function does not evaluate its arguments --
it exists purely to help set up a model with grouping terms.
}
\examples{
\dontrun{
# simulate some data
dat <- data.frame(
y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100),
g1 = sample(1:10, 100, TRUE), g2 = sample(1:10, 100, TRUE)
)
# multi-membership model with two members per group and equal weights
fit1 <- brm(y ~ x1 + (1|mm(g1, g2)), data = dat)
summary(fit1)
# weight the first member two times for than the second member
dat$w1 <- rep(2, 100)
dat$w2 <- rep(1, 100)
fit2 <- brm(y ~ x1 + (1|mm(g1, g2, weights = cbind(w1, w2))), data = dat)
summary(fit2)
# multi-membership model with level specific covariate values
dat$xc <- (dat$x1 + dat$x2) / 2
fit3 <- brm(y ~ xc + (1 + mmc(x1, x2) | mm(g1, g2)), data = dat)
summary(fit3)
}
}
\seealso{
\code{\link{brmsformula}}, \code{\link{mmc}}
}
brms/man/set_prior.Rd 0000644 0001762 0000144 00000044336 13612313327 014300 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/priors.R
\name{set_prior}
\alias{set_prior}
\alias{brmsprior}
\alias{brmsprior-class}
\alias{prior}
\alias{prior_}
\alias{prior_string}
\alias{empty_prior}
\title{Prior Definitions for \pkg{brms} Models}
\usage{
set_prior(
prior,
class = "b",
coef = "",
group = "",
resp = "",
dpar = "",
nlpar = "",
lb = NA,
ub = NA,
check = TRUE
)
prior(prior, ...)
prior_(prior, ...)
prior_string(prior, ...)
empty_prior()
}
\arguments{
\item{prior}{A character string defining a distribution in \pkg{Stan} language}
\item{class}{The parameter class. Defaults to \code{"b"}
(i.e. population-level effects).
See 'Details' for other valid parameter classes.}
\item{coef}{Name of the coefficient within the parameter class.}
\item{group}{Grouping factor for group-level parameters.}
\item{resp}{Name of the response variable.
Only used in multivariate models.}
\item{dpar}{Name of a distributional parameter.
Only used in distributional models.}
\item{nlpar}{Name of a non-linear parameter.
Only used in non-linear models.}
\item{lb}{Lower bound for parameter restriction. Currently only allowed
for classes \code{"b"}. Defaults to \code{NULL}, that is no restriction.}
\item{ub}{Upper bound for parameter restriction. Currently only allowed
for classes \code{"b"}. Defaults to \code{NULL}, that is no restriction.}
\item{check}{Logical; Indicates whether priors
should be checked for validity (as far as possible).
Defaults to \code{TRUE}. If \code{FALSE}, \code{prior} is passed
to the Stan code as is, and all other arguments are ignored.}
\item{...}{Arguments passed to \code{set_prior}.}
}
\value{
An object of class \code{brmsprior} to be used in the \code{prior}
argument of \code{\link{brm}}.
}
\description{
Define priors for specific parameters or classes of parameters.
}
\details{
\code{set_prior} is used to define prior distributions for parameters
in \pkg{brms} models. The functions \code{prior}, \code{prior_}, and
\code{prior_string} are aliases of \code{set_prior} each allowing
for a different kind of argument specification.
\code{prior} allows specifying arguments as expression without
quotation marks using non-standard evaluation.
\code{prior_} allows specifying arguments as one-sided formulas
or wrapped in \code{quote}.
\code{prior_string} allows specifying arguments as strings just
as \code{set_prior} itself.
Below, we explain its usage and list some common
prior distributions for parameters.
A complete overview on possible prior distributions is given
in the Stan Reference Manual available at \url{http://mc-stan.org/}.
To combine multiple priors, use \code{c(...)} or the \code{+} operator
(see 'Examples'). \pkg{brms} does not check if the priors are written
in correct \pkg{Stan} language. Instead, \pkg{Stan} will check their
syntactical correctness when the model is parsed to \code{C++} and
returns an error if they are not.
This, however, does not imply that priors are always meaningful if they are
accepted by \pkg{Stan}. Although \pkg{brms} trys to find common problems
(e.g., setting bounded priors on unbounded parameters), there is no guarantee
that the defined priors are reasonable for the model.
Below, we list the types of parameters in \pkg{brms} models,
for which the user can specify prior distributions.
1. Population-level ('fixed') effects
Every Population-level effect has its own regression parameter
represents the name of the corresponding population-level effect.
Suppose, for instance, that \code{y} is predicted by \code{x1} and \code{x2}
(i.e., \code{y ~ x1 + x2} in formula syntax).
Then, \code{x1} and \code{x2} have regression parameters
\code{b_x1} and \code{b_x2} respectively.
The default prior for population-level effects (including monotonic and
category specific effects) is an improper flat prior over the reals.
Other common options are normal priors or student-t priors.
If we want to have a normal prior with mean 0 and
standard deviation 5 for \code{x1}, and a unit student-t prior with 10
degrees of freedom for \code{x2}, we can specify this via
\code{set_prior("normal(0,5)", class = "b", coef = "x1")} and \cr
\code{set_prior("student_t(10,0,1)", class = "b", coef = "x2")}.
To put the same prior on all population-level effects at once,
we may write as a shortcut \code{set_prior("", class = "b")}.
This also leads to faster sampling, because priors can be vectorized in this case.
Both ways of defining priors can be combined using for instance
\code{set_prior("normal(0,2)", class = "b")} and \cr
\code{set_prior("normal(0,10)", class = "b", coef = "x1")}
at the same time. This will set a \code{normal(0,10)} prior on
the effect of \code{x1} and a \code{normal(0,2)} prior
on all other population-level effects.
However, this will break vectorization and
may slow down the sampling procedure a bit.
In case of the default intercept parameterization
(discussed in the 'Details' section of \code{\link{brmsformula}}),
general priors on class \code{"b"} will \emph{not} affect
the intercept. Instead, the intercept has its own parameter class
named \code{"Intercept"} and priors can thus be
specified via \code{set_prior("", class = "Intercept")}.
Setting a prior on the intercept will not break vectorization
of the other population-level effects.
Note that technically, this prior is set on an intercept that
results when internally centering all population-level predictors
around zero to improve sampling efficiency. On this centered
intercept, specifying a prior is actually much easier and
intuitive than on the original intercept, since the former
represents the expected response value when all predictors
are at their means. To treat the intercept as an ordinary
population-level effect and avoid the centering parameterization,
use \code{0 + intercept} on the right-hand side of the model formula.
A special shrinkage prior to be applied on population-level effects is the
(regularized) horseshoe prior and related priors. See
\code{\link{horseshoe}} for details. Another shrinkage prior is the
so-called lasso prior. See \code{\link{lasso}} for details.
In non-linear models, population-level effects are defined separately
for each non-linear parameter. Accordingly, it is necessary to specify
the non-linear parameter in \code{set_prior} so that priors
we can be assigned correctly.
If, for instance, \code{alpha} is the parameter and \code{x} the predictor
for which we want to define the prior, we can write
\code{set_prior("", coef = "x", nlpar = "alpha")}.
As a shortcut we can use \code{set_prior("", nlpar = "alpha")}
to set the same prior on all population-level effects of \code{alpha} at once.
If desired, population-level effects can be restricted to fall only
within a certain interval using the \code{lb} and \code{ub} arguments
of \code{set_prior}. This is often required when defining priors
that are not defined everywhere on the real line, such as uniform
or gamma priors. When defining a \code{uniform(2,4)} prior,
you should write \code{set_prior("uniform(2,4)", lb = 2, ub = 4)}.
When using a prior that is defined on the positive reals only
(such as a gamma prior) set \code{lb = 0}.
In most situations, it is not useful to restrict population-level
parameters through bounded priors
(non-linear models are an important exception),
but if you really want to this is the way to go.
2. Standard deviations of group-level ('random') effects
Each group-level effect of each grouping factor has a standard deviation named
\code{sd__}. Consider, for instance, the formula
\code{y ~ x1 + x2 + (1 + x1 | g)}.
We see that the intercept as well as \code{x1} are group-level effects
nested in the grouping factor \code{g}.
The corresponding standard deviation parameters are named as
\code{sd_g_Intercept} and \code{sd_g_x1} respectively.
These parameters are restricted to be non-negative and, by default,
have a half student-t prior with 3 degrees of freedom and a
scale parameter that depends on the standard deviation of the response
after applying the link function. Minimally, the scale parameter is 10.
This prior is used (a) to be only very weakly informative in order to influence
results as few as possible, while (b) providing at least some regularization
to considerably improve convergence and sampling efficiency.
To define a prior distribution only for standard deviations
of a specific grouping factor,
use \cr \code{set_prior("", class = "sd", group = "")}.
To define a prior distribution only for a specific standard deviation
of a specific grouping factor, you may write \cr
\code{set_prior("", class = "sd", group = "", coef = "")}.
Recommendations on useful prior distributions for
standard deviations are given in Gelman (2006), but note that he
is no longer recommending uniform priors, anymore. \cr
When defining priors on group-level parameters in non-linear models,
please make sure to specify the corresponding non-linear parameter
through the \code{nlpar} argument in the same way as
for population-level effects.
3. Correlations of group-level ('random') effects
If there is more than one group-level effect per grouping factor,
the correlations between those effects have to be estimated.
The prior \code{lkj_corr_cholesky(eta)} or in short
\code{lkj(eta)} with \code{eta > 0}
is essentially the only prior for (Cholesky factors) of correlation matrices.
If \code{eta = 1} (the default) all correlations matrices
are equally likely a priori. If \code{eta > 1}, extreme correlations
become less likely, whereas \code{0 < eta < 1} results in
higher probabilities for extreme correlations.
Correlation matrix parameters in \code{brms} models are named as
\code{cor_}, (e.g., \code{cor_g} if \code{g} is the grouping factor).
To set the same prior on every correlation matrix,
use for instance \code{set_prior("lkj(2)", class = "cor")}.
Internally, the priors are transformed to be put on the Cholesky factors
of the correlation matrices to improve efficiency and numerical stability.
The corresponding parameter class of the Cholesky factors is \code{L},
but it is not recommended to specify priors for this parameter class directly.
4. Splines
Splines are implemented in \pkg{brms} using the 'random effects'
formulation as explained in \code{\link[mgcv:gamm]{gamm}}).
Thus, each spline has its corresponding standard deviations
modeling the variability within this term. In \pkg{brms}, this
parameter class is called \code{sds} and priors can
be specified via \code{set_prior("", class = "sds",
coef = "")}. The default prior is the same as
for standard deviations of group-level effects.
5. Gaussian processes
Gaussian processes as currently implemented in \pkg{brms} have
two parameters, the standard deviation parameter \code{sdgp},
and characteristic length-scale parameter \code{lscale}
(see \code{\link{gp}} for more details). The default prior
of \code{sdgp} is the same as for standard deviations of
group-level effects. The default prior of \code{lscale}
is an informative inverse-gamma prior specifically tuned
to the covariates of the Gaussian process (for more details see
\url{https://betanalpha.github.io/assets/case_studies/gp_part3/part3.html}).
This tuned prior may be overly informative in some cases, so please
consider other priors as well to make sure inference is
robust to the prior specification. If tuning fails, a half-normal prior
is used instead.
6. Autocorrelation parameters
The autocorrelation parameters currently implemented are named
\code{ar} (autoregression), \code{ma} (moving average),
\code{arr} (autoregression of the response), \code{car}
(spatial conditional autoregression), as well as \code{lagsar}
and \code{errorsar} (Spatial simultaneous autoregression).
Priors can be defined by \code{set_prior("", class = "ar")}
for \code{ar} and similar for other autocorrelation parameters.
By default, \code{ar} and \code{ma} are bounded between \code{-1}
and \code{1}, \code{car}, \code{lagsar}, and \code{errorsar} are
bounded between \code{0}, and \code{1}, and \code{arr} is unbounded
(you may change this by using the arguments \code{lb} and \code{ub}).
The default prior is flat over the definition area.
7. Distance parameters of monotonic effects
As explained in the details section of \code{\link{brm}},
monotonic effects make use of a special parameter vector to
estimate the 'normalized distances' between consecutive predictor
categories. This is realized in \pkg{Stan} using the \code{simplex}
parameter type. This class is named \code{"simo"} (short for
simplex monotonic) in \pkg{brms}.
The only valid prior for simplex parameters is the
dirichlet prior, which accepts a vector of length \code{K - 1}
(K = number of predictor categories) as input defining the
'concentration' of the distribution. Explaining the dirichlet prior
is beyond the scope of this documentation, but we want to describe
how to define this prior syntactically correct.
If a predictor \code{x} with \code{K} categories is modeled as monotonic,
we can define a prior on its corresponding simplex via \cr
\code{prior(dirichlet(), class = simo, coef = mox1)}.
The \code{1} in the end of \code{coef} indicates that this is the first
simplex in this term. If interactions between multiple monotonic
variables are modeled, multiple simplexes per term are required.
For \code{}, we can put in any \code{R} expression
defining a vector of length \code{K - 1}. The default is a uniform
prior (i.e. \code{ = rep(1, K-1)}) over all simplexes
of the respective dimension.
8. Parameters for specific families
Some families need additional parameters to be estimated.
Families \code{gaussian}, \code{student}, \code{skew_normal},
\code{lognormal}, and \code{gen_extreme_value} need the parameter
\code{sigma} to account for the residual standard deviation.
By default, \code{sigma} has a half student-t prior that scales
in the same way as the group-level standard deviations.
Further, family \code{student} needs the parameter
\code{nu} representing the degrees of freedom of students-t distribution.
By default, \code{nu} has prior \code{gamma(2, 0.1)}
and a fixed lower bound of \code{1}.
Families \code{gamma}, \code{weibull}, \code{inverse.gaussian}, and
\code{negbinomial} need a \code{shape} parameter that has a
\code{gamma(0.01, 0.01)} prior by default.
For families \code{cumulative}, \code{cratio}, \code{sratio},
and \code{acat}, and only if \code{threshold = "equidistant"},
the parameter \code{delta} is used to model the distance between
two adjacent thresholds.
By default, \code{delta} has an improper flat prior over the reals.
The \code{von_mises} family needs the parameter \code{kappa}, representing
the concentration parameter. By default, \code{kappa} has prior
\code{gamma(2, 0.01)}. \cr
Every family specific parameter has its own prior class, so that
\code{set_prior("", class = "")} is the right way to go.
All of these priors are chosen to be weakly informative,
having only minimal influence on the estimations,
while improving convergence and sampling efficiency.
Fixing parameters to constants is possible by using the \code{constant}
function, for example, \code{constant(1)} to fix a parameter to 1.
Broadcasting to vectors and matrices is done automatically.
A limitation of the current implementation is that the same parameter
vector cannot contain estimated and fixed values at the same time,
but this will be possible in the future.
Often, it may not be immediately clear, which parameters are present in the
model. To get a full list of parameters and parameter classes for which
priors can be specified (depending on the model) use function
\code{\link{get_prior}}.
}
\section{Functions}{
\itemize{
\item \code{prior}: Alias of \code{set_prior} allowing to
specify arguments as expressions without quotation marks.
\item \code{prior_}: Alias of \code{set_prior} allowing to specify
arguments as as one-sided formulas or wrapped in \code{quote}.
\item \code{prior_string}: Alias of \code{set_prior} allowing to
specify arguments as strings.
\item \code{empty_prior}: Create an empty \code{brmsprior} object.
}}
\examples{
## use alias functions
(prior1 <- prior(cauchy(0, 1), class = sd))
(prior2 <- prior_(~cauchy(0, 1), class = ~sd))
(prior3 <- prior_string("cauchy(0, 1)", class = "sd"))
identical(prior1, prior2)
identical(prior1, prior3)
# check which parameters can have priors
get_prior(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = cumulative())
# define some priors
bprior <- c(prior_string("normal(0,10)", class = "b"),
prior(normal(1,2), class = b, coef = treat),
prior_(~cauchy(0,2), class = ~sd,
group = ~subject, coef = ~Intercept))
# verify that the priors indeed found their way into Stan's model code
make_stancode(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = cumulative(),
prior = bprior)
# use the horseshoe prior to model sparsity in regression coefficients
make_stancode(count ~ zAge + zBase * Trt,
data = epilepsy, family = poisson(),
prior = set_prior("horseshoe(3)"))
# fix certain priors to constants
bprior <- prior(constant(1), class = "b") +
prior(constant(2), class = "b", coef = "zBase") +
prior(constant(0.5), class = "sd")
make_stancode(count ~ zAge + zBase + (1 | patient),
data = epilepsy, prior = bprior)
# pass priors to Stan without checking
prior <- prior_string("target += normal_lpdf(b[1] | 0, 1)", check = FALSE)
make_stancode(count ~ Trt, data = epilepsy, prior = prior)
}
\references{
Gelman A. (2006). Prior distributions for variance parameters in hierarchical models.
Bayesian analysis, 1(3), 515 -- 534.
}
\seealso{
\code{\link{get_prior}}
}
brms/man/is.brmsprior.Rd 0000644 0001762 0000144 00000000470 13623751614 014721 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/priors.R
\name{is.brmsprior}
\alias{is.brmsprior}
\title{Checks if argument is a \code{brmsprior} object}
\usage{
is.brmsprior(x)
}
\arguments{
\item{x}{An \R object}
}
\description{
Checks if argument is a \code{brmsprior} object
}
brms/man/diagnostic-quantities.Rd 0000644 0001762 0000144 00000002754 13601151454 016577 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diagnostics.R
\name{diagnostic-quantities}
\alias{diagnostic-quantities}
\alias{log_posterior}
\alias{nuts_params}
\alias{rhat}
\alias{neff_ratio}
\alias{log_posterior.brmsfit}
\alias{nuts_params.brmsfit}
\alias{rhat.brmsfit}
\alias{neff_ratio.brmsfit}
\title{Extract Diagnostic Quantities of \pkg{brms} Models}
\usage{
\method{log_posterior}{brmsfit}(object, ...)
\method{nuts_params}{brmsfit}(object, pars = NULL, ...)
\method{rhat}{brmsfit}(object, pars = NULL, ...)
\method{neff_ratio}{brmsfit}(object, pars = NULL, ...)
}
\arguments{
\item{object}{A \code{brmsfit} object.}
\item{...}{Arguments passed to individual methods.}
\item{pars}{An optional character vector of parameter names.
For \code{nuts_params} these will be NUTS sampler parameter
names rather than model parameters. If pars is omitted
all parameters are included.}
}
\value{
The exact form of the output depends on the method.
}
\description{
Extract quantities that can be used to diagnose sampling behavior
of the algorithms applied by \pkg{Stan} at the back-end of \pkg{brms}.
}
\details{
For more details see
\code{\link[bayesplot:bayesplot-extractors]{bayesplot-extractors}}.
}
\examples{
\dontrun{
fit <- brm(time ~ age * sex, data = kidney)
lp <- log_posterior(fit)
head(lp)
np <- nuts_params(fit)
str(np)
# extract the number of divergence transitions
sum(subset(np, Parameter == "divergent__")$Value)
head(rhat(fit))
head(neff_ratio(fit))
}
}
brms/man/add_criterion.Rd 0000644 0001762 0000144 00000004503 13605604042 015067 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{add_criterion}
\alias{add_criterion}
\alias{add_criterion.brmsfit}
\title{Add model fit criteria to model objects}
\usage{
add_criterion(x, ...)
\method{add_criterion}{brmsfit}(
x,
criterion,
model_name = NULL,
overwrite = FALSE,
file = NULL,
force_save = FALSE,
...
)
}
\arguments{
\item{x}{An \R object typically of class \code{brmsfit}.}
\item{...}{Further arguments passed to the underlying
functions computing the model fit criteria.}
\item{criterion}{Names of model fit criteria
to compute. Currently supported are \code{"loo"},
\code{"waic"}, \code{"kfold"}, \code{"loo_subsample"},
\code{"bayes_R2"} (Bayesian R-squared),
\code{"loo_R2"} (LOO-adjusted R-squared), and
\code{"marglik"} (log marginal likelihood).}
\item{model_name}{Optional name of the model. If \code{NULL}
(the default) the name is taken from the call to \code{x}.}
\item{overwrite}{Logical; Indicates if already stored fit
indices should be overwritten. Defaults to \code{FALSE}.}
\item{file}{Either \code{NULL} or a character string. In the latter case, the
fitted model object including the newly added criterion values is saved via
\code{\link{saveRDS}} in a file named after the string supplied in
\code{file}. The \code{.rds} extension is added automatically. If \code{x}
was already stored in a file before, the file name will be reused
automatically (with a message) unless overwritten by \code{file}. In any
case, \code{file} only applies if new criteria were actually added via
\code{add_criterion} or if \code{force_save} was set to \code{TRUE}.}
\item{force_save}{Logical; only relevant if \code{file} is specified and
ignored otherwise. If \code{TRUE}, the fitted model object will be saved
regardless of whether new criteria were added via \code{add_criterion}.}
}
\value{
An object of the same class as \code{x}, but
with model fit criteria added for later usage.
}
\description{
Add model fit criteria to model objects
}
\details{
Functions \code{add_loo} and \code{add_waic} are aliases of
\code{add_criterion} with fixed values for the \code{criterion} argument.
}
\examples{
\dontrun{
fit <- brm(count ~ Trt, data = epilepsy)
# add both LOO and WAIC at once
fit <- add_criterion(fit, c("loo", "waic"))
print(fit$criteria$loo)
print(fit$criteria$waic)
}
}
brms/man/vcov.brmsfit.Rd 0000644 0001762 0000144 00000002102 13565500267 014705 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsfit-methods.R
\name{vcov.brmsfit}
\alias{vcov.brmsfit}
\title{Covariance and Correlation Matrix of Population-Level Effects}
\usage{
\method{vcov}{brmsfit}(object, correlation = FALSE, pars = NULL, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{correlation}{Logical; if \code{FALSE} (the default), compute
the covariance matrix, if \code{TRUE}, compute the correlation matrix.}
\item{pars}{Optional names of coefficients to extract.
By default, all coefficients are extracted.}
\item{...}{Currently ignored.}
}
\value{
covariance or correlation matrix of population-level parameters
}
\description{
Get a point estimate of the covariance or
correlation matrix of population-level parameters
}
\details{
Estimates are obtained by calculating the maximum likelihood
covariances (correlations) of the posterior samples.
}
\examples{
\dontrun{
fit <- brm(count ~ zAge + zBase * Trt + (1+Trt|visit),
data = epilepsy, family = gaussian(), chains = 2)
vcov(fit)
}
}
brms/man/mvbrmsformula.Rd 0000644 0001762 0000144 00000002745 13565500267 015174 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsformula.R
\name{mvbrmsformula}
\alias{mvbrmsformula}
\alias{mvbf}
\title{Set up a multivariate model formula for use in \pkg{brms}}
\usage{
mvbrmsformula(..., flist = NULL, rescor = NULL)
}
\arguments{
\item{...}{Objects of class \code{formula} or \code{brmsformula},
each specifying a univariate model. See \code{\link{brmsformula}}
for details on how to specify univariate models.}
\item{flist}{Optional list of formulas, which are treated in the
same way as formulas passed via the \code{...} argument.}
\item{rescor}{Logical; Indicates if residual correlation between
the response variables should be modeled. Currently, this is only
possible in multivariate \code{gaussian} and \code{student} models.
If \code{NULL} (the default), \code{rescor} is internally set to
\code{TRUE} when possible.}
}
\value{
An object of class \code{mvbrmsformula}, which
is essentially a \code{list} containing all model formulas
as well as some additional information for multivariate models.
}
\description{
Set up a multivariate model formula for use in the \pkg{brms} package
allowing to define (potentially non-linear) additive multilevel
models for all parameters of the assumed response distributions.
}
\details{
See \code{vignette("brms_multivariate")} for a case study.
}
\examples{
bf1 <- bf(y1 ~ x + (1|g))
bf2 <- bf(y2 ~ s(z))
mvbf(bf1, bf2)
}
\seealso{
\code{\link{brmsformula}}, \code{\link{brmsformula-helpers}}
}
brms/man/make_standata.Rd 0000644 0001762 0000144 00000011545 13611651307 015064 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_standata.R
\name{make_standata}
\alias{make_standata}
\title{Data for \pkg{brms} Models}
\usage{
make_standata(
formula,
data,
family = gaussian(),
prior = NULL,
data2 = NULL,
autocor = NULL,
cov_ranef = NULL,
sample_prior = c("no", "yes", "only"),
stanvars = NULL,
knots = NULL,
check_response = TRUE,
only_response = FALSE,
control = list(),
...
)
}
\arguments{
\item{formula}{An object of class \code{\link[stats:formula]{formula}},
\code{\link{brmsformula}}, or \code{\link{mvbrmsformula}} (or one that can
be coerced to that classes): A symbolic description of the model to be
fitted. The details of model specification are explained in
\code{\link{brmsformula}}.}
\item{data}{An object of class \code{data.frame} (or one that can be coerced
to that class) containing data of all variables used in the model.}
\item{family}{A description of the response distribution and link function to
be used in the model. This can be a family function, a call to a family
function or a character string naming the family. Every family function has
a \code{link} argument allowing to specify the link function to be applied
on the response variable. If not specified, default links are used. For
details of supported families see \code{\link{brmsfamily}}. By default, a
linear \code{gaussian} model is applied. In multivariate models,
\code{family} might also be a list of families.}
\item{prior}{One or more \code{brmsprior} objects created by
\code{\link{set_prior}} or related functions and combined using the
\code{c} method or the \code{+} operator. See also \code{\link{get_prior}}
for more help.}
\item{data2}{A named \code{list} of objects containing data, which
cannot be passed via argument \code{data}. Required for some objects
used in autocorrelation structures to specify dependency structures.}
\item{autocor}{(Deprecated) An optional \code{\link{cor_brms}} object
describing the correlation structure within the response variable (i.e.,
the 'autocorrelation'). See the documentation of \code{\link{cor_brms}} for
a description of the available correlation structures. Defaults to
\code{NULL}, corresponding to no correlations. In multivariate models,
\code{autocor} might also be a list of autocorrelation structures.
It is now recommend to specify autocorrelation terms directly
within \code{formula}. See \code{\link{brmsformula}} for more details.}
\item{cov_ranef}{A list of matrices that are proportional to the (within)
covariance structure of the group-level effects. The names of the matrices
should correspond to columns in \code{data} that are used as grouping
factors. All levels of the grouping factor should appear as rownames of the
corresponding matrix. This argument can be used, among others to model
pedigrees and phylogenetic effects. See
\code{vignette("brms_phylogenetics")} for more details.}
\item{sample_prior}{Indicate if samples from priors should be drawn
additionally to the posterior samples (defaults to \code{"no"}). Among
others, these samples can be used to calculate Bayes factors for point
hypotheses via \code{\link{hypothesis}}. Please note that improper priors
are not sampled, including the default improper priors used by \code{brm}.
See \code{\link{set_prior}} on how to set (proper) priors. Please also note
that prior samples for the overall intercept are not obtained by default for
technical reasons. See \code{\link{brmsformula}} how to obtain prior samples
for the intercept. If \code{sample_prior} is set to \code{"only"}, samples
are drawn solely from the priors ignoring the likelihood, which allows among
others to generate samples from the prior predictive distribution. In this
case, all parameters must have proper priors.}
\item{stanvars}{An optional \code{stanvars} object generated by function
\code{\link{stanvar}} to define additional variables for use in
\pkg{Stan}'s program blocks.}
\item{knots}{Optional list containing user specified knot values to be used
for basis construction of smoothing terms. See
\code{\link[mgcv:gamm]{gamm}} for more details.}
\item{check_response}{Logical; check validity of the response?}
\item{only_response}{Logical; extract data related to the response only?}
\item{control}{A named list currently for internal usage only}
\item{...}{Other potential arguments}
}
\value{
A named list of objects containing the required data
to fit a \pkg{brms} model with \pkg{Stan}.
}
\description{
Generate data for \pkg{brms} models to be passed to \pkg{Stan}
}
\examples{
data1 <- make_standata(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "cumulative")
names(data1)
data2 <- make_standata(count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = "poisson")
names(data2)
}
\author{
Paul-Christian Buerkner \email{paul.buerkner@gmail.com}
}
brms/man/prior_summary.brmsfit.Rd 0000644 0001762 0000144 00000002116 13601151454 016634 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.R
\name{prior_summary.brmsfit}
\alias{prior_summary.brmsfit}
\alias{prior_summary}
\title{Extract Priors of a Bayesian Model Fitted with \pkg{brms}}
\usage{
\method{prior_summary}{brmsfit}(object, all = TRUE, ...)
}
\arguments{
\item{object}{A \code{brmsfit} object}
\item{all}{Logical; Show all parameters in the model which may have
priors (\code{TRUE}) or only those with proper priors (\code{FALSE})?}
\item{...}{Further arguments passed to or from other methods.}
}
\value{
For \code{brmsfit} objects, an object of class \code{brmsprior}.
}
\description{
Extract Priors of a Bayesian Model Fitted with \pkg{brms}
}
\examples{
\dontrun{
fit <- brm(count ~ zAge + zBase * Trt
+ (1|patient) + (1|obs),
data = epilepsy, family = poisson(),
prior = c(prior(student_t(5,0,10), class = b),
prior(cauchy(0,2), class = sd)))
prior_summary(fit)
prior_summary(fit, all = FALSE)
print(prior_summary(fit, all = FALSE), show_df = FALSE)
}
}
brms/man/nsamples.brmsfit.Rd 0000644 0001762 0000144 00000001210 13565500267 015551 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsfit-methods.R
\name{nsamples.brmsfit}
\alias{nsamples.brmsfit}
\alias{nsamples}
\title{Number of Posterior Samples}
\usage{
\method{nsamples}{brmsfit}(object, subset = NULL, incl_warmup = FALSE, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{subset}{An optional integer vector defining a subset of samples
to be considered.}
\item{incl_warmup}{A flag indicating whether to also count warmup / burn-in
samples.}
\item{...}{Currently ignored.}
}
\description{
Extract the number of posterior samples stored in a fitted Bayesian model.
}
brms/man/fitted.brmsfit.Rd 0000644 0001762 0000144 00000010747 13603652171 015220 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pp_expect.R
\name{fitted.brmsfit}
\alias{fitted.brmsfit}
\title{Expected Values of the Posterior Predictive Distribution}
\usage{
\method{fitted}{brmsfit}(
object,
newdata = NULL,
re_formula = NULL,
scale = c("response", "linear"),
resp = NULL,
dpar = NULL,
nlpar = NULL,
nsamples = NULL,
subset = NULL,
sort = FALSE,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used.
\code{NA} values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.}
\item{re_formula}{formula containing group-level effects to be considered in
the prediction. If \code{NULL} (default), include all group-level effects;
if \code{NA}, include no group-level effects.}
\item{scale}{Either \code{"response"} or \code{"linear"}.
If \code{"response"}, results are returned on the scale
of the response variable. If \code{"linear"},
results are returned on the scale of the linear predictor term,
that is without applying the inverse link function or
other transformations.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{dpar}{Optional name of a predicted distributional parameter.
If specified, fitted values of this parameters are returned.}
\item{nlpar}{Optional name of a predicted non-linear parameter.
If specified, fitted values of this parameters are returned.}
\item{nsamples}{Positive integer indicating how many posterior samples should
be used. If \code{NULL} (the default) all samples are used. Ignored if
\code{subset} is not \code{NULL}.}
\item{subset}{A numeric vector specifying the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{sort}{Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order (\code{FALSE}; default) or in the order of the
time series (\code{TRUE}).}
\item{summary}{Should summary statistics be returned
instead of the raw values? Default is \code{TRUE}..}
\item{robust}{If \code{FALSE} (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If \code{TRUE}, the median and the
median absolute deviation (MAD) are applied instead.
Only used if \code{summary} is \code{TRUE}.}
\item{probs}{The percentiles to be computed by the \code{quantile}
function. Only used if \code{summary} is \code{TRUE}.}
\item{...}{Further arguments passed to \code{\link{extract_draws}}
that control several aspects of data validation and prediction.}
}
\value{
An \code{array} of predicted \emph{mean} response values.
If \code{summary = FALSE} the output resembles those of
\code{\link{pp_expect.brmsfit}}.
If \code{summary = TRUE} the output depends on the family: For categorical
and ordinal families, the output is an N x E x C array, where N is the
number of observations, E is the number of summary statistics, and C is the
number of categories. For all other families, the output is an N x E
matrix. The number of summary statistics E is equal to \code{2 +
length(probs)}: The \code{Estimate} column contains point estimates (either
mean or median depending on argument \code{robust}), while the
\code{Est.Error} column contains uncertainty estimates (either standard
deviation or median absolute deviation depending on argument
\code{robust}). The remaining columns starting with \code{Q} contain
quantile estimates as specifed via argument \code{probs}.
In multivariate models, an additional dimension is added to the output
which indexes along the different response variables.
}
\description{
This method is an alias of \code{\link{pp_expect.brmsfit}}
with additional arguments for obtaining summaries of the computed samples.
}
\examples{
\dontrun{
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
## extract fitted values
fitted_values <- fitted(fit)
head(fitted_values)
## plot fitted means against actual response
dat <- as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
ggplot(dat) + geom_point(aes(x = Estimate, y = Y))
}
}
\seealso{
\code{\link{pp_expect.brmsfit}}
}
brms/man/mo.Rd 0000644 0001762 0000144 00000002706 13606623217 012705 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-sp.R
\name{mo}
\alias{mo}
\title{Monotonic Predictors in \pkg{brms} Models}
\usage{
mo(x)
}
\arguments{
\item{x}{An integer variable or an ordered factor to be modeled as monotonic.}
}
\description{
Specify a monotonic predictor term in \pkg{brms}. The function does not
evaluate its arguments -- it exists purely to help set up a model.
}
\details{
For detailed documentation see \code{help(brmsformula)}
as well as \code{vignette("brms_monotonic")}.
}
\examples{
\dontrun{
# generate some data
income_options <- c("below_20", "20_to_40", "40_to_100", "greater_100")
income <- factor(sample(income_options, 100, TRUE),
levels = income_options, ordered = TRUE)
mean_ls <- c(30, 60, 70, 75)
ls <- mean_ls[income] + rnorm(100, sd = 7)
dat <- data.frame(income, ls)
# fit a simple monotonic model
fit1 <- brm(ls ~ mo(income), data = dat)
# summarise the model
summary(fit1)
plot(fit1, N = 6)
plot(marginal_effects(fit1), points = TRUE)
# model interaction with other variables
dat$x <- sample(c("a", "b", "c"), 100, TRUE)
fit2 <- brm(ls ~ mo(income)*x, data = dat)
# summarise the model
summary(fit2)
plot(marginal_effects(fit2), points = TRUE)
}
}
\references{
Bürkner P. C. & Charpentier, E. (in review). Monotonic Effects: A Principled
Approach for Including Ordinal Predictors in Regression Models. PsyArXiv
preprint.
}
\seealso{
\code{\link{brmsformula}}
}
brms/man/is.cor_brms.Rd 0000644 0001762 0000144 00000001000 13567776477 014523 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autocor.R
\name{is.cor_brms}
\alias{is.cor_brms}
\alias{is.cor_arma}
\alias{is.cor_cosy}
\alias{is.cor_sar}
\alias{is.cor_car}
\alias{is.cor_fixed}
\title{Check if argument is a correlation structure}
\usage{
is.cor_brms(x)
is.cor_arma(x)
is.cor_cosy(x)
is.cor_sar(x)
is.cor_car(x)
is.cor_fixed(x)
}
\arguments{
\item{x}{An \R object.}
}
\description{
Check if argument is one of the correlation structures
used in \pkg{brms}.
}
brms/man/cor_car.Rd 0000644 0001762 0000144 00000004550 13611651306 013675 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autocor.R
\name{cor_car}
\alias{cor_car}
\alias{cor_icar}
\title{(Deprecated) Spatial conditional autoregressive (CAR) structures}
\usage{
cor_car(W, formula = ~1, type = "escar")
cor_icar(W, formula = ~1)
}
\arguments{
\item{W}{Adjacency matrix of locations.
All non-zero entries are treated as if the two locations
are adjacent. If \code{formula} contains a grouping factor,
the row names of \code{W} have to match the levels
of the grouping factor.}
\item{formula}{An optional one-sided formula of the form
\code{~ 1 | g}, where \code{g} is a grouping factor mapping
observations to spatial locations. If not specified,
each observation is treated as a separate location.
It is recommended to always specify a grouping factor
to allow for handling of new data in post-processing methods.}
\item{type}{Type of the CAR structure. Currently implemented
are \code{"escar"} (exact sparse CAR), \code{"esicar"}
(exact sparse intrinsic CAR), \code{"icar"} (intrinsic CAR),
and \code{"bym2"}. More information is provided in the 'Details' section.}
}
\description{
These function are deprecated. Please see \code{\link{car}} for the new
syntax. These functions are constructors for the \code{cor_car} class
implementing spatial conditional autoregressive structures.
}
\details{
The \code{escar} and \code{esicar} types are
implemented based on the case study of Max Joseph
(\url{https://github.com/mbjoseph/CARstan}). The \code{icar} and
\code{bym2} type is implemented based on the case study of Mitzi Morris
(\url{http://mc-stan.org/users/documentation/case-studies/icar_stan.html}).
}
\examples{
\dontrun{
# generate some spatial data
east <- north <- 1:10
Grid <- expand.grid(east, north)
K <- nrow(Grid)
# set up distance and neighbourhood matrices
distance <- as.matrix(dist(Grid))
W <- array(0, c(K, K))
W[distance == 1] <- 1
# generate the covariates and response data
x1 <- rnorm(K)
x2 <- rnorm(K)
theta <- rnorm(K, sd = 0.05)
phi <- rmulti_normal(
1, mu = rep(0, K), Sigma = 0.4 * exp(-0.1 * distance)
)
eta <- x1 + x2 + phi
prob <- exp(eta) / (1 + exp(eta))
size <- rep(50, K)
y <- rbinom(n = K, size = size, prob = prob)
dat <- data.frame(y, size, x1, x2)
# fit a CAR model
fit <- brm(y | trials(size) ~ x1 + x2, data = dat,
family = binomial(), autocor = cor_car(W))
summary(fit)
}
}
brms/man/posterior_summary.Rd 0000644 0001762 0000144 00000002607 13601153642 016070 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.R
\name{posterior_summary}
\alias{posterior_summary}
\alias{posterior_summary.default}
\alias{posterior_summary.brmsfit}
\title{Summarize Posterior Samples}
\usage{
posterior_summary(x, ...)
\method{posterior_summary}{default}(x, probs = c(0.025, 0.975), robust = FALSE, ...)
\method{posterior_summary}{brmsfit}(x, pars = NA, probs = c(0.025, 0.975), robust = FALSE, ...)
}
\arguments{
\item{x}{An \R object.}
\item{...}{More arguments passed to or from other methods.}
\item{probs}{The percentiles to be computed by the
\code{quantile} function.}
\item{robust}{If \code{FALSE} (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If \code{TRUE}, the median and the
median absolute deviation (MAD) are applied instead.}
\item{pars}{Names of parameters for which posterior samples
should be returned, as given by a character vector or regular expressions.
By default, all posterior samples of all parameters are extracted.}
}
\value{
A matrix where rows indicate parameters
and columns indicate the summary estimates.
}
\description{
Summarizes posterior samples based on point estimates (mean or median),
estimation errors (SD or MAD) and quantiles.
}
\examples{
\dontrun{
fit <- brm(time ~ age * sex, data = kidney)
posterior_summary(fit)
}
}
brms/man/pp_expect.brmsfit.Rd 0000644 0001762 0000144 00000006775 13601124313 015724 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pp_expect.R
\name{pp_expect.brmsfit}
\alias{pp_expect.brmsfit}
\alias{pp_expect}
\title{Expected Values of the Posterior Predictive Distribution}
\usage{
\method{pp_expect}{brmsfit}(
object,
newdata = NULL,
re_formula = NULL,
re.form = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
nsamples = NULL,
subset = NULL,
sort = FALSE,
...
)
pp_expect(object, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used.
\code{NA} values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.}
\item{re_formula}{formula containing group-level effects to be considered in
the prediction. If \code{NULL} (default), include all group-level effects;
if \code{NA}, include no group-level effects.}
\item{re.form}{Alias of \code{re_formula}.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{dpar}{Optional name of a predicted distributional parameter.
If specified, fitted values of this parameters are returned.}
\item{nlpar}{Optional name of a predicted non-linear parameter.
If specified, fitted values of this parameters are returned.}
\item{nsamples}{Positive integer indicating how many posterior samples should
be used. If \code{NULL} (the default) all samples are used. Ignored if
\code{subset} is not \code{NULL}.}
\item{subset}{A numeric vector specifying the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{sort}{Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order (\code{FALSE}; default) or in the order of the
time series (\code{TRUE}).}
\item{...}{Further arguments passed to \code{\link{extract_draws}}
that control several aspects of data validation and prediction.}
}
\value{
An \code{array} of predicted \emph{mean} response values. For
categorical and ordinal models, the output is an S x N x C array.
Otherwise, the output is an S x N matrix, where S is the number of
posterior samples, N is the number of observations, and C is the number of
categories. In multivariate models, an additional dimension is added to the
output which indexes along the different response variables.
}
\description{
Compute posterior samples of the expected value/mean of the posterior
predictive distribution. Can be performed for the data used to fit the model
(posterior predictive checks) or for new data. By definition, these
predictions have smaller variance than the posterior predictions performed by
the \code{\link{posterior_predict.brmsfit}} method. This is because only the
uncertainty in the mean is incorporated in the samples computed by
\code{pp_expect} while any residual error is ignored. However, the estimated
means of both methods averaged across samples should be very similar.
}
\details{
\code{NA} values within factors in \code{newdata},
are interpreted as if all dummy variables of this factor are
zero. This allows, for instance, to make predictions of the grand mean
when using sum coding.
}
\examples{
\dontrun{
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
## extract fitted values
ppe <- pp_expect(fit)
str(ppe)
}
}
brms/man/cosy.Rd 0000644 0001762 0000144 00000001750 13611651307 013242 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-ac.R
\name{cosy}
\alias{cosy}
\title{Set up COSY correlation structures}
\usage{
cosy(time = NA, gr = NA)
}
\arguments{
\item{time}{An optional time variable specifying the time ordering
of the observations. By default, the existing order of the observations
in the data is used.}
\item{gr}{An optional grouping variable. If specified, the correlation
structure is assumed to apply only to observations within the same grouping
level.}
}
\value{
An object of class \code{'cosy_term'}, which is a list
of arguments to be interpreted by the formula
parsing functions of \pkg{brms}.
}
\description{
Set up a compounds symmetry (COSY) term in \pkg{brms}. The function does
not evaluate its arguments -- it exists purely to help set up a model with
COSY terms.
}
\examples{
\dontrun{
data("lh")
lh <- as.data.frame(lh)
fit <- brm(x ~ cosy(), data = lh)
summary(fit)
}
}
\seealso{
\code{\link{autocor-terms}}
}
brms/man/cor_brms.Rd 0000644 0001762 0000144 00000002122 13611651306 014064 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autocor.R
\name{cor_brms}
\alias{cor_brms}
\alias{cor_brms-class}
\title{(Deprecated) Correlation structure classes for the \pkg{brms} package}
\description{
Classes of correlation structures available in the \pkg{brms} package.
\code{cor_brms} is not a correlation structure itself,
but the class common to all correlation structures implemented in \pkg{brms}.
}
\section{Available correlation structures}{
\describe{
\item{cor_arma}{autoregressive-moving average (ARMA) structure,
with arbitrary orders for the autoregressive and moving
average components}
\item{cor_ar}{autoregressive (AR) structure of arbitrary order}
\item{cor_ma}{moving average (MA) structure of arbitrary order}
\item{cor_car}{Spatial conditional autoregressive (CAR) structure}
\item{cor_sar}{Spatial simultaneous autoregressive (SAR) structure}
\item{cor_fixed}{fixed user-defined covariance structure}
}
}
\seealso{
\code{\link{cor_arma}, \link{cor_ar}, \link{cor_ma},
\link{cor_car}, \link{cor_sar}, \link{cor_fixed}}
}
brms/man/is.mvbrmsterms.Rd 0000644 0001762 0000144 00000000567 13623751613 015271 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsterms.R
\name{is.mvbrmsterms}
\alias{is.mvbrmsterms}
\title{Checks if argument is a \code{mvbrmsterms} object}
\usage{
is.mvbrmsterms(x)
}
\arguments{
\item{x}{An \R object}
}
\description{
Checks if argument is a \code{mvbrmsterms} object
}
\seealso{
\code{\link[brms:parse_bf]{parse_bf}}
}
brms/man/hypothesis.brmsfit.Rd 0000644 0001762 0000144 00000014265 13601110030 016114 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hypothesis.R
\name{hypothesis.brmsfit}
\alias{hypothesis.brmsfit}
\alias{hypothesis}
\alias{hypothesis.default}
\title{Non-Linear Hypothesis Testing}
\usage{
\method{hypothesis}{brmsfit}(
x,
hypothesis,
class = "b",
group = "",
scope = c("standard", "ranef", "coef"),
alpha = 0.05,
seed = NULL,
...
)
hypothesis(x, ...)
\method{hypothesis}{default}(x, hypothesis, alpha = 0.05, ...)
}
\arguments{
\item{x}{An \code{R} object. If it is no \code{brmsfit} object,
it must be coercible to a \code{data.frame}.}
\item{hypothesis}{A character vector specifying one or more
non-linear hypothesis concerning parameters of the model.}
\item{class}{A string specifying the class of parameters being tested.
Default is "b" for population-level effects.
Other typical options are "sd" or "cor".
If \code{class = NULL}, all parameters can be tested
against each other, but have to be specified with their full name
(see also \code{\link[brms:parnames]{parnames}})}
\item{group}{Name of a grouping factor to evaluate only
group-level effects parameters related to this grouping factor.}
\item{scope}{Indicates where to look for the variables specified in
\code{hypothesis}. If \code{"standard"}, use the full parameter names
(subject to the restriction given by \code{class} and \code{group}).
If \code{"coef"} or \code{"ranef"}, compute the hypothesis for all levels
of the grouping factor given in \code{"group"}, based on the
output of \code{\link{coef.brmsfit}} and \code{\link{ranef.brmsfit}},
respectively.}
\item{alpha}{The alpha-level of the tests (default is 0.05;
see 'Details' for more information).}
\item{seed}{A single numeric value passed to \code{\link{set.seed}}
to make results reproducible.}
\item{...}{Currently ignored.}
}
\value{
A \code{\link{brmshypothesis}} object.
}
\description{
Perform non-linear hypothesis testing for all model parameters.
}
\details{
Among others, \code{hypothesis} computes an evidence ratio
(\code{Evid.Ratio}) for each hypothesis. For a one-sided hypothesis, this
is just the posterior probability (\code{Post.Prob}) under the hypothesis
against its alternative. That is, when the hypothesis is of the form
\code{a > b}, the evidence ratio is the ratio of the posterior probability
of \code{a > b} and the posterior probability of \code{a < b}. In this
example, values greater than one indicate that the evidence in favor of
\code{a > b} is larger than evidence in favor of \code{a < b}. For an
two-sided (point) hypothesis, the evidence ratio is a Bayes factor between
the hypothesis and its alternative computed via the Savage-Dickey density
ratio method. That is the posterior density at the point of interest
divided by the prior density at that point. Values greater than one
indicate that evidence in favor of the point hypothesis has increased after
seeing the data. In order to calculate this Bayes factor, all parameters
related to the hypothesis must have proper priors and argument
\code{sample_prior} of function \code{brm} must be set to \code{"yes"}.
Otherwise \code{Evid.Ratio} (and \code{Post.Prob}) will be \code{NA}.
Please note that, for technical reasons, we cannot sample from priors of
certain parameters classes. Most notably, these include overall intercept
parameters (prior class \code{"Intercept"}) as well as group-level
coefficients. When interpreting Bayes factors, make sure that your priors
are reasonable and carefully chosen, as the result will depend heavily on
the priors. In particular, avoid using default priors.
The \code{Evid.Ratio} may sometimes be \code{0} or \code{Inf} implying very
small or large evidence, respectively, in favor of the tested hypothesis.
For one-sided hypotheses pairs, this basically means that all posterior
samples are on the same side of the value dividing the two hypotheses. In
that sense, instead of \code{0} or \code{Inf,} you may rather read it as
\code{Evid.Ratio} smaller \code{1 / S} or greater \code{S}, respectively,
where \code{S} denotes the number of posterior samples used in the
computations.
The argument \code{alpha} specifies the size of the credible interval
(i.e., Bayesian confidence interval). For instance, if we tested a
two-sided hypothesis and set \code{alpha = 0.05} (5\%) an, the credible
interval will contain \code{1 - alpha = 0.95} (95\%) of the posterior
values. Hence, \code{alpha * 100}\% of the posterior values will
lie outside of the credible interval. Although this allows testing of
hypotheses in a similar manner as in the frequentist null-hypothesis
testing framework, we strongly argue against using arbitrary cutoffs (e.g.,
\code{p < .05}) to determine the 'existence' of an effect.
}
\examples{
\dontrun{
## define priors
prior <- c(set_prior("normal(0,2)", class = "b"),
set_prior("student_t(10,0,1)", class = "sigma"),
set_prior("student_t(10,0,1)", class = "sd"))
## fit a linear mixed effects models
fit <- brm(time ~ age + sex + disease + (1 + age|patient),
data = kidney, family = lognormal(),
prior = prior, sample_prior = "yes",
control = list(adapt_delta = 0.95))
## perform two-sided hypothesis testing
(hyp1 <- hypothesis(fit, "sexfemale = age + diseasePKD"))
plot(hyp1)
hypothesis(fit, "exp(age) - 3 = 0", alpha = 0.01)
## perform one-sided hypothesis testing
hypothesis(fit, "diseasePKD + diseaseGN - 3 < 0")
hypothesis(fit, "age < Intercept",
class = "sd", group = "patient")
## test the amount of random intercept variance on all variance
h <- paste("sd_patient__Intercept^2 / (sd_patient__Intercept^2 +",
"sd_patient__age^2 + sigma^2) = 0")
(hyp2 <- hypothesis(fit, h, class = NULL))
plot(hyp2)
## test more than one hypothesis at once
h <- c("diseaseGN = diseaseAN", "2 * diseaseGN - diseasePKD = 0")
(hyp3 <- hypothesis(fit, h))
plot(hyp3, ignore_prior = TRUE)
## compute hypotheses for all levels of a grouping factor
hypothesis(fit, "age = 0", scope = "coef", group = "patient")
## use the default method
dat <- as.data.frame(fit)
hypothesis(dat, "b_age > 0")
}
}
\seealso{
\code{\link{brmshypothesis}}
}
\author{
Paul-Christian Buerkner \email{paul.buerkner@gmail.com}
}
brms/man/family.brmsfit.Rd 0000644 0001762 0000144 00000001132 13601155652 015206 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsfit-methods.R
\name{family.brmsfit}
\alias{family.brmsfit}
\title{Extract Model Family Objects}
\usage{
\method{family}{brmsfit}(object, resp = NULL, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{...}{Currently unused.}
}
\value{
A \code{brmsfamily} object
or a list of such objects for multivariate models.
}
\description{
Extract Model Family Objects
}
brms/man/expp1.Rd 0000644 0001762 0000144 00000000427 13623751614 013327 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/numeric-helpers.R
\name{expp1}
\alias{expp1}
\title{Exponential function plus one.}
\usage{
expp1(x)
}
\arguments{
\item{x}{A numeric or complex vector.}
}
\description{
Computes \code{exp(x) + 1}.
}
brms/man/kfold_predict.Rd 0000644 0001762 0000144 00000003316 13601151454 015073 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kfold.R
\name{kfold_predict}
\alias{kfold_predict}
\title{Predictions from K-Fold Cross-Validation}
\usage{
kfold_predict(x, method = c("predict", "fitted"), resp = NULL, ...)
}
\arguments{
\item{x}{Object of class \code{'kfold'} computed by \code{\link{kfold}}.
For \code{kfold_predict} to work, the fitted model objects need to have
been stored via argument \code{save_fits} of \code{\link{kfold}}.}
\item{method}{The method used to make predictions. Either \code{"predict"}
or \code{"fitted"}. See \code{\link{predict.brmsfit}} for details.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{...}{Further arguments passed to \code{\link{extract_draws}}
that control several aspects of data validation and prediction.}
}
\value{
A \code{list} with two slots named \code{'y'} and \code{'yrep'}.
Slot \code{y} contains the vector of observed responses.
Slot \code{yrep} contains the matrix of predicted responses,
with rows being posterior draws and columns being observations.
}
\description{
Compute and evaluate predictions after performing K-fold
cross-validation via \code{\link{kfold}}.
}
\examples{
\dontrun{
fit <- brm(count ~ zBase * Trt + (1|patient),
data = epilepsy, family = poisson())
# perform k-fold cross validation
(kf <- kfold(fit, save_fits = TRUE, chains = 1))
# define a loss function
rmse <- function(y, yrep) {
yrep_mean <- colMeans(yrep)
sqrt(mean((yrep_mean - y)^2))
}
# predict responses and evaluate the loss
kfp <- kfold_predict(kf)
rmse(y = kfp$y, yrep = kfp$yrep)
}
}
\seealso{
\code{\link{kfold}}
}
brms/man/update.brmsfit_multiple.Rd 0000644 0001762 0000144 00000002145 13601201144 017113 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/update.R
\name{update.brmsfit_multiple}
\alias{update.brmsfit_multiple}
\title{Update \pkg{brms} models based on multiple data sets}
\usage{
\method{update}{brmsfit_multiple}(object, formula., newdata = NULL, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit_multiple}.}
\item{formula.}{Changes to the formula; for details see
\code{\link{update.formula}} and \code{\link{brmsformula}}.}
\item{newdata}{List of \code{data.frames} to update the model with new data.
Currently required even if the original data should be used.}
\item{...}{Other arguments passed to \code{\link{update.brmsfit}}
and \code{\link{brm_multiple}}.}
}
\description{
This method allows to update an existing \code{brmsfit_multiple} object.
}
\examples{
\dontrun{
library(mice)
imp <- mice(nhanes2)
# initially fit the model
fit_imp1 <- brm_multiple(bmi ~ age + hyp + chl, data = imp, chains = 1)
summary(fit_imp1)
# update the model using fewer predictors
fit_imp2 <- update(fit_imp1, formula. = . ~ hyp + chl, newdata = imp)
summary(fit_imp2)
}
}
brms/man/stancode.brmsfit.Rd 0000644 0001762 0000144 00000001175 13606326742 015541 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_stancode.R
\name{stancode.brmsfit}
\alias{stancode.brmsfit}
\alias{stancode}
\title{Extract Stan model code}
\usage{
\method{stancode}{brmsfit}(object, version = TRUE, ...)
stancode(object, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{version}{Logical; indicates if the first line containing
the \pkg{brms} version number should be included.
Defaults to \code{TRUE}.}
\item{...}{Currently ignored.}
}
\value{
Stan model code for further processing.
}
\description{
Extract Stan code that was used to specify the model.
}
brms/man/Dirichlet.Rd 0000644 0001762 0000144 00000001463 13565500267 014203 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{Dirichlet}
\alias{Dirichlet}
\alias{ddirichlet}
\alias{rdirichlet}
\title{The Dirichlet Distribution}
\usage{
ddirichlet(x, alpha, log = FALSE)
rdirichlet(n, alpha)
}
\arguments{
\item{x}{Matrix of quantiles. Each row corresponds to one probability vector.}
\item{alpha}{Matrix of positive shape parameters. Each row corresponds to one
probability vector.}
\item{log}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{n}{Number of samples to draw from the distribution.}
}
\description{
Density function and random number generation for the dirichlet
distribution with shape parameter vector \code{alpha}.
}
\details{
See \code{vignette("brms_families")} for details on the
parameterization.
}
brms/man/print.brmsprior.Rd 0000644 0001762 0000144 00000001021 13565500270 015426 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/priors.R
\name{print.brmsprior}
\alias{print.brmsprior}
\title{Print method for \code{brmsprior} objects}
\usage{
\method{print}{brmsprior}(x, show_df, ...)
}
\arguments{
\item{x}{An object of class \code{brmsprior}.}
\item{show_df}{Logical; Print priors as a single
\code{data.frame} (\code{TRUE}) or as a sequence of
sampling statements (\code{FALSE})?}
\item{...}{Currently ignored.}
}
\description{
Print method for \code{brmsprior} objects
}
brms/man/pp_check.brmsfit.Rd 0000644 0001762 0000144 00000005444 13601151454 015507 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pp_check.R
\name{pp_check.brmsfit}
\alias{pp_check.brmsfit}
\alias{pp_check}
\title{Posterior Predictive Checks for \code{brmsfit} Objects}
\usage{
\method{pp_check}{brmsfit}(
object,
type,
nsamples,
group = NULL,
x = NULL,
newdata = NULL,
resp = NULL,
subset = NULL,
...
)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{type}{Type of the ppc plot as given by a character string.
See \code{\link[bayesplot:PPC-overview]{PPC}} for an overview
of currently supported types. You may also use an invalid
type (e.g. \code{type = "xyz"}) to get a list of supported
types in the resulting error message.}
\item{nsamples}{Positive integer indicating how many
posterior samples should be used.
If \code{NULL} all samples are used. If not specified,
the number of posterior samples is chosen automatically.
Ignored if \code{subset} is not \code{NULL}.}
\item{group}{Optional name of a factor variable in the model
by which to stratify the ppc plot. This argument is required for
ppc \code{*_grouped} types and ignored otherwise.}
\item{x}{Optional name of a variable in the model.
Only used for ppc types having an \code{x} argument
and ignored otherwise.}
\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used.
\code{NA} values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{subset}{A numeric vector specifying the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{...}{Further arguments passed to \code{\link{predict.brmsfit}}
as well as to the PPC function specified in \code{type}.}
}
\value{
A ggplot object that can be further
customized using the \pkg{ggplot2} package.
}
\description{
Perform posterior predictive checks with the help
of the \pkg{bayesplot} package.
}
\details{
For a detailed explanation of each of the ppc functions,
see the \code{\link[bayesplot:PPC-overview]{PPC}}
documentation of the \pkg{\link[bayesplot:bayesplot]{bayesplot}}
package.
}
\examples{
\dontrun{
fit <- brm(count ~ zAge + zBase * Trt
+ (1|patient) + (1|obs),
data = epilepsy, family = poisson())
pp_check(fit) # shows dens_overlay plot by default
pp_check(fit, type = "error_hist", nsamples = 11)
pp_check(fit, type = "scatter_avg", nsamples = 100)
pp_check(fit, type = "stat_2d")
pp_check(fit, type = "rootogram")
pp_check(fit, type = "loo_pit")
## get an overview of all valid types
pp_check(fit, type = "xyz")
}
}
brms/man/conditional_smooths.brmsfit.Rd 0000644 0001762 0000144 00000007245 13601107424 020011 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/conditional_smooths.R
\name{conditional_smooths.brmsfit}
\alias{conditional_smooths.brmsfit}
\alias{marginal_smooths}
\alias{marginal_smooths.brmsfit}
\alias{conditional_smooths}
\title{Display Smooth Terms}
\usage{
\method{conditional_smooths}{brmsfit}(
x,
smooths = NULL,
int_conditions = NULL,
probs = c(0.025, 0.975),
spaghetti = FALSE,
resolution = 100,
too_far = 0,
subset = NULL,
nsamples = NULL,
...
)
conditional_smooths(x, ...)
}
\arguments{
\item{x}{An object of class \code{brmsfit}.}
\item{smooths}{Optional character vector of smooth terms
to display. If \code{NULL} (the default) all smooth terms
are shown.}
\item{int_conditions}{An optional named \code{list} whose elements are numeric
vectors of values of the second variables in two-way interactions.
At these values, predictions are evaluated. The names of
\code{int_conditions} have to match the variable names exactly.
Additionally, the elements of the numeric vectors may be named themselves,
in which case their names appear as labels for the conditions in the plots.
Instead of vectors, functions returning vectors may be passed and are
applied on the original values of the corresponding variable.
If \code{NULL} (the default), predictions are evaluated at the
\eqn{mean} and at \eqn{mean +/- sd}.}
\item{probs}{The quantiles to be used in the computation of credible
intervals (defaults to 2.5 and 97.5 percent quantiles)}
\item{spaghetti}{Logical. Indicates if predictions should
be visualized via spaghetti plots. Only applied for numeric
predictors. If \code{TRUE}, it is recommended
to set argument \code{nsamples} to a relatively small value
(e.g. \code{100}) in order to reduce computation time.}
\item{resolution}{Number of support points used to generate
the plots. Higher resolution leads to smoother plots.
Defaults to \code{100}. If \code{surface} is \code{TRUE},
this implies \code{10000} support points for interaction terms,
so it might be necessary to reduce \code{resolution}
when only few RAM is available.}
\item{too_far}{Positive number.
For surface plots only: Grid points that are too
far away from the actual data points can be excluded from the plot.
\code{too_far} determines what is too far. The grid is scaled into
the unit square and then grid points more than \code{too_far}
from the predictor variables are excluded. By default, all
grid points are used. Ignored for non-surface plots.}
\item{subset}{A numeric vector specifying
the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{nsamples}{Positive integer indicating how many
posterior samples should be used.
If \code{NULL} (the default) all samples are used.
Ignored if \code{subset} is not \code{NULL}.}
\item{...}{Currently ignored.}
}
\value{
For the \code{brmsfit} method,
an object of class \code{brms_conditional_effects}. See
\code{\link{conditional_effects}} for
more details and documentation of the related plotting function.
}
\description{
Display smooth \code{s} and \code{t2} terms of models
fitted with \pkg{brms}.
}
\details{
Two-dimensional smooth terms will be visualized using
either contour or raster plots.
}
\examples{
\dontrun{
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
fit <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
# show all smooth terms
plot(conditional_smooths(fit), rug = TRUE, ask = FALSE)
# show only the smooth term s(x2)
plot(conditional_smooths(fit, smooths = "s(x2)"), ask = FALSE)
# fit and plot a two-dimensional smooth term
fit2 <- brm(y ~ t2(x0, x2), data = dat)
ms <- conditional_smooths(fit2)
plot(ms, stype = "contour")
plot(ms, stype = "raster")
}
}
brms/man/loo_predict.brmsfit.Rd 0000644 0001762 0000144 00000006205 13601151454 016232 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo_predict.R
\name{loo_predict.brmsfit}
\alias{loo_predict.brmsfit}
\alias{loo_predict}
\alias{loo_linpred}
\alias{loo_predictive_interval}
\alias{loo_linpred.brmsfit}
\alias{loo_predictive_interval.brmsfit}
\title{Compute Weighted Expectations Using LOO}
\usage{
\method{loo_predict}{brmsfit}(
object,
type = c("mean", "var", "quantile"),
probs = 0.5,
psis_object = NULL,
resp = NULL,
...
)
\method{loo_linpred}{brmsfit}(
object,
type = c("mean", "var", "quantile"),
probs = 0.5,
psis_object = NULL,
resp = NULL,
...
)
\method{loo_predictive_interval}{brmsfit}(object, prob = 0.9, psis_object = NULL, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{type}{The statistic to be computed on the results.
Can by either \code{"mean"} (default), \code{"var"}, or
\code{"quantile"}.}
\item{probs}{A vector of quantiles to compute.
Only used if \code{type = quantile}.}
\item{psis_object}{An optional object returend by \code{\link[loo]{psis}}.
If \code{psis_object} is missing then \code{\link[loo]{psis}} is executed
internally, which may be time consuming for models fit to very large datasets.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{...}{Optional arguments passed to the underlying methods that is
\code{\link[brms:log_lik.brmsfit]{log_lik}}, as well as
\code{\link[brms:posterior_predict.brmsfit]{posterior_predict}} or
\code{\link[brms:posterior_linpred.brmsfit]{posterior_linpred}}.}
\item{prob}{For \code{loo_predictive_interval}, a scalar in \eqn{(0,1)}
indicating the desired probability mass to include in the intervals. The
default is \code{prob = 0.9} (\eqn{90}\% intervals).}
}
\value{
\code{loo_predict} and \code{loo_linpred} return a vector with one
element per observation. The only exception is if \code{type = "quantile"}
and \code{length(probs) >= 2}, in which case a separate vector for each
element of \code{probs} is computed and they are returned in a matrix with
\code{length(probs)} rows and one column per observation.
\code{loo_predictive_interval} returns a matrix with one row per
observation and two columns.
\code{loo_predictive_interval(..., prob = p)} is equivalent to
\code{loo_predict(..., type = "quantile", probs = c(a, 1-a))} with
\code{a = (1 - p)/2}, except it transposes the result and adds informative
column names.
}
\description{
These functions are wrappers around the \code{\link[loo]{E_loo}}
function of the \pkg{loo} package.
}
\examples{
\dontrun{
## data from help("lm")
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
d <- data.frame(
weight = c(ctl, trt),
group = gl(2, 10, 20, labels = c("Ctl", "Trt"))
)
fit <- brm(weight ~ group, data = d)
loo_predictive_interval(fit, prob = 0.8)
## optionally log-weights can be pre-computed and reused
psis <- loo::psis(-log_lik(fit), cores = 2)
loo_predictive_interval(fit, prob = 0.8, psis_object = psis)
loo_predict(fit, type = "var", psis_object = psis)
}
}
brms/man/fcor.Rd 0000644 0001762 0000144 00000002167 13611651307 013221 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-ac.R
\name{fcor}
\alias{fcor}
\title{Fixed residual correlation (FCOR) structures}
\usage{
fcor(M)
}
\arguments{
\item{M}{Known correlation/covariance matrix of the response variable.
If a vector is passed, it will be used as diagonal entries
(variances) and correlations/covariances will be set to zero.
The actual covariance matrix used in the likelihood is obtained
by multiplying \code{M} by the square of the residual standard
deviation parameter \code{sigma} estimated as part of the model.}
}
\value{
An object of class \code{'fcor_term'}, which is a list
of arguments to be interpreted by the formula
parsing functions of \pkg{brms}.
}
\description{
Set up a fixed residual correlation (FCOR) term in \pkg{brms}. The function
does not evaluate its arguments -- it exists purely to help set up a model
with FCOR terms.
}
\examples{
\dontrun{
dat <- data.frame(y = rnorm(3))
V <- cbind(c(0.5, 0.3, 0.2), c(0.3, 1, 0.1), c(0.2, 0.1, 0.2))
fit <- brm(y ~ 1 + fcor(V), data = dat, data2 = list(V = V))
}
}
\seealso{
\code{\link{autocor-terms}}
}
brms/man/Shifted_Lognormal.Rd 0000644 0001762 0000144 00000002631 13565500267 015672 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{Shifted_Lognormal}
\alias{Shifted_Lognormal}
\alias{dshifted_lnorm}
\alias{pshifted_lnorm}
\alias{qshifted_lnorm}
\alias{rshifted_lnorm}
\title{The Shifted Log Normal Distribution}
\usage{
dshifted_lnorm(x, meanlog = 0, sdlog = 1, shift = 0, log = FALSE)
pshifted_lnorm(
q,
meanlog = 0,
sdlog = 1,
shift = 0,
lower.tail = TRUE,
log.p = FALSE
)
qshifted_lnorm(
p,
meanlog = 0,
sdlog = 1,
shift = 0,
lower.tail = TRUE,
log.p = FALSE
)
rshifted_lnorm(n, meanlog = 0, sdlog = 1, shift = 0)
}
\arguments{
\item{x, q}{Vector of quantiles.}
\item{meanlog}{Vector of means.}
\item{sdlog}{Vector of standard deviations.}
\item{shift}{Vector of shifts.}
\item{log}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{lower.tail}{Logical; If \code{TRUE} (default), return P(X <= x).
Else, return P(X > x) .}
\item{log.p}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{p}{Vector of probabilities.}
\item{n}{Number of samples to draw from the distribution.}
}
\description{
Density, distribution function, quantile function and random generation
for the shifted log normal distribution with mean \code{meanlog},
standard deviation \code{sdlog}, and shift parameter \code{shift}.
}
\details{
See \code{vignette("brms_families")} for details
on the parameterization.
}
brms/man/parnames.Rd 0000644 0001762 0000144 00000000705 13623751614 014077 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior_samples.R
\name{parnames}
\alias{parnames}
\alias{parnames.brmsfit}
\title{Extract Parameter Names}
\usage{
parnames(x, ...)
}
\arguments{
\item{x}{An \R object}
\item{...}{Further arguments passed to or from other methods.}
}
\value{
A character vector containing the parameter names of the model.
}
\description{
Extract all parameter names of a given model.
}
brms/man/ar.Rd 0000644 0001762 0000144 00000003263 13611651307 012670 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-ac.R
\name{ar}
\alias{ar}
\title{Set up AR(p) correlation structures}
\usage{
ar(time = NA, gr = NA, p = 1, cov = FALSE)
}
\arguments{
\item{time}{An optional time variable specifying the time ordering
of the observations. By default, the existing order of the observations
in the data is used.}
\item{gr}{An optional grouping variable. If specified, the correlation
structure is assumed to apply only to observations within the same grouping
level.}
\item{p}{A non-negative integer specifying the autoregressive (AR)
order of the ARMA structure. Default is \code{1}.}
\item{cov}{A flag indicating whether ARMA effects should be estimated by
means of residual covariance matrices. This is currently only possible for
stationary ARMA effects of order 1. If the model family does not have
natural residuals, latent residuals are added automatically. If
\code{FALSE} (the default), a regression formulation is used that is
considerably faster and allows for ARMA effects of order higher than 1 but
is only available for \code{gaussian} models and some of its
generalizations.}
}
\value{
An object of class \code{'arma_term'}, which is a list
of arguments to be interpreted by the formula
parsing functions of \pkg{brms}.
}
\description{
Set up an autoregressive (AR) term of order p in \pkg{brms}. The function
does not evaluate its arguments -- it exists purely to help set up a model
with AR terms.
}
\examples{
\dontrun{
data("LakeHuron")
LakeHuron <- as.data.frame(LakeHuron)
fit <- brm(x ~ ar(p = 2), data = LakeHuron)
summary(fit)
}
}
\seealso{
\code{\link{autocor-terms}}, \code{\link{arma}}, \code{\link{ma}}
}
brms/man/ngrps.brmsfit.Rd 0000644 0001762 0000144 00000000755 13623751613 015073 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsfit-methods.R
\name{ngrps.brmsfit}
\alias{ngrps.brmsfit}
\alias{ngrps}
\title{Number of Grouping Factor Levels}
\usage{
\method{ngrps}{brmsfit}(object, ...)
ngrps(object, ...)
}
\arguments{
\item{object}{An \R object.}
\item{...}{Currently ignored.}
}
\value{
A named list containing the number of levels per
grouping factor.
}
\description{
Extract the number of levels of one or more grouping factors.
}
brms/man/is.brmsfit_multiple.Rd 0000644 0001762 0000144 00000000542 13623751613 016262 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsfit-class.R
\name{is.brmsfit_multiple}
\alias{is.brmsfit_multiple}
\title{Checks if argument is a \code{brmsfit_multiple} object}
\usage{
is.brmsfit_multiple(x)
}
\arguments{
\item{x}{An \R object}
}
\description{
Checks if argument is a \code{brmsfit_multiple} object
}
brms/man/autocor-terms.Rd 0000644 0001762 0000144 00000002531 13611651307 015067 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-ac.R
\name{autocor-terms}
\alias{autocor-terms}
\title{Autocorrelation structures}
\description{
Specify autocorrelation terms in \pkg{brms} models. Currently supported terms
are \code{\link{arma}}, \code{\link{ar}}, \code{\link{ma}},
\code{\link{cosy}}, \code{\link{sar}}, \code{\link{car}}, and
\code{\link{fcor}}. Terms can be directly specified within the formula, or
passed to the \code{autocor} argument of \code{\link{brmsformula}} in the
form of a one-sided formula. For deprecated ways of specifying
autocorrelation terms, see \code{\link{cor_brms}}.
}
\details{
The autocor term functions are almost solely useful when called in
formulas passed to the \pkg{brms} package. They do not evaluate its
arguments -- but exist purely to help set up a model with autocorrelation
terms.
}
\examples{
# specify autocor terms within the formula
y ~ x + arma(p = 1, q = 1) + car(M)
# specify autocor terms in the 'autocor' argument
bf(y ~ x, autocor = ~ arma(p = 1, q = 1) + car(M))
# specify autocor terms via 'acformula'
bf(y ~ x) + acformula(~ arma(p = 1, q = 1) + car(M))
}
\seealso{
\code{\link{brmsformula}}, \code{\link{acformula}},
\code{\link{arma}}, \code{\link{ar}}, \code{\link{ma}},
\code{\link{cosy}}, \code{\link{sar}}, \code{\link{car}},
\code{\link{fcor}}
}
brms/man/ZeroInflated.Rd 0000644 0001762 0000144 00000003457 13565500267 014667 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{ZeroInflated}
\alias{ZeroInflated}
\alias{dzero_inflated_poisson}
\alias{pzero_inflated_poisson}
\alias{dzero_inflated_negbinomial}
\alias{pzero_inflated_negbinomial}
\alias{dzero_inflated_binomial}
\alias{pzero_inflated_binomial}
\alias{dzero_inflated_beta}
\alias{pzero_inflated_beta}
\title{Zero-Inflated Distributions}
\usage{
dzero_inflated_poisson(x, lambda, zi, log = FALSE)
pzero_inflated_poisson(q, lambda, zi, lower.tail = TRUE, log.p = FALSE)
dzero_inflated_negbinomial(x, mu, shape, zi, log = FALSE)
pzero_inflated_negbinomial(q, mu, shape, zi, lower.tail = TRUE, log.p = FALSE)
dzero_inflated_binomial(x, size, prob, zi, log = FALSE)
pzero_inflated_binomial(q, size, prob, zi, lower.tail = TRUE, log.p = FALSE)
dzero_inflated_beta(x, shape1, shape2, zi, log = FALSE)
pzero_inflated_beta(q, shape1, shape2, zi, lower.tail = TRUE, log.p = FALSE)
}
\arguments{
\item{x}{Vector of quantiles.}
\item{zi}{zero-inflation propability}
\item{log}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{q}{Vector of quantiles.}
\item{lower.tail}{Logical; If \code{TRUE} (default), return P(X <= x).
Else, return P(X > x) .}
\item{log.p}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{mu, lambda}{location parameter}
\item{shape, shape1, shape2}{shape parameter}
\item{size}{number of trials}
\item{prob}{probability of success on each trial}
}
\description{
Density and distribution functions for zero-inflated distributions.
}
\details{
The density of a zero-inflated distribution can be specified as follows.
If \eqn{x = 0} set \eqn{f(x) = \theta + (1 - \theta) * g(0)}.
Else set \eqn{f(x) = (1 - \theta) * g(x)},
where \eqn{g(x)} is the density of the non-zero-inflated part.
}
brms/man/predict.brmsfit.Rd 0000644 0001762 0000144 00000011042 13603652171 015360 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior_predict.R
\name{predict.brmsfit}
\alias{predict.brmsfit}
\title{Samples from the Posterior Predictive Distribution}
\usage{
\method{predict}{brmsfit}(
object,
newdata = NULL,
re_formula = NULL,
transform = NULL,
resp = NULL,
negative_rt = FALSE,
nsamples = NULL,
subset = NULL,
sort = FALSE,
ntrys = 5,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used.
\code{NA} values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.}
\item{re_formula}{formula containing group-level effects to be considered in
the prediction. If \code{NULL} (default), include all group-level effects;
if \code{NA}, include no group-level effects.}
\item{transform}{A function or a character string naming
a function to be applied on the predicted responses
before summary statistics are computed.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{negative_rt}{Only relevant for Wiener diffusion models.
A flag indicating whether response times of responses
on the lower boundary should be returned as negative values.
This allows to distinguish responses on the upper and
lower boundary. Defaults to \code{FALSE}.}
\item{nsamples}{Positive integer indicating how many posterior samples should
be used. If \code{NULL} (the default) all samples are used. Ignored if
\code{subset} is not \code{NULL}.}
\item{subset}{A numeric vector specifying the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{sort}{Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order (\code{FALSE}; default) or in the order of the
time series (\code{TRUE}).}
\item{ntrys}{Parameter used in rejection sampling
for truncated discrete models only
(defaults to \code{5}). See Details for more information.}
\item{summary}{Should summary statistics be returned
instead of the raw values? Default is \code{TRUE}.}
\item{robust}{If \code{FALSE} (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If \code{TRUE}, the median and the
median absolute deviation (MAD) are applied instead.
Only used if \code{summary} is \code{TRUE}.}
\item{probs}{The percentiles to be computed by the \code{quantile}
function. Only used if \code{summary} is \code{TRUE}.}
\item{...}{Further arguments passed to \code{\link{extract_draws}}
that control several aspects of data validation and prediction.}
}
\value{
An \code{array} of predicted response values.
If \code{summary = FALSE} the output resembles those of
\code{\link{posterior_predict.brmsfit}}.
If \code{summary = TRUE} the output depends on the family: For categorical
and ordinal families, the output is an N x C matrix, where N is the number
of observations, C is the number of categories, and the values are
predicted category probabilites. For all other families, the output is a N
x E matrix where E = \code{2 + length(probs)} is the number of summary
statistics: The \code{Estimate} column contains point estimates (either
mean or median depending on argument \code{robust}), while the
\code{Est.Error} column contains uncertainty estimates (either standard
deviation or median absolute deviation depending on argument
\code{robust}). The remaining columns starting with \code{Q} contain
quantile estimates as specifed via argument \code{probs}.
}
\description{
This method is an alias of \code{\link{posterior_predict.brmsfit}}
with additional arguments for obtaining summaries of the computed samples.
}
\examples{
\dontrun{
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1 + age || patient),
data = kidney, family = "exponential", inits = "0")
## predicted responses
pp <- predict(fit)
head(pp)
## predicted responses excluding the group-level effect of age
pp <- predict(fit, re_formula = ~ (1 | patient))
head(pp)
## predicted responses of patient 1 for new data
newdata <- data.frame(
sex = factor(c("male", "female")),
age = c(20, 50),
patient = c(1, 1)
)
predict(fit, newdata = newdata)
}
}
\seealso{
\code{\link{posterior_predict.brmsfit}}
}
brms/man/bridge_sampler.brmsfit.Rd 0000644 0001762 0000144 00000004344 13601151454 016710 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bridgesampling.R
\name{bridge_sampler.brmsfit}
\alias{bridge_sampler.brmsfit}
\alias{bridge_sampler}
\title{Log Marginal Likelihood via Bridge Sampling}
\usage{
\method{bridge_sampler}{brmsfit}(samples, ...)
}
\arguments{
\item{samples}{A \code{brmsfit} object.}
\item{...}{Additional arguments passed to
\code{\link[bridgesampling:bridge_sampler]{bridge_sampler.stanfit}}.}
}
\description{
Computes log marginal likelihood via bridge sampling,
which can be used in the computation of bayes factors
and posterior model probabilities.
The \code{brmsfit} method is just a thin wrapper around
the corresponding method for \code{stanfit} objects.
}
\details{
Computing the marginal likelihood requires samples
of all variables defined in Stan's \code{parameters} block
to be saved. Otherwise \code{bridge_sampler} cannot be computed.
Thus, please set \code{save_all_pars = TRUE} in the call to \code{brm},
if you are planning to apply \code{bridge_sampler} to your models.
The computation of marginal likelihoods based on bridge sampling requires
a lot more posterior samples than usual. A good conservative
rule of thump is perhaps 10-fold more samples (read: the default of 4000
samples may not be enough in many cases). If not enough posterior
samples are provided, the bridge sampling algorithm tends to be
unstable leading to considerably different results each time it is run.
We thus recommend running \code{bridge_sampler}
multiple times to check the stability of the results.
More details are provided under
\code{\link[bridgesampling:bridge_sampler]{bridgesampling:bridge_sampler}}.
}
\examples{
\dontrun{
# model with the treatment effect
fit1 <- brm(
count ~ zAge + zBase + Trt,
data = epilepsy, family = negbinomial(),
prior = prior(normal(0, 1), class = b),
save_all_pars = TRUE
)
summary(fit1)
bridge_sampler(fit1)
# model without the treatment effect
fit2 <- brm(
count ~ zAge + zBase,
data = epilepsy, family = negbinomial(),
prior = prior(normal(0, 1), class = b),
save_all_pars = TRUE
)
summary(fit2)
bridge_sampler(fit2)
}
}
\seealso{
\code{
\link[brms:bayes_factor]{bayes_factor},
\link[brms:post_prob]{post_prob}
}
}
brms/man/loo_subsample.brmsfit.Rd 0000644 0001762 0000144 00000003135 13605602346 016577 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{loo_subsample.brmsfit}
\alias{loo_subsample.brmsfit}
\alias{loo_subsample}
\title{Efficient approximate leave-one-out cross-validation (LOO) using subsampling}
\usage{
\method{loo_subsample}{brmsfit}(x, ..., compare = TRUE, resp = NULL, model_names = NULL)
}
\arguments{
\item{x}{A \code{brmsfit} object.}
\item{...}{More \code{brmsfit} objects or further arguments
passed to the underlying post-processing functions.
In particular, see \code{\link{extract_draws}} for further
supported arguments.}
\item{compare}{A flag indicating if the information criteria
of the models should be compared to each other
via \code{\link{loo_compare}}.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{model_names}{If \code{NULL} (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.}
}
\description{
Efficient approximate leave-one-out cross-validation (LOO) using subsampling
}
\details{
More details can be found on
\code{\link[loo:loo_subsample]{loo_subsample}}.
}
\examples{
\dontrun{
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
data = inhaler)
(loo1 <- loo_subsample(fit1))
# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
(loo2 <- loo_subsample(fit2))
# compare both models
loo_compare(loo1, loo2)
}
}
brms/man/posterior_predict.brmsfit.Rd 0000644 0001762 0000144 00000011335 13600774753 017503 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior_predict.R
\name{posterior_predict.brmsfit}
\alias{posterior_predict.brmsfit}
\alias{posterior_predict}
\title{Samples from the Posterior Predictive Distribution}
\usage{
\method{posterior_predict}{brmsfit}(
object,
newdata = NULL,
re_formula = NULL,
re.form = NULL,
transform = NULL,
resp = NULL,
negative_rt = FALSE,
nsamples = NULL,
subset = NULL,
sort = FALSE,
ntrys = 5,
...
)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{newdata}{An optional data.frame for which to evaluate predictions. If
\code{NULL} (default), the original data of the model is used.
\code{NA} values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.}
\item{re_formula}{formula containing group-level effects to be considered in
the prediction. If \code{NULL} (default), include all group-level effects;
if \code{NA}, include no group-level effects.}
\item{re.form}{Alias of \code{re_formula}.}
\item{transform}{A function or a character string naming
a function to be applied on the predicted responses
before summary statistics are computed.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{negative_rt}{Only relevant for Wiener diffusion models.
A flag indicating whether response times of responses
on the lower boundary should be returned as negative values.
This allows to distinguish responses on the upper and
lower boundary. Defaults to \code{FALSE}.}
\item{nsamples}{Positive integer indicating how many posterior samples should
be used. If \code{NULL} (the default) all samples are used. Ignored if
\code{subset} is not \code{NULL}.}
\item{subset}{A numeric vector specifying the posterior samples to be used.
If \code{NULL} (the default), all samples are used.}
\item{sort}{Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order (\code{FALSE}; default) or in the order of the
time series (\code{TRUE}).}
\item{ntrys}{Parameter used in rejection sampling
for truncated discrete models only
(defaults to \code{5}). See Details for more information.}
\item{...}{Further arguments passed to \code{\link{extract_draws}}
that control several aspects of data validation and prediction.}
}
\value{
An \code{array} of predicted response values. If \code{summary =
FALSE}, the output is as an S x N matrix, where S is the number of
posterior samples and N is the number of observations. In multivariate
models, an additional dimension is added to the output which indexes along
the different response variables.
}
\description{
Compute posterior samples of the posterior predictive distribution. Can be
performed for the data used to fit the model (posterior predictive checks) or
for new data. By definition, these samples have higher variance than samples
of the means of the posterior predictive distribution computed by
\code{\link{posterior_predict.brmsfit}}. This is because the residual error
is incorporated in \code{posterior_predict}. However, the estimated means of
both methods averaged across samples should be very similar.
}
\details{
\code{NA} values within factors in \code{newdata},
are interpreted as if all dummy variables of this factor are
zero. This allows, for instance, to make predictions of the grand mean
when using sum coding.
For truncated discrete models only: In the absence of any general algorithm
to sample from truncated discrete distributions, rejection sampling is
applied in this special case. This means that values are sampled until a
value lies within the defined truncation boundaries. In practice, this
procedure may be rather slow (especially in \R). Thus, we try to do
approximate rejection sampling by sampling each value \code{ntrys} times
and then select a valid value. If all values are invalid, the closest
boundary is used, instead. If there are more than a few of these
pathological cases, a warning will occur suggesting to increase argument
\code{ntrys}.
}
\examples{
\dontrun{
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1 + age || patient),
data = kidney, family = "exponential", inits = "0")
## predicted responses
pp <- posterior_predict(fit)
str(pp)
## predicted responses excluding the group-level effect of age
pp <- posterior_predict(fit, re_formula = ~ (1 | patient))
str(pp)
## predicted responses of patient 1 for new data
newdata <- data.frame(
sex = factor(c("male", "female")),
age = c(20, 50),
patient = c(1, 1)
)
pp <- posterior_predict(fit, newdata = newdata)
str(pp)
}
}
brms/man/as.mcmc.brmsfit.Rd 0000644 0001762 0000144 00000002466 13601162150 015250 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior_samples.R
\name{as.mcmc.brmsfit}
\alias{as.mcmc.brmsfit}
\alias{as.mcmc}
\title{Extract posterior samples for use with the \pkg{coda} package}
\usage{
\method{as.mcmc}{brmsfit}(
x,
pars = NA,
fixed = FALSE,
combine_chains = FALSE,
inc_warmup = FALSE,
...
)
}
\arguments{
\item{x}{An \code{R} object typically of class \code{brmsfit}}
\item{pars}{Names of parameters for which posterior samples
should be returned, as given by a character vector or regular expressions.
By default, all posterior samples of all parameters are extracted.}
\item{fixed}{Indicates whether parameter names
should be matched exactly (\code{TRUE}) or treated as
regular expressions (\code{FALSE}). Default is \code{FALSE}.}
\item{combine_chains}{Indicates whether chains should be combined.}
\item{inc_warmup}{Indicates if the warmup samples should be included.
Default is \code{FALSE}. Warmup samples are used to tune the
parameters of the sampling algorithm and should not be analyzed.}
\item{...}{currently unused}
}
\value{
If \code{combine_chains = TRUE} an \code{mcmc} object is returned.
If \code{combine_chains = FALSE} an \code{mcmc.list} object is returned.
}
\description{
Extract posterior samples for use with the \pkg{coda} package
}
brms/man/kfold.brmsfit.Rd 0000644 0001762 0000144 00000013446 13606352357 015045 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kfold.R
\name{kfold.brmsfit}
\alias{kfold.brmsfit}
\alias{kfold}
\title{K-Fold Cross-Validation}
\usage{
\method{kfold}{brmsfit}(
x,
...,
K = 10,
Ksub = NULL,
folds = NULL,
group = NULL,
exact_loo = NULL,
compare = TRUE,
resp = NULL,
model_names = NULL,
save_fits = FALSE
)
}
\arguments{
\item{x}{A \code{brmsfit} object.}
\item{...}{More \code{brmsfit} objects or further arguments
passed to the underlying post-processing functions.
In particular, see \code{\link{extract_draws}} for further
supported arguments.}
\item{K}{The number of subsets of equal (if possible) size
into which the data will be partitioned for performing
\eqn{K}-fold cross-validation. The model is refit \code{K} times, each time
leaving out one of the \code{K} subsets. If \code{K} is equal to the total
number of observations in the data then \eqn{K}-fold cross-validation is
equivalent to exact leave-one-out cross-validation.}
\item{Ksub}{Optional number of subsets (of those subsets defined by \code{K})
to be evaluated. If \code{NULL} (the default), \eqn{K}-fold cross-validation
will be performed on all subsets. If \code{Ksub} is a single integer,
\code{Ksub} subsets (out of all \code{K}) subsets will be randomly chosen.
If \code{Ksub} consists of multiple integers or a one-dimensional array
(created via \code{as.array}) potentially of length one, the corresponding
subsets will be used. This argument is primarily useful, if evaluation of
all subsets is infeasible for some reason.}
\item{folds}{Determines how the subsets are being constructed.
Possible values are \code{NULL} (the default), \code{"stratified"},
\code{"grouped"}, or \code{"loo"}. May also be a vector of length
equal to the number of observations in the data. Alters the way
\code{group} is handled. More information is provided in the 'Details'
section.}
\item{group}{Optional name of a grouping variable or factor in the model.
What exactly is done with this variable depends on argument \code{folds}.
More information is provided in the 'Details' section.}
\item{exact_loo}{Deprecated! Please use \code{folds = "loo"} instead.}
\item{compare}{A flag indicating if the information criteria
of the models should be compared to each other
via \code{\link{loo_compare}}.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{model_names}{If \code{NULL} (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.}
\item{save_fits}{If \code{TRUE}, a component \code{fits} is added to
the returned object to store the cross-validated \code{brmsfit}
objects and the indices of the omitted observations for each fold.
Defaults to \code{FALSE}.}
}
\value{
\code{kfold} returns an object that has a similar structure as the
objects returned by the \code{loo} and \code{waic} methods and
can be used with the same post-processing functions.
}
\description{
Perform exact K-fold cross-validation by refitting the model \eqn{K}
times each leaving out one-\eqn{K}th of the original data.
Folds can be run in parallel using the \pkg{future} package.
}
\details{
The \code{kfold} function performs exact \eqn{K}-fold
cross-validation. First the data are partitioned into \eqn{K} folds
(i.e. subsets) of equal (or as close to equal as possible) size by default.
Then the model is refit \eqn{K} times, each time leaving out one of the
\code{K} subsets. If \eqn{K} is equal to the total number of observations
in the data then \eqn{K}-fold cross-validation is equivalent to exact
leave-one-out cross-validation (to which \code{loo} is an efficient
approximation). The \code{compare_ic} function is also compatible with
the objects returned by \code{kfold}.
The subsets can be constructed in multiple different ways:
\itemize{
\item If both \code{folds} and \code{group} are \code{NULL}, the subsets
are randomly chosen so that they have equal (or as close to equal as
possible) size.
\item If \code{folds} is \code{NULL} but \code{group} is specified, the
data is split up into subsets, each time omitting all observations of one
of the factor levels, while ignoring argument \code{K}.
\item If \code{folds = "stratified"} the subsets are stratified after
\code{group} using \code{\link[loo:kfold-helpers]{loo::kfold_split_stratified}}.
\item If \code{folds = "grouped"} the subsets are split by
\code{group} using \code{\link[loo:kfold-helpers]{loo::kfold_split_grouped}}.
\item If \code{folds = "loo"} exact leave-one-out cross-validation
will be performed and \code{K} will be ignored. Further, if \code{group}
is specified, all observations corresponding to the factor level of the
currently predicted single value are omitted. Thus, in this case, the
predicted values are only a subset of the omitted ones.
\item If \code{folds} is a numeric vector, it must contain one element per
observation in the data. Each element of the vector is an integer in
\code{1:K} indicating to which of the \code{K} folds the corresponding
observation belongs. There are some convenience functions available in
the \pkg{loo} package that create integer vectors to use for this purpose
(see the Examples section below and also the
\link[loo:kfold-helpers]{kfold-helpers} page).
}
}
\examples{
\dontrun{
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient) + (1|obs),
data = epilepsy, family = poisson())
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
# perform 10-fold cross validation
(kfold1 <- kfold(fit1, chains = 1))
# use the future package for parallelization
library(future)
plan(multiprocess)
kfold(fit1, chains = 1)
}
}
\seealso{
\code{\link{loo}}, \code{\link{reloo}}
}
brms/man/waic.brmsfit.Rd 0000644 0001762 0000144 00000005611 13601201144 014642 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{waic.brmsfit}
\alias{waic.brmsfit}
\alias{waic}
\alias{WAIC}
\alias{WAIC.brmsfit}
\title{Widely Applicable Information Criterion (WAIC)}
\usage{
\method{waic}{brmsfit}(
x,
...,
compare = TRUE,
resp = NULL,
pointwise = FALSE,
model_names = NULL
)
}
\arguments{
\item{x}{A \code{brmsfit} object.}
\item{...}{More \code{brmsfit} objects or further arguments
passed to the underlying post-processing functions.
In particular, see \code{\link{extract_draws}} for further
supported arguments.}
\item{compare}{A flag indicating if the information criteria
of the models should be compared to each other
via \code{\link{loo_compare}}.}
\item{resp}{Optional names of response variables. If specified, predictions
are performed only for the specified response variables.}
\item{pointwise}{A flag indicating whether to compute the full
log-likelihood matrix at once or separately for each observation.
The latter approach is usually considerably slower but
requires much less working memory. Accordingly, if one runs
into memory issues, \code{pointwise = TRUE} is the way to go.}
\item{model_names}{If \code{NULL} (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.}
}
\value{
If just one object is provided, an object of class \code{loo}.
If multiple objects are provided, an object of class \code{loolist}.
}
\description{
Compute the widely applicable information criterion (WAIC)
based on the posterior likelihood using the \pkg{loo} package.
For more details see \code{\link[loo:waic]{waic}}.
}
\details{
See \code{\link{loo_compare}} for details on model comparisons.
For \code{brmsfit} objects, \code{WAIC} is an alias of \code{waic}.
Use method \code{\link[brms:add_criterion]{add_criterion}} to store
information criteria in the fitted model object for later usage.
}
\examples{
\dontrun{
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
data = inhaler)
(waic1 <- waic(fit1))
# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
(waic2 <- waic(fit2))
# compare both models
loo_compare(waic1, waic2)
}
}
\references{
Vehtari, A., Gelman, A., & Gabry J. (2016). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC. In Statistics
and Computing, doi:10.1007/s11222-016-9696-4. arXiv preprint arXiv:1507.04544.
Gelman, A., Hwang, J., & Vehtari, A. (2014).
Understanding predictive information criteria for Bayesian models.
Statistics and Computing, 24, 997-1016.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation
and widely applicable information criterion in singular learning theory.
The Journal of Machine Learning Research, 11, 3571-3594.
}
brms/man/cor_sar.Rd 0000644 0001762 0000144 00000003361 13611651306 013714 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autocor.R
\name{cor_sar}
\alias{cor_sar}
\alias{cor_lagsar}
\alias{cor_errorsar}
\title{(Deprecated) Spatial simultaneous autoregressive (SAR) structures}
\usage{
cor_sar(W, type = c("lag", "error"))
cor_lagsar(W)
cor_errorsar(W)
}
\arguments{
\item{W}{An object specifying the spatial weighting matrix.
Can be either the spatial weight matrix itself or an
object of class \code{listw} or \code{nb}, from which
the spatial weighting matrix can be computed.}
\item{type}{Type of the SAR structure. Either \code{"lag"}
(for SAR of the response values) or \code{"error"}
(for SAR of the residuals).}
}
\value{
An object of class \code{cor_sar} to be used in calls to
\code{\link{brm}}.
}
\description{
Thse functions are deprecated. Please see \code{\link{sar}} for the new
syntax. These functions are constructors for the \code{cor_sar} class
implementing spatial simultaneous autoregressive structures.
The \code{lagsar} structure implements SAR of the response values:
\deqn{y = \rho W y + \eta + e}
The \code{errorsar} structure implements SAR of the residuals:
\deqn{y = \eta + u, u = \rho W u + e}
In the above equations, \eqn{\eta} is the predictor term and
\eqn{e} are independent normally or t-distributed residuals.
}
\details{
Currently, only families \code{gaussian} and \code{student}
support SAR structures.
}
\examples{
\dontrun{
data(oldcol, package = "spdep")
fit1 <- brm(CRIME ~ INC + HOVAL, data = COL.OLD,
autocor = cor_lagsar(COL.nb),
chains = 2, cores = 2)
summary(fit1)
plot(fit1)
fit2 <- brm(CRIME ~ INC + HOVAL, data = COL.OLD,
autocor = cor_errorsar(COL.nb),
chains = 2, cores = 2)
summary(fit2)
plot(fit2)
}
}
brms/man/SkewNormal.Rd 0000644 0001762 0000144 00000003377 13565500267 014364 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{SkewNormal}
\alias{SkewNormal}
\alias{dskew_normal}
\alias{pskew_normal}
\alias{qskew_normal}
\alias{rskew_normal}
\title{The Skew-Normal Distribution}
\usage{
dskew_normal(
x,
mu = 0,
sigma = 1,
alpha = 0,
xi = NULL,
omega = NULL,
log = FALSE
)
pskew_normal(
q,
mu = 0,
sigma = 1,
alpha = 0,
xi = NULL,
omega = NULL,
lower.tail = TRUE,
log.p = FALSE
)
qskew_normal(
p,
mu = 0,
sigma = 1,
alpha = 0,
xi = NULL,
omega = NULL,
lower.tail = TRUE,
log.p = FALSE,
tol = 1e-08
)
rskew_normal(n, mu = 0, sigma = 1, alpha = 0, xi = NULL, omega = NULL)
}
\arguments{
\item{x, q}{Vector of quantiles.}
\item{mu}{Vector of mean values.}
\item{sigma}{Vector of standard deviation values.}
\item{alpha}{Vector of skewness values.}
\item{xi}{Optional vector of location values.
If \code{NULL} (the default), will be computed internally.}
\item{omega}{Optional vector of scale values.
If \code{NULL} (the default), will be computed internally.}
\item{log}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{lower.tail}{Logical; If \code{TRUE} (default), return P(X <= x).
Else, return P(X > x) .}
\item{log.p}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{p}{Vector of probabilities.}
\item{tol}{Tolerance of the approximation used in the
computation of quantiles.}
\item{n}{Number of samples to draw from the distribution.}
}
\description{
Density, distribution function, and random generation for the
skew-normal distribution with mean \code{mu},
standard deviation \code{sigma}, and skewness \code{alpha}.
}
\details{
See \code{vignette("brms_families")} for details
on the parameterization.
}
brms/DESCRIPTION 0000644 0001762 0000144 00000004457 13624533241 012740 0 ustar ligges users Package: brms
Encoding: UTF-8
Type: Package
Title: Bayesian Regression Models using 'Stan'
Version: 2.12.0
Date: 2020-02-21
Authors@R: person("Paul-Christian", "Bürkner", email = "paul.buerkner@gmail.com",
role = c("aut", "cre"))
Depends: R (>= 3.5.0), Rcpp (>= 0.12.0), methods
Imports: rstan (>= 2.19.2), ggplot2 (>= 2.0.0), loo (>= 2.2.0), Matrix
(>= 1.1.1), mgcv (>= 1.8-13), rstantools (>= 1.5.1), bayesplot
(>= 1.5.0), shinystan (>= 2.4.0), bridgesampling (>= 0.3-0),
glue (>= 1.3.0), matrixStats, nleqslv, nlme, coda, abind,
future, stats, utils, parallel, grDevices, backports
Suggests: testthat (>= 0.9.1), RWiener, mice, spdep, mnormt, lme4,
MCMCglmm, splines2, ape, arm, statmod, digest, R.rsp, knitr,
rmarkdown
Description: Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: Bürkner (2017)
; Bürkner (2018) ;
Carpenter et al. (2017) .
LazyData: true
NeedsCompilation: no
License: GPL-2
URL: https://github.com/paul-buerkner/brms,
http://discourse.mc-stan.org
BugReports: https://github.com/paul-buerkner/brms/issues
VignetteBuilder: knitr, R.rsp
RoxygenNote: 7.0.2
Packaged: 2020-02-21 13:58:21 UTC; paulb
Author: Paul-Christian Bürkner [aut, cre]
Maintainer: Paul-Christian Bürkner
Repository: CRAN
Date/Publication: 2020-02-23 17:30:09 UTC
brms/build/ 0000755 0001762 0000144 00000000000 13623760774 012333 5 ustar ligges users brms/build/vignette.rds 0000644 0001762 0000144 00000001031 13623760774 014665 0 ustar ligges users TQo0ұJXن~BpN0àJ3 8f rZ#KHJ4Df\pF9!gYoM3hw( D@t ڬ+P qzl9rV//%ށ&ax"r`)
*>o3: aK >N } QWǷP}1tn?1Ӂ7/r gt|~L a[Kar] ar;")
expect_match2(scode, "vector[Kma] ma;")
expect_match2(scode, "target += uniform_lpdf(ar | -1, 1)")
expect_match2(scode, "target += normal_lpdf(ma | 0, 0.5)")
expect_match2(scode,
"- 1 * log_diff_exp(normal_lcdf(1 | 0, 0.5), normal_lcdf(-1 | 0, 0.5))"
)
expect_match2(scode, "target += normal_lpdf(bsp | 0, 5)")
expect_match2(scode, "target += dirichlet_lpdf(simo_1 | con_simo_1)")
expect_match2(scode, "prior_simo_1 = dirichlet_rng(con_simo_1)")
expect_match2(scode, "prior_ar = uniform_rng(-1,1)")
expect_match2(scode, "while (prior_ar < -1 || prior_ar > 1)")
# test for problem described in #213
prior <- c(prior(normal(0, 1), coef = x1),
prior(normal(0, 2), coef = x1, dpar = sigma))
scode <- make_stancode(bf(y ~ x1, sigma ~ x1), dat, prior = prior)
expect_match2(scode, "target += normal_lpdf(b[1] | 0, 1);")
expect_match2(scode, "target += normal_lpdf(b_sigma[1] | 0, 2);")
prior <- c(set_prior("target += normal_lpdf(b[1] | 0, 1)", check = FALSE),
set_prior("", class = "sigma"))
scode <- make_stancode(y ~ x1, dat, prior = prior, sample_prior = TRUE)
expect_match2(scode, "target += normal_lpdf(b[1] | 0, 1)")
expect_true(!grepl("sigma \\|", scode))
prior <- prior(gamma(0, 1), coef = x1)
expect_warning(make_stancode(y ~ x1, dat, prior = prior),
"no natural lower bound")
prior <- prior(uniform(0,5), class = sd)
expect_warning(make_stancode(y ~ x1 + (1|g), dat, prior = prior),
"no natural upper bound")
prior <- prior(uniform(-1, 1), class = cor)
expect_error(
make_stancode(y ~ x1 + (x1|g), dat, prior = prior),
"prior for correlation matrices is the 'lkj' prior"
)
})
test_that("special shrinkage priors appear in the Stan code", {
dat <- data.frame(y = 1:10, x1 = rnorm(10), x2 = rnorm(10))
# horseshoe prior
hs <- horseshoe(7, scale_global = 2, df_global = 3,
df_slab = 6, scale_slab = 3)
scode <- make_stancode(y ~ x1*x2, data = dat,
prior = set_prior(hs),
sample_prior = TRUE)
expect_match2(scode, "vector[Kc] hs_local[2];")
expect_match2(scode, "real hs_global[2];")
expect_match2(scode,
"target += inv_gamma_lpdf(hs_local[2] | 0.5 * hs_df, 0.5 * hs_df);"
)
expect_match2(scode,
"target += inv_gamma_lpdf(hs_global[2] | 0.5 * hs_df_global, 0.5 * hs_df_global);"
)
expect_match2(scode,
"target += inv_gamma_lpdf(hs_c2 | 0.5 * hs_df_slab, 0.5 * hs_df_slab);"
)
expect_match2(scode,
paste0(
"b = horseshoe(zb, hs_local, hs_global, ",
"hs_scale_global * sigma, hs_scale_slab^2 * hs_c2);"
)
)
scode <- make_stancode(y ~ x1*x2, data = dat, poisson(),
prior = prior(horseshoe(scale_global = 3)))
expect_match2(scode,
paste0(
"b = horseshoe(zb, hs_local, hs_global, ",
"hs_scale_global, hs_scale_slab^2 * hs_c2);"
)
)
scode <- make_stancode(x1 ~ mo(y), dat, prior = prior(horseshoe()))
expect_match2(scode, "target += normal_lpdf(zbsp | 0, 1);")
expect_match2(scode,
"target += normal_lpdf(hs_localsp[1] | 0, 1)\n - 1 * log(0.5);"
)
expect_match2(scode,
"target += inv_gamma_lpdf(hs_localsp[2] | 0.5 * hs_df, 0.5 * hs_df);"
)
expect_match2(scode,
paste0(
"bsp = horseshoe(zbsp, hs_localsp, hs_global, ",
"hs_scale_global * sigma, hs_scale_slab^2 * hs_c2);"
)
)
# lasso prior
scode <- make_stancode(y ~ x1*x2, data = dat,
prior = prior(lasso(2, scale = 10)),
sample_prior = TRUE)
expect_match2(scode, "target += chi_square_lpdf(lasso_inv_lambda | lasso_df);")
expect_match2(scode,
"target += double_exponential_lpdf(b | 0, lasso_scale * lasso_inv_lambda);"
)
scode <- make_stancode(x1 ~ mo(y), dat, prior = prior(lasso()))
expect_match2(scode,
"double_exponential_lpdf(bsp | 0, lasso_scale * lasso_inv_lambda)"
)
# horseshoe and lasso prior applied in a non-linear model
hs_a1 <- horseshoe(7, scale_global = 2, df_global = 3)
lasso_a2 <- lasso(2, scale = 10)
scode <- make_stancode(
bf(y ~ a1 + a2, a1 ~ x1, a2 ~ 0 + x2, nl = TRUE),
data = dat, sample_prior = TRUE,
prior = c(set_prior(hs_a1, nlpar = "a1"),
set_prior(lasso_a2, nlpar = "a2"))
)
expect_match2(scode, "vector[K_a1] hs_local_a1[2];")
expect_match2(scode, "real hs_global_a1[2];")
expect_match2(scode,
"target += inv_gamma_lpdf(hs_local_a1[2] | 0.5 * hs_df_a1, 0.5 * hs_df_a1);"
)
expect_match2(scode,
"target += inv_gamma_lpdf(hs_global_a1[2] | 0.5 * hs_df_global_a1, 0.5 * hs_df_global_a1);"
)
expect_match2(scode,
"target += inv_gamma_lpdf(hs_c2_a1 | 0.5 * hs_df_slab_a1, 0.5 * hs_df_slab_a1);"
)
expect_match2(scode,
paste0(
"b_a1 = horseshoe(zb_a1, hs_local_a1, hs_global_a1, ",
"hs_scale_global_a1 * sigma, hs_scale_slab_a1^2 * hs_c2_a1);"
)
)
expect_match2(scode,
"target += chi_square_lpdf(lasso_inv_lambda_a2 | lasso_df_a2);"
)
expect_match2(scode,
"target += double_exponential_lpdf(b_a2 | 0, lasso_scale_a2 * lasso_inv_lambda_a2);"
)
# check error messages
expect_error(make_stancode(y ~ x1*x2, data = dat,
prior = prior(horseshoe(-1))),
"Degrees of freedom of the local priors")
expect_error(make_stancode(y ~ x1*x2, data = dat,
prior = prior(horseshoe(1, -1))),
"Scale of the global prior")
expect_error(make_stancode(y ~ x1*x2, data = dat, prior = prior(lasso(-1))),
"Degrees of freedom of the shrinkage parameter prior")
expect_error(make_stancode(y ~ cs(x1), dat, acat(), prior = prior(lasso())),
"Special priors are not yet allowed")
bprior <- prior(horseshoe()) + prior(normal(0, 1), coef = "y")
expect_error(make_stancode(x1 ~ y, dat, prior = bprior),
"Defining separate priors for single coefficients")
expect_error(make_stancode(x1 ~ y, dat, prior = prior(lasso(), lb = 0)),
"Setting boundaries on coefficients is not allowed")
})
test_that("priors can be fixed to constants", {
dat <- data.frame(y = 1:12, x1 = rnorm(12), x2 = rnorm(12),
g = rep(1:6, each = 2), h = factor(rep(1:2, each = 6)))
prior <- prior(normal(0, 1), b) +
prior(constant(3), b, coef = x1) +
prior(constant(-1), b, coef = x2) +
prior(constant(10), Intercept) +
prior(normal(0, 5), sd) +
prior(constant(1), sd, group = g, coef = x2) +
prior(constant(2), sd, group = g, coef = x1) +
prior(constant(0.3), sigma)
scode <- make_stancode(y ~ x1*x2 + (x1*x2 | g), dat, prior = prior)
expect_match2(scode, "b[1] = 3;")
expect_match2(scode, "b[2] = -1;")
expect_match2(scode, "b[3] = par_b_3;")
expect_match2(scode, "target += normal_lpdf(b[3] | 0, 1);")
expect_match2(scode, "Intercept = 1")
expect_match2(scode, "sd_1[3] = 1;")
expect_match2(scode, "sd_1[2] = 2;")
expect_match2(scode, "sd_1[4] = par_sd_1_4;")
expect_match2(scode, "target += normal_lpdf(sd_1[4] | 0, 5)")
expect_match2(scode, "sigma = 0.3;")
prior <- prior(constant(3))
scode <- make_stancode(y ~ x2 + x1 + cs(g), dat, family = sratio(),
prior = prior)
expect_match2(scode, "b = rep_vector(3, rows(b));")
expect_match2(scode, "bcs = rep_matrix(3, rows(bcs), cols(bcs));")
prior <- prior(normal(0, 3)) +
prior(constant(3), coef = x1) +
prior(constant(-1), coef = g)
scode <- make_stancode(y ~ x1 + cs(x2) + cs(g), dat, family = sratio(),
prior = prior)
expect_match2(scode, "b[1] = 3;")
expect_match2(scode, "bcs[1] = par_bcs_1;")
expect_match2(scode, "target += normal_lpdf(bcs[1] | 0, 3);")
expect_match2(scode, "bcs[2] = rep_row_vector(-1, cols(bcs[2]));")
prior <- prior(constant(3), class = "sd", group = "g") +
prior("constant([[1, 0], [0, 1]])", class = "cor")
scode <- make_stancode(y ~ x1 + (x1 | gr(g, by = h)), dat, prior = prior)
expect_match2(scode, "sd_1 = rep_matrix(3, rows(sd_1), cols(sd_1));")
expect_match2(scode, "L_1[2] = [[1, 0], [0, 1]];")
prior <- prior(constant(0.5), class = lscale, coef = gpx1h1) +
prior(normal(0, 10), class = lscale, coef = gpx1h2)
scode <- make_stancode(y ~ gp(x1, by = h), dat, prior = prior)
expect_match2(scode, "lscale_1[1][1] = 0.5;")
expect_match2(scode, "lscale_1[2][1] = par_lscale_1_2_1;")
expect_match2(scode, "target += normal_lpdf(lscale_1[2][1] | 0, 10)")
# test error messages
prior <- prior(normal(0, 1), Intercept) +
prior(constant(3), Intercept, coef = 2)
expect_error(
make_stancode(y ~ x1, data = dat, family = cumulative(), prior = prior),
"Can either estimate or fix all values"
)
})
test_that("link functions appear in the Stan code", {
dat <- data.frame(y = 1:10, x = rnorm(10))
expect_match2(make_stancode(y ~ s(x), dat, family = poisson()),
"target += poisson_log_lpmf(Y | mu);")
expect_match2(make_stancode(mvbind(y, y + 1) ~ x, dat, family = gaussian("log")),
"mu_y[n] = exp(mu_y[n]);")
expect_match2(make_stancode(y ~ x, dat, family = von_mises(tan_half)),
"mu[n] = inv_tan_half(mu[n]);")
expect_match2(make_stancode(y ~ x, dat, family = weibull()),
"mu[n] = exp(mu[n]) / tgamma(1 + 1 / shape);")
expect_match2(make_stancode(y ~ x, dat, family = exponential("identity")),
"mu[n] = inv(mu[n]);")
expect_match2(make_stancode(y ~ x, dat, family = poisson("sqrt")),
"mu[n] = square(mu[n]);")
expect_match2(make_stancode(y ~ s(x), dat, family = bernoulli()),
"target += bernoulli_logit_lpmf(Y | mu);")
})
test_that("Stan GLM primitives are applied correctly", {
dat <- data.frame(x = rnorm(10), y = 1:10)
scode <- make_stancode(y ~ x, dat, family = gaussian)
expect_match2(scode, "normal_id_glm_lpdf(Y | Xc, Intercept, b, sigma)")
scode <- make_stancode(y ~ x, dat, family = bernoulli)
expect_match2(scode, "bernoulli_logit_glm_lpmf(Y | Xc, Intercept, b)")
scode <- make_stancode(y ~ x, dat, family = poisson)
expect_match2(scode, "poisson_log_glm_lpmf(Y | Xc, Intercept, b)")
scode <- make_stancode(y ~ x, dat, family = negbinomial)
expect_match2(scode,
"neg_binomial_2_log_glm_lpmf(Y | Xc, Intercept, b, shape)"
)
scode <- make_stancode(y ~ 0 + x, dat, family = gaussian)
expect_match2(scode, "normal_id_glm_lpdf(Y | X, 0, b, sigma)")
bform <- bf(y ~ x) + bf(x ~ 1, family = negbinomial()) + set_rescor(FALSE)
scode <- make_stancode(bform, dat, family = gaussian)
expect_match2(scode,
"normal_id_glm_lpdf(Y_y | Xc_y, Intercept_y, b_y, sigma_y)"
)
scode <- make_stancode(bf(y ~ x, decomp = "QR"), dat, family = gaussian)
expect_match2(scode, "normal_id_glm_lpdf(Y | XQ, Intercept, bQ, sigma);")
})
test_that("customized covariances appear in the Stan code", {
scode <- make_stancode(rating ~ treat + period + carry + (1|subject),
data = inhaler, cov_ranef = list(subject = 1))
expect_match2(scode, "r_1_1 = (sd_1[1] * (Lcov_1 * z_1[1]))")
scode <- make_stancode(rating ~ treat + period + carry + (1+carry|subject),
data = inhaler, cov_ranef = list(subject = 1))
expect_match2(scode,
"kronecker(Lcov_1, diag_pre_multiply(sd_1, L_1)) * to_vector(z_1)"
)
expect_match2(scode, "cor_1[choose(k - 1, 2) + j] = Cor_1[j, k];")
scode <- make_stancode(rating ~ treat + period + carry + (1+carry||subject),
data = inhaler, cov_ranef = list(subject = 1))
expect_match2(scode, " r_1_1 = (sd_1[1] * (Lcov_1 * z_1[1]));")
expect_match2(scode, " r_1_2 = (sd_1[2] * (Lcov_1 * z_1[2]));")
})
test_that("truncation appears in the Stan code", {
scode <- make_stancode(time | trunc(0) ~ age + sex + disease,
data = kidney, family = "gamma")
expect_match2(scode, "target += gamma_lpdf(Y[n] | shape, mu[n]) -")
expect_match2(scode, "gamma_lccdf(lb[n] | shape, mu[n]);")
scode <- make_stancode(time | trunc(ub = 100) ~ age + sex + disease,
data = kidney, family = student("log"))
expect_match2(scode, "target += student_t_lpdf(Y[n] | nu, mu[n], sigma) -")
expect_match2(scode, "student_t_lcdf(ub[n] | nu, mu[n], sigma);")
scode <- make_stancode(count | trunc(0, 150) ~ Trt,
data = epilepsy, family = "poisson")
expect_match2(scode, "target += poisson_lpmf(Y[n] | mu[n]) -")
expect_match2(scode,
"log_diff_exp(poisson_lcdf(ub[n] | mu[n]), poisson_lcdf(lb[n] - 1 | mu[n]));"
)
})
test_that("make_stancode handles models without fixed effects", {
expect_match2(make_stancode(count ~ 0 + (1|patient) + (1+Trt|visit),
data = epilepsy, family = "poisson"),
"mu = rep_vector(0, N);")
})
test_that("make_stancode correctly restricts FE parameters", {
data <- data.frame(y = rep(0:1, each = 5), x = rnorm(10))
scode <- make_stancode(y ~ x, data, prior = set_prior("", lb = 2))
expect_match2(scode, "vector[Kc] b")
scode <- make_stancode(
y ~ x, data, prior = set_prior("normal (0, 2)", ub = "4")
)
expect_match2(scode, "vector[Kc] b")
expect_match2(scode, "- 1 * normal_lcdf(4 | 0, 2)")
prior <- set_prior("normal(0,5)", lb = "-3", ub = 5)
scode <- make_stancode(y ~ 0 + x, data, prior = prior)
expect_match2(scode, "vector[K] b")
})
test_that("self-defined functions appear in the Stan code", {
# cauchit link
scode <- make_stancode(rating ~ treat, data = inhaler,
family = bernoulli("cauchit"))
expect_match2(scode, "real inv_cauchit(real y)")
# softplus link
scode <- make_stancode(rating ~ treat, data = inhaler,
family = brmsfamily("poisson", "softplus"))
expect_match2(scode, "real log_expm1(real x)")
# tan_half link
expect_match2(make_stancode(rating ~ treat, data = inhaler,
family = von_mises("tan_half")),
"real inv_tan_half(real y)")
# logm1 link
expect_match2(make_stancode(rating ~ treat, data = inhaler,
family = frechet()),
"real expp1(real y)")
# inverse gaussian models
scode <- make_stancode(time | cens(censored) ~ age, data = kidney,
family = inverse.gaussian)
expect_match2(scode, "real inv_gaussian_lpdf(real y")
expect_match2(scode, "real inv_gaussian_lcdf(real y")
expect_match2(scode, "real inv_gaussian_lccdf(real y")
expect_match2(scode, "real inv_gaussian_vector_lpdf(vector y")
# von Mises models
scode <- make_stancode(time ~ age, data = kidney, family = von_mises)
expect_match2(scode, "real von_mises_real_lpdf(real y")
expect_match2(scode, "real von_mises_vector_lpdf(vector y")
# zero-inflated and hurdle models
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = "zero_inflated_poisson"),
"real zero_inflated_poisson_lpmf(int y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = "zero_inflated_negbinomial"),
"real zero_inflated_neg_binomial_lpmf(int y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = "zero_inflated_binomial"),
"real zero_inflated_binomial_lpmf(int y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = "zero_inflated_beta"),
"real zero_inflated_beta_lpdf(real y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = "zero_one_inflated_beta"),
"real zero_one_inflated_beta_lpdf(real y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = hurdle_poisson()),
"real hurdle_poisson_lpmf(int y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = hurdle_negbinomial),
"real hurdle_neg_binomial_lpmf(int y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = hurdle_gamma("log")),
"real hurdle_gamma_lpdf(real y")
expect_match2(make_stancode(count ~ Trt, data = epilepsy,
family = hurdle_lognormal("identity")),
"real hurdle_lognormal_lpdf(real y")
# linear models with special covariance structures
expect_match2(
make_stancode(rating ~ treat + ar(cov = TRUE), data = inhaler),
"real normal_time_hom_lpdf(vector y"
)
expect_match2(
make_stancode(time ~ age + ar(cov = TRUE), data = kidney,
family = "student"),
"real student_t_time_hom_lpdf(vector y"
)
# ARMA covariance matrices
expect_match2(
make_stancode(rating ~ treat + ar(cov = TRUE), data = inhaler),
"matrix cholesky_cor_ar1(real ar"
)
expect_match2(
make_stancode(time ~ age + ma(cov = TRUE), data = kidney),
"matrix cholesky_cor_ma1(real ma"
)
expect_match2(
make_stancode(time ~ age + arma(cov = TRUE), data = kidney),
"matrix cholesky_cor_arma1(real ar, real ma"
)
# kronecker products
expect_match(make_stancode(rating ~ treat + period + carry + (1+carry|subject),
data = inhaler, cov_ranef = list(subject = 1)),
"matrix as_matrix.*matrix kronecker")
})
test_that("invalid combinations of modeling options are detected", {
data <- data.frame(y1 = rnorm(10), y2 = rnorm(10),
wi = 1:10, ci = sample(-1:1, 10, TRUE))
expect_error(
make_stancode(y1 | cens(ci) ~ y2 + ar(cov = TRUE), data = data),
"Invalid addition arguments for this model"
)
expect_error(
make_stancode(mvbind(y1, y2) ~ 1 + ar(cov = TRUE), data = data),
"Explicit covariance terms cannot be modeled when 'rescor'"
)
expect_error(
make_stancode(y1 | resp_se(wi) ~ y2 + ma(), data = data),
"Please set cov = TRUE in ARMA correlation structures"
)
})
test_that("Stan code for multivariate models is correct", {
dat <- data.frame(
y1 = rnorm(10), y2 = rnorm(10),
x = 1:10, g = rep(1:2, each = 5),
censi = sample(0:1, 10, TRUE)
)
# models with residual correlations
prior <- prior(horseshoe(2), resp = "y1") +
prior(horseshoe(2), resp = "y2")
scode <- make_stancode(mvbind(y1, y2) ~ x, dat, prior = prior)
expect_match2(scode, "target += multi_normal_cholesky_lpdf(Y | Mu, LSigma);")
expect_match2(scode, "LSigma = diag_pre_multiply(sigma, Lrescor);")
expect_match2(scode, "target += normal_lpdf(hs_local_y1[1] | 0, 1)")
expect_match2(scode,
"target += inv_gamma_lpdf(hs_local_y2[2] | 0.5 * hs_df_y2, 0.5 * hs_df_y2)"
)
expect_match2(scode, "rescor[choose(k - 1, 2) + j] = Rescor[j, k];")
prior <- prior(lasso(2, 10), resp = "y1") +
prior(lasso(2, 10), resp = "y2")
scode <- make_stancode(mvbind(y1, y2) ~ x, dat, student(), prior = prior)
expect_match2(scode, "target += multi_student_t_lpdf(Y | nu, Mu, Sigma);")
expect_match2(scode, "matrix[nresp, nresp] Sigma = multiply_lower")
expect_match2(scode, "target += gamma_lpdf(nu | 2, 0.1)")
expect_match2(scode,
"target += chi_square_lpdf(lasso_inv_lambda_y1 | lasso_df_y1)"
)
expect_match2(scode,
"target += chi_square_lpdf(lasso_inv_lambda_y2 | lasso_df_y2)"
)
expect_match2(make_stancode(mvbind(y1, y2) | weights(x) ~ 1, dat),
"target += weights[n] * multi_normal_cholesky_lpdf(Y[n] | Mu[n], LSigma);"
)
# models without residual correlations
expect_warning(
bform <- bf(y1 | cens(censi) ~ x + y2 + (x|2|g)) +
gaussian() + cor_ar() +
(bf(x ~ 1) + mixture(poisson, nmix = 2)) +
(bf(y2 ~ s(y2) + (1|2|g)) + skew_normal()),
"Using 'cor_brms' objects for 'autocor' is deprecated"
)
bprior <- prior(normal(0, 5), resp = y1) +
prior(normal(0, 10), resp = y2)
scode <- make_stancode(bform, dat, prior = bprior)
expect_match2(scode, "r_1_y2_3 = r_1[, 3]")
expect_match2(scode, "err_y1[n] = Y_y1[n] - mu_y1[n]")
expect_match2(scode, "target += normal_lccdf(Y_y1[n] | mu_y1[n], sigma_y1)")
expect_match2(scode, "target += skew_normal_lpdf(Y_y2 | mu_y2, omega_y2, alpha_y2)")
expect_match2(scode, "ps[1] = log(theta1_x) + poisson_log_lpmf(Y_x[n] | mu1_x[n])")
expect_match2(scode, "target += normal_lpdf(b_y1 | 0, 5)")
expect_match2(scode, "target += normal_lpdf(bs_y2 | 0, 10)")
# multivariate binomial models
bform <- bf(x ~ 1) + bf(g ~ 1) + binomial()
scode <- make_stancode(bform, dat)
expect_match2(scode, "binomial_logit_lpmf(Y_x | trials_x, mu_x)")
expect_match2(scode, "binomial_logit_lpmf(Y_g | trials_g, mu_g)")
bform <- bform + weibull()
scode <- make_stancode(bform, dat)
expect_match2(scode, "mu_g[n] = exp(mu_g[n]) / tgamma(1 + 1 / shape_g)")
})
test_that("Stan code for categorical models is correct", {
dat <- data.frame(y = rep(c(1, 2, 3, "a_b"), 2), x = 1:8, .g = 1:8)
prior <- prior(normal(0, 5), "b", dpar = muab) +
prior(normal(0, 10), "b", dpar = mu2) +
prior(cauchy(0, 1), "Intercept", dpar = mu2) +
prior(normal(0, 2), "Intercept", dpar = mu3)
scode <- make_stancode(y ~ x + (1 |ID| .g), data = dat,
family = categorical(), prior = prior)
expect_match2(scode, "target += categorical_logit_lpmf(Y[n] | mu[n]);")
expect_match2(scode, "mu[n] = [0, mu2[n], mu3[n], muab[n]]';")
expect_match2(scode, "mu2 = Intercept_mu2 + Xc_mu2 * b_mu2;")
expect_match2(scode, "muab[n] += r_1_muab_3[J_1[n]] * Z_1_muab_3[n];")
expect_match2(scode, "target += normal_lpdf(b_mu2 | 0, 10);")
expect_match2(scode, "target += normal_lpdf(b_muab | 0, 5);")
expect_match2(scode, "target += cauchy_lpdf(Intercept_mu2 | 0, 1);")
expect_match2(scode, "target += normal_lpdf(Intercept_mu3 | 0, 2);")
scode <- make_stancode(y ~ x + (1 |ID| .g), data = dat,
family = categorical(refcat = NA))
expect_match2(scode, "mu[n] = [mu1[n], mu2[n], mu3[n], muab[n]]';")
})
test_that("Stan code for multinomial models is correct", {
N <- 15
dat <- data.frame(
y1 = rbinom(N, 10, 0.3), y2 = rbinom(N, 10, 0.5),
y3 = rbinom(N, 10, 0.7), x = rnorm(N)
)
dat$size <- with(dat, y1 + y2 + y3)
dat$y <- with(dat, cbind(y1, y2, y3))
prior <- prior(normal(0, 10), "b", dpar = muy2) +
prior(cauchy(0, 1), "Intercept", dpar = muy2) +
prior(normal(0, 2), "Intercept", dpar = muy3)
scode <- make_stancode(bf(y | trials(size) ~ 1, muy2 ~ x), data = dat,
family = multinomial(), prior = prior)
expect_match2(scode, "int Y[N, ncat];")
expect_match2(scode, "target += multinomial_logit_lpmf(Y[n] | mu[n]);")
expect_match2(scode, "muy2 = Intercept_muy2 + Xc_muy2 * b_muy2;")
expect_match2(scode, "target += normal_lpdf(b_muy2 | 0, 10);")
expect_match2(scode, "target += cauchy_lpdf(Intercept_muy2 | 0, 1);")
expect_match2(scode, "target += normal_lpdf(Intercept_muy3 | 0, 2);")
})
test_that("Stan code for dirichlet models is correct", {
N <- 15
dat <- as.data.frame(rdirichlet(N, c(3, 2, 1)))
names(dat) <- c("y1", "y2", "y3")
dat$x <- rnorm(N)
dat$y <- with(dat, cbind(y1, y2, y3))
prior <- prior(normal(0, 5), class = "b", dpar = "muy3") +
prior(exponential(10), "phi")
scode <- make_stancode(bf(y ~ 1, muy3 ~ x), data = dat,
family = dirichlet(), prior = prior)
expect_match2(scode, "vector[ncat] Y[N];")
expect_match2(scode, "target += dirichlet_logit_lpdf(Y[n] | mu[n], phi);")
expect_match2(scode, "muy3 = Intercept_muy3 + Xc_muy3 * b_muy3;")
expect_match2(scode, "target += normal_lpdf(b_muy3 | 0, 5);")
expect_match2(scode, "target += exponential_lpdf(phi | 10);")
scode <- make_stancode(bf(y ~ x, phi ~ x), data = dat,
family = dirichlet())
expect_match2(scode, "target += dirichlet_logit_lpdf(Y[n] | mu[n], phi[n]);")
expect_match2(scode, "vector[N] phi = Intercept_phi + Xc_phi * b_phi;")
expect_match2(scode, "phi[n] = exp(phi[n]);")
})
test_that("Stan code for ARMA models is correct", {
dat <- data.frame(y = rep(1:4, 2), x = 1:8, time = 1:8)
scode <- make_stancode(y ~ x + ar(time), dat, student())
expect_match2(scode, "err[n] = Y[n] - mu[n];")
expect_match2(scode, "mu[n] += Err[n, 1:Kar] * ar;")
scode <- make_stancode(y ~ x + ma(time, q = 2), dat, student())
expect_match2(scode, "mu[n] += Err[n, 1:Kma] * ma;")
expect_warning(
scode <- make_stancode(mvbind(y, x) ~ 1, dat, gaussian(),
autocor = cor_ar()),
"Argument 'autocor' should be specified within the 'formula' argument"
)
expect_match2(scode, "err_y[n] = Y_y[n] - mu_y[n];")
bform <- bf(y ~ x, sigma ~ x) + acformula(~arma(time, cov = TRUE))
scode <- make_stancode(bform, dat, family = student)
expect_match2(scode, "student_t_time_het_lpdf(Y | nu, mu, sigma, chol_cor")
bform <- bf(y ~ exp(eta) - 1, eta ~ x, autocor = ~ar(time), nl = TRUE)
scode <- make_stancode(bform, dat, family = student,
prior = prior(normal(0, 1), nlpar = eta))
expect_match2(scode, "mu[n] += Err[n, 1:Kar] * ar;")
# correlations of latent residuals
scode <- make_stancode(
y ~ x + ar(time, cov = TRUE), dat, family = poisson,
prior = prior(cauchy(0, 10), class = sderr)
)
expect_match2(scode, "chol_cor = cholesky_cor_ar1(ar[1], max(nobs_tg));")
expect_match2(scode,
"err = scale_time_err(zerr, sderr, chol_cor, nobs_tg, begin_tg, end_tg);"
)
expect_match2(scode, "vector[N] mu = Intercept + Xc * b + err;")
expect_match2(scode, "target += cauchy_lpdf(sderr | 0, 10);")
})
test_that("Stan code for compound symmetry models is correct", {
dat <- data.frame(y = rep(1:4, 2), x = 1:8, time = 1:8)
scode <- make_stancode(
y ~ x + cosy(time), dat,
prior = prior(normal(0, 2), cosy)
)
expect_match2(scode, "chol_cor = cholesky_cor_cosy(cosy, max(nobs_tg));")
expect_match2(scode, "target += normal_lpdf(cosy | 0, 2);")
scode <- make_stancode(bf(y ~ x + cosy(time), sigma ~ x), dat)
expect_match2(scode, "normal_time_het_lpdf(Y | mu, sigma, chol_cor")
scode <- make_stancode(y ~ x + cosy(time), dat, family = poisson)
expect_match2(scode, "chol_cor = cholesky_cor_cosy(cosy, max(nobs_tg));")
})
test_that("Stan code for intercept only models is correct", {
expect_match2(make_stancode(rating ~ 1, data = inhaler),
"b_Intercept = Intercept;")
expect_match2(make_stancode(rating ~ 1, data = inhaler, family = cratio()),
"b_Intercept = Intercept;")
expect_match2(make_stancode(rating ~ 1, data = inhaler, family = categorical()),
"b_mu3_Intercept = Intercept_mu3;")
})
test_that("Stan code of ordinal models is correct", {
dat <- data.frame(y = c(rep(1:4, 2), 1, 1), x1 = rnorm(10),
x2 = rnorm(10), g = rep(1:2, 5))
scode <- make_stancode(
y ~ x1, dat, family = cumulative(),
prior = prior(normal(0, 2), Intercept, coef = 2)
)
expect_match2(scode,
"target += ordered_logistic_lpmf(Y[n] | mu[n], Intercept);"
)
expect_match2(scode, "target += student_t_lpdf(Intercept[1] | 3, 0, 10);")
expect_match2(scode, "target += normal_lpdf(Intercept[2] | 0, 2);")
scode <- make_stancode(
y ~ x1, dat, cumulative("probit", threshold = "equidistant"),
prior = prior(normal(0, 2), Intercept)
)
expect_match2(scode, "real cumulative_probit_lpmf(int y")
expect_match2(scode, "p = Phi(disc * (thres[1] - mu));")
expect_match2(scode, "real delta;")
expect_match2(scode, "Intercept[k] = first_Intercept + (k - 1.0) * delta;")
expect_match2(scode, "b_Intercept = Intercept + dot_product(means_X, b);")
expect_match2(scode, "target += normal_lpdf(first_Intercept | 0, 2);")
scode <- make_stancode(y ~ x1, dat, family = cratio("probit_approx"))
expect_match2(scode, "real cratio_probit_approx_lpmf(int y")
expect_match2(scode, "q[k] = Phi_approx(disc * (mu - thres[k]));")
scode <- make_stancode(y ~ x1 + cs(x2) + cs(g), dat, family = sratio())
expect_match2(scode, "real sratio_logit_lpmf(int y")
expect_match2(scode, "matrix[N, Kcs] Xcs;")
expect_match2(scode, "matrix[Kcs, nthres] bcs;")
expect_match2(scode, "mucs = Xcs * bcs;")
expect_match2(scode,
"target += sratio_logit_lpmf(Y[n] | mu[n], disc, Intercept - mucs[n]');"
)
scode <- make_stancode(y ~ x1 + cse(x2) + (cse(1)|g), dat, family = acat())
expect_match2(scode, "real acat_logit_lpmf(int y")
expect_match2(scode, "mucs[n, 1] = mucs[n, 1] + r_1_1[J_1[n]] * Z_1_1[n];")
expect_match2(scode, "b_Intercept = Intercept + dot_product(means_X, b);")
scode <- make_stancode(y ~ x1 + (cse(x2)||g), dat, family = acat("probit"))
expect_match2(scode,
paste("mucs[n, 3] = mucs[n, 3] + r_1_3[J_1[n]] * Z_1_3[n]",
"+ r_1_6[J_1[n]] * Z_1_6[n];"))
expect_match2(scode,
"target += acat_probit_lpmf(Y[n] | mu[n], disc, Intercept - mucs[n]');"
)
# non-linear ordinal models
scode <- make_stancode(
bf(y ~ eta, eta ~ x1, nl = TRUE), dat, family = cumulative(),
prior = prior(normal(0, 2), nlpar = eta)
)
expect_match2(scode, "ordered[nthres] Intercept;")
expect_match2(scode,
"target += ordered_logistic_lpmf(Y[n] | mu[n], Intercept);"
)
# ordinal mixture models with fixed intercepts
scode <- make_stancode(
bf(y ~ 1, mu1 ~ x1, mu2 ~ 1), data = dat,
family = mixture(cumulative(), nmix = 2, order = "mu")
)
expect_match2(scode, "Intercept_mu2 = fixed_Intercept;")
expect_match2(scode, "target += student_t_lpdf(fixed_Intercept | 3, 0, 10);")
})
test_that("ordinal disc parameters appear in the Stan code", {
scode <- make_stancode(
bf(rating ~ period + carry + treat, disc ~ period),
data = inhaler, family = cumulative(),
prior = prior(normal(0,5), dpar = disc)
)
expect_match2(scode,
"target += cumulative_logit_lpmf(Y[n] | mu[n], disc[n], Intercept)"
)
expect_match2(scode, "target += normal_lpdf(b_disc | 0, 5)")
expect_match2(scode, "disc[n] = exp(disc[n])")
})
test_that("grouped ordinal thresholds appear in the Stan code", {
dat <- data.frame(
y = sample(1:6, 10, TRUE),
y2 = sample(1:6, 10, TRUE),
gr = rep(c("a", "b"), each = 5),
th = rep(5:6, each = 5),
x = rnorm(10)
)
prior <- prior(normal(0,1), class = "Intercept", group = "b")
scode <- make_stancode(
y | thres(th, gr) ~ x, data = dat,
family = sratio(), prior = prior
)
expect_match2(scode, "int nthres[ngrthres];")
expect_match2(scode, "merged_Intercept[Kthres_start[1]:Kthres_end[1]] = Intercept_1;")
expect_match2(scode, "target += sratio_logit_merged_lpmf(Y[n]")
expect_match2(scode, "target += normal_lpdf(Intercept_2 | 0, 1);")
# centering needs to be deactivated automatically
expect_match2(scode, "vector[nthres[1]] b_Intercept_1 = Intercept_1;")
# model with equidistant thresholds
scode <- make_stancode(
y | thres(th, gr) ~ x, data = dat,
family = cumulative(threshold = "equidistant"),
prior = prior
)
expect_match2(scode, "target += cumulative_logit_merged_lpmf(Y[n]")
expect_match2(scode, "real first_Intercept_1;")
expect_match2(scode, "target += normal_lpdf(first_Intercept_2 | 0, 1);")
expect_match2(scode, "Intercept_2[k] = first_Intercept_2 + (k - 1.0) * delta_2;")
# ordinal mixture model
scode <- make_stancode(
y | thres(th, gr) ~ x, data = dat,
family = mixture(cratio, acat, order = "mu"),
prior = prior
)
expect_match2(scode, "ps[1] = log(theta1) + cratio_logit_merged_lpmf(Y[n]")
expect_match2(scode, "ps[2] = log(theta2) + acat_logit_merged_lpmf(Y[n]")
expect_match2(scode, "vector[nmthres] merged_Intercept_mu1;")
expect_match2(scode, "merged_Intercept_mu2[Kthres_start[1]:Kthres_end[1]] = Intercept_mu2_1;")
expect_match2(scode, "vector[nthres[1]] b_mu1_Intercept_1 = Intercept_mu1_1;")
# multivariate ordinal model
bform <- bf(y | thres(th, gr) ~ x, family = sratio) +
bf(y2 | thres(th, gr) ~ x, family = cumulative)
scode <- make_stancode(bform, data = dat)
expect_match2(scode, "target += student_t_lpdf(Intercept_y2_1 | 3, 0, 10);")
expect_match2(scode, "merged_Intercept_y[Kthres_start_y[2]:Kthres_end_y[2]] = Intercept_y_2;")
})
test_that("monotonic effects appear in the Stan code", {
dat <- data.frame(y = rpois(120, 10), x1 = rep(1:4, 30),
x2 = factor(rep(c("a", "b", "c"), 40), ordered = TRUE),
g = rep(1:10, each = 12))
prior <- c(prior(normal(0,1), class = b, coef = mox1),
prior(dirichlet(c(1,0.5,2)), simo, coef = mox11),
prior(dirichlet(c(1,0.5,2)), simo, coef = mox21))
scode <- make_stancode(y ~ y*mo(x1)*mo(x2), dat, prior = prior)
expect_match2(scode, "int Xmo_3[N];")
expect_match2(scode, "simplex[Jmo[1]] simo_1;")
expect_match2(scode, "(bsp[2]) * mo(simo_2, Xmo_2[n])")
expect_match2(scode,
"(bsp[6]) * mo(simo_7, Xmo_7[n]) * mo(simo_8, Xmo_8[n]) * Csp_3[n]"
)
expect_match2(scode, "target += normal_lpdf(bsp[1] | 0, 1)")
expect_match2(scode, "target += dirichlet_lpdf(simo_1 | con_simo_1);")
expect_match2(scode, "target += dirichlet_lpdf(simo_8 | con_simo_8);")
scode <- make_stancode(y ~ mo(x1) + (mo(x1) | x2), dat)
expect_match2(scode, "(bsp[1] + r_1_2[J_1[n]]) * mo(simo_1, Xmo_1[n])")
expect_true(!grepl("Z_1_w", scode))
# test issue reported in discourse post #12978
scode <- make_stancode(y ~ mo(x1) + (mo(x1) | x2) + (mo(x1) | g), dat)
expect_match2(scode, "(bsp[1] + r_1_2[J_1[n]] + r_2_2[J_2[n]]) * mo(simo_1, Xmo_1[n])")
# test issue #813
scode <- make_stancode(y ~ mo(x1):y, dat)
expect_match2(scode, "mu[n] += (bsp[1]) * mo(simo_1, Xmo_1[n]) * Csp_1[n];")
expect_error(
make_stancode(y ~ mo(x1) + (mo(x2) | x2), dat),
"Special group-level terms require"
)
prior <- prior(beta(1, 1), simo, coef = mox11)
expect_error(
make_stancode(y ~ mo(x1), dat, prior = prior),
"'dirichlet' is the only valid prior for simplex parameters"
)
})
test_that("Stan code for non-linear models is correct", {
flist <- list(a ~ x, b ~ z + (1|g))
data <- data.frame(
y = rgamma(9, 1, 1), x = rnorm(9),
z = rnorm(9), v = 1L:9L, g = rep(1:3, 3)
)
prior <- c(set_prior("normal(0,5)", nlpar = "a"),
set_prior("normal(0,1)", nlpar = "b"))
# syntactic validity is already checked within make_stancode
scode <- make_stancode(
bf(y ~ a - exp(b^z) * (z <= a) * v, flist = flist, nl = TRUE),
data = data, prior = prior
)
expect_match2(scode,
"mu[n] = nlp_a[n] - exp(nlp_b[n] ^ C_1[n]) * (C_1[n] <= nlp_a[n]) * C_2[n];"
)
expect_match2(scode, "vector[N] C_1;")
expect_match2(scode, "int C_2[N];")
# non-linear predictor can be computed outside a loop
scode <- make_stancode(bf(y ~ a - exp(b + z), flist = flist,
nl = TRUE, loop = FALSE),
data = data, prior = prior)
expect_match2(scode, "\n mu = nlp_a - exp(nlp_b + C_1);")
flist <- list(a1 ~ 1, a2 ~ z + (x|g))
prior <- c(set_prior("beta(1,1)", nlpar = "a1", lb = 0, ub = 1),
set_prior("normal(0,1)", nlpar = "a2"))
scode <- make_stancode(
bf(y ~ a1 * exp(-x/(a2 + z)),
flist = flist, nl = TRUE),
data = data, family = Gamma("log"),
prior = prior
)
expect_match2(scode,
paste("mu[n] = shape * exp(-(nlp_a1[n] *",
"exp( - C_1[n] / (nlp_a2[n] + C_2[n]))));"))
bform <- bf(y ~ x) +
nlf(sigma ~ a1 * exp(-x/(a2 + z))) +
lf(a1 ~ 1, a2 ~ z + (x|g)) +
lf(alpha ~ x)
scode <- make_stancode(
bform, data, family = skew_normal(),
prior = c(
prior(normal(0, 1), nlpar = a1),
prior(normal(0, 5), nlpar = a2)
)
)
expect_match2(scode, "nlp_a1 = X_a1 * b_a1")
expect_match2(scode,
"sigma[n] = exp(nlp_a1[n] * exp( - C_sigma_1[n] / (nlp_a2[n] + C_sigma_2[n])))"
)
expect_match2(scode, "target += normal_lpdf(b_a2 | 0, 5)")
expect_error(make_stancode(bform, data, family = skew_normal()),
"Priors on population-level coefficients are required")
})
test_that("Stan code for nested non-linear parameters is correct", {
dat <- data.frame(y = rnorm(10), x = rnorm(10), z = 1:5)
bform <- bf(
y ~ lb + (1 - lb) * inv_logit(b * x),
b + a ~ 1 + (1 | z), nlf(lb ~ inv_logit(a / x)),
nl = TRUE
)
bprior <- prior(normal(0, 1), nlpar = "a") +
prior(normal(0, 1), nlpar = "b")
scode <- make_stancode(bform, dat, prior = bprior)
expect_match2(scode, "nlp_lb[n] = inv_logit(nlp_a[n] / C_lb_1[n]);")
expect_match2(scode,
"mu[n] = nlp_lb[n] + (1 - nlp_lb[n]) * inv_logit(nlp_b[n] * C_1[n]);"
)
})
test_that("make_stancode accepts very long non-linear formulas", {
data <- data.frame(y = rnorm(10), this_is_a_very_long_predictor = rnorm(10))
expect_silent(make_stancode(bf(y ~ b0 + this_is_a_very_long_predictor +
this_is_a_very_long_predictor +
this_is_a_very_long_predictor,
b0 ~ 1, nl = TRUE),
data = data, prior = prior(normal(0,1), nlpar = "b0")))
})
test_that("no loop in trans-par is defined for simple 'identity' models", {
expect_true(!grepl(make_stancode(time ~ age, data = kidney),
"mu[n] = (mu[n]);", fixed = TRUE))
expect_true(!grepl(make_stancode(time ~ age, data = kidney,
family = poisson("identity")),
"mu[n] = (mu[n]);", fixed = TRUE))
})
test_that("known standard errors appear in the Stan code", {
scode <- make_stancode(time | se(age) ~ sex, data = kidney)
expect_match2(scode, "target += normal_lpdf(Y | mu, se)")
scode <- make_stancode(time | se(age) + weights(age) ~ sex, data = kidney)
expect_match2(scode, "target += weights[n] * normal_lpdf(Y[n] | mu[n], se[n])")
scode <- make_stancode(time | se(age, sigma = TRUE) ~ sex, data = kidney)
expect_match2(scode, "target += normal_lpdf(Y | mu, sqrt(square(sigma) + se2))")
scode <- make_stancode(bf(time | se(age, sigma = TRUE) ~ sex, sigma ~ sex),
data = kidney)
expect_match2(scode, "target += normal_lpdf(Y | mu, sqrt(square(sigma) + se2))")
})
test_that("functions defined in 'stan_funs' appear in the functions block", {
test_fun <- paste0(" real test_fun(real a, real b) {\n",
" return a + b;\n",
" }\n")
scode <- SW(make_stancode(time ~ age, data = kidney, stan_funs = test_fun))
expect_match2(scode, test_fun)
})
test_that("FCOR matrices appear in the Stan code", {
data <- data.frame(y = 1:5)
V <- diag(5)
expect_match2(make_stancode(y ~ fcor(V), data = data, family = gaussian()),
"target += normal_fcor_hom_lpdf(Y | mu, sigma, Lfcor);")
expect_match2(make_stancode(y ~ fcor(V), data = data, family = student()),
"target += student_t_fcor_hom_lpdf(Y | nu, mu, sigma, Lfcor);")
})
test_that("Stan code for GAMMs is correct", {
dat <- data.frame(y = rnorm(10), x = rnorm(10), g = factor(rep(1:2, 5)))
scode <- make_stancode(y ~ s(x) + (1|g), data = dat,
prior = set_prior("normal(0,2)", "sds"))
expect_match2(scode, "Zs_1_1 * s_1_1")
expect_match2(scode, "matrix[N, knots_1[1]] Zs_1_1")
expect_match2(scode, "target += normal_lpdf(zs_1_1 | 0, 1)")
expect_match2(scode, "target += normal_lpdf(sds_1_1 | 0,2)")
prior <- c(set_prior("normal(0,5)", nlpar = "lp"),
set_prior("normal(0,2)", "sds", nlpar = "lp"))
scode <- make_stancode(bf(y ~ lp, lp ~ s(x) + (1|g), nl = TRUE),
data = dat, prior = prior)
expect_match2(scode, "Zs_lp_1_1 * s_lp_1_1")
expect_match2(scode, "matrix[N, knots_lp_1[1]] Zs_lp_1_1")
expect_match2(scode, "target += normal_lpdf(zs_lp_1_1 | 0, 1)")
expect_match2(scode, "target += normal_lpdf(sds_lp_1_1 | 0,2)")
scode <- make_stancode(y ~ s(x) + t2(x,y), data = dat,
prior = set_prior("normal(0,2)", "sds"))
expect_match2(scode, "Zs_2_2 * s_2_2")
expect_match2(scode, "matrix[N, knots_2[2]] Zs_2_2")
expect_match2(scode, "target += normal_lpdf(zs_2_2 | 0, 1)")
expect_match2(scode, "target += normal_lpdf(sds_2_2 | 0,2)")
scode <- make_stancode(y ~ g + s(x, by = g), data = dat)
expect_match2(scode, "vector[knots_2[1]] zs_2_1")
expect_match2(scode, "s_2_1 = sds_2_1 * zs_2_1")
})
test_that("Stan code of response times models is correct", {
dat <- epilepsy
dat$cens <- sample(-1:1, nrow(dat), TRUE)
scode <- make_stancode(count ~ Trt + (1|patient),
data = dat, family = exgaussian("log"),
prior = prior(gamma(1,1), class = beta))
expect_match2(scode,
"target += exp_mod_normal_lpdf(Y | mu - beta, sigma, inv(beta))"
)
expect_match2(scode, "mu[n] = exp(mu[n])")
expect_match2(scode, "target += gamma_lpdf(beta | 1, 1)")
scode <- make_stancode(bf(count ~ Trt + (1|patient),
sigma ~ Trt, beta ~ Trt),
data = dat, family = exgaussian())
expect_match2(scode,
"target += exp_mod_normal_lpdf(Y | mu - beta, sigma, inv(beta))"
)
expect_match2(scode, "beta[n] = exp(beta[n])")
scode <- make_stancode(count | cens(cens) ~ Trt + (1|patient),
data = dat, family = exgaussian("inverse"))
expect_match2(scode, "exp_mod_normal_lccdf(Y[n] | mu[n] - beta, sigma, inv(beta))")
scode <- make_stancode(count ~ Trt, dat, family = shifted_lognormal())
expect_match2(scode, "target += lognormal_lpdf(Y - ndt | mu, sigma)")
scode <- make_stancode(count | cens(cens) ~ Trt, dat, family = shifted_lognormal())
expect_match2(scode, "target += lognormal_lcdf(Y[n] - ndt | mu[n], sigma)")
# test issue #837
scode <- make_stancode(mvbind(count, zBase) ~ Trt, data = dat,
family = shifted_lognormal())
expect_match2(scode, "target += uniform_lpdf(ndt_count | 0, min_Y_count)")
expect_match2(scode, "target += uniform_lpdf(ndt_zBase | 0, min_Y_zBase)")
})
test_that("Stan code of wiener diffusion models is correct", {
dat <- data.frame(q = 1:10, resp = sample(0:1, 10, TRUE), x = rnorm(10))
scode <- make_stancode(q | dec(resp) ~ x, data = dat, family = wiener())
expect_match2(scode,
"target += wiener_diffusion_lpdf(Y[n] | dec[n], bs, ndt, bias, mu[n])"
)
scode <- make_stancode(bf(q | dec(resp) ~ x, bs ~ x, ndt ~ x, bias ~ x),
data = dat, family = wiener())
expect_match2(scode,
"target += wiener_diffusion_lpdf(Y[n] | dec[n], bs[n], ndt[n], bias[n], mu[n])"
)
expect_match2(scode, "bias[n] = inv_logit(bias[n]);")
scode <- make_stancode(bf(q | dec(resp) ~ x, ndt = 0.5),
data = dat, family = wiener())
expect_match2(scode, "real ndt;")
expect_error(make_stancode(q ~ x, data = dat, family = wiener()),
"Addition argument 'dec' is required for family 'wiener'")
})
test_that("Group IDs appear in the Stan code", {
form <- bf(count ~ Trt + (1+Trt|3|visit) + (1|patient),
shape ~ (1|3|visit) + (Trt||patient))
scode <- make_stancode(form, data = epilepsy, family = negbinomial())
expect_match2(scode, "r_2_1 = r_2[, 1]")
expect_match2(scode, "r_2_shape_3 = r_2[, 3]")
form <- bf(count ~ a, sigma ~ (1|3|visit) + (Trt||patient),
a ~ Trt + (1+Trt|3|visit) + (1|patient), nl = TRUE)
scode <- make_stancode(form, data = epilepsy, family = student(),
prior = set_prior("normal(0,5)", nlpar = "a"))
expect_match2(scode, "r_2_a_2 = r_2[, 2];")
expect_match2(scode, "r_1_sigma_2 = (sd_1[2] * (z_1[2]));")
})
test_that("distributional gamma models are handled correctly", {
# test fix of issue #124
scode <- make_stancode(
bf(time ~ age * sex + disease + (1|patient),
shape ~ age + (1|patient)),
data = kidney, family = Gamma("log")
)
expect_match(scode, paste0(
brms:::escape_all("shape[n] = exp(shape[n]);"), ".+",
brms:::escape_all("mu[n] = shape[n] * exp(-(mu[n]));")
))
scode <- make_stancode(
bf(time ~ inv_logit(a) * exp(b * age),
a + b ~ sex + (1|patient), nl = TRUE,
shape ~ age + (1|patient)),
data = kidney, family = Gamma("identity"),
prior = c(set_prior("normal(2,2)", nlpar = "a"),
set_prior("normal(0,3)", nlpar = "b"))
)
expect_match(scode, paste0(
brms:::escape_all("shape[n] = exp(shape[n]);"), ".+",
brms:::escape_all("mu[n] = shape[n] / (inv_logit(nlp_a[n]) * exp(nlp_b[n] * C_1[n]));")
))
})
test_that("weighted, censored, and truncated likelihoods are correct", {
dat <- data.frame(y = 1:9, x = rep(-1:1, 3), y2 = 10:18)
scode <- make_stancode(y | weights(y2) ~ 1, dat, poisson())
expect_match2(scode, "target += weights[n] * poisson_log_lpmf(Y[n] | mu[n]);")
scode <- make_stancode(y | trials(y2) + weights(y2) ~ 1, dat, binomial())
expect_match2(scode,
"target += weights[n] * binomial_logit_lpmf(Y[n] | trials[n], mu[n]);"
)
expect_match2(make_stancode(y | cens(x, y2) ~ 1, dat, poisson()),
"target += poisson_lpmf(Y[n] | mu[n]);")
expect_match2(make_stancode(y | cens(x) ~ 1, dat, weibull()),
"target += weibull_lccdf(Y[n] | shape, mu[n]);")
dat$x[1] <- 2
scode <- make_stancode(y | cens(x, y2) ~ 1, dat, gaussian())
expect_match2(scode, paste0(
"target += log_diff_exp(\n",
" normal_lcdf(rcens[n] | mu[n], sigma),"
))
dat$x <- 1
expect_match2(make_stancode(y | cens(x) + weights(x) ~ 1, dat, weibull()),
"target += weights[n] * weibull_lccdf(Y[n] | shape, mu[n]);")
scode <- make_stancode(y | cens(x) + trunc(0.1) ~ 1, dat, weibull())
expect_match2(scode, "target += weibull_lccdf(Y[n] | shape, mu[n]) -")
expect_match2(scode, " weibull_lccdf(lb[n] | shape, mu[n]);")
scode <- make_stancode(y | cens(x) + trunc(ub = 30) ~ 1, dat)
expect_match2(scode, "target += normal_lccdf(Y[n] | mu[n], sigma) -")
expect_match2(scode, " normal_lcdf(ub[n] | mu[n], sigma);")
scode <- make_stancode(y | weights(x) + trunc(0, 30) ~ 1, dat)
expect_match2(scode, "target += weights[n] * normal_lpdf(Y[n] | mu[n], sigma) -")
expect_match2(scode, " log_diff_exp(normal_lcdf(ub[n] | mu[n], sigma),")
})
test_that("noise-free terms appear in the Stan code", {
N <- 30
dat <- data.frame(
y = rnorm(N), x = rnorm(N), z = rnorm(N),
xsd = abs(rnorm(N, 1)), zsd = abs(rnorm(N, 1)),
ID = rep(1:5, each = N / 5)
)
me_prior <- prior(normal(0,5)) +
prior(normal(0, 10), "meanme") +
prior(cauchy(0, 5), "sdme", coef = "mez") +
prior(lkj(2), "corme")
scode <- make_stancode(
y ~ me(x, xsd)*me(z, zsd)*x, data = dat, prior = me_prior,
sample_prior = "yes"
)
expect_match2(scode,
"(bsp[1]) * Xme_1[n] + (bsp[2]) * Xme_2[n] + (bsp[3]) * Xme_1[n] * Xme_2[n]"
)
expect_match2(scode, "(bsp[6]) * Xme_1[n] * Xme_2[n] * Csp_3[n]")
expect_match2(scode, "target += normal_lpdf(Xn_2 | Xme_2, noise_2)")
expect_match2(scode, "target += normal_lpdf(bsp | 0, 5)")
expect_match2(scode, "target += normal_lpdf(to_vector(zme_1) | 0, 1)")
expect_match2(scode, "target += normal_lpdf(meanme_1 | 0, 10)")
expect_match2(scode, "target += cauchy_lpdf(sdme_1[2] | 0, 5)")
expect_match2(scode, "target += lkj_corr_cholesky_lpdf(Lme_1 | 2)")
expect_match2(scode, "+ (diag_pre_multiply(sdme_1, Lme_1) * zme_1)'")
expect_match2(scode, "corme_1[choose(k - 1, 2) + j] = Corme_1[j, k];")
scode <- make_stancode(
y ~ me(x, xsd)*z + (me(x, xsd)*z | ID), data = dat
)
expect_match2(scode, "(bsp[1] + r_1_3[J_1[n]]) * Xme_1[n]")
expect_match2(scode, "(bsp[2] + r_1_4[J_1[n]]) * Xme_1[n] * Csp_1[n]")
expect_match2(make_stancode(y ~ I(me(x, xsd)^2), data = dat),
"(bsp[1]) * (Xme_1[n]^2)")
# test that noise-free variables are unique across model parts
scode <- make_stancode(
bf(y ~ me(x, xsd)*me(z, zsd)*x, sigma ~ me(x, xsd)),
data = dat, prior = prior(normal(0,5))
)
expect_match2(scode, "mu[n] += (bsp[1]) * Xme_1[n]")
expect_match2(scode, "sigma[n] += (bsp_sigma[1]) * Xme_1[n]")
scode <- make_stancode(
bf(y ~ a * b, a + b ~ me(x, xsd), nl = TRUE),
data = dat,
prior = prior(normal(0,5), nlpar = a) +
prior(normal(0, 5), nlpar = b)
)
expect_match2(scode, "nlp_a[n] += (bsp_a[1]) * Xme_1[n]")
expect_match2(scode, "nlp_b[n] += (bsp_b[1]) * Xme_1[n]")
bform <- bf(mvbind(y, z) ~ me(x, xsd)) + set_mecor(FALSE)
scode <- make_stancode(mvbind(y, z) ~ me(x, xsd), dat)
expect_match2(scode, "mu_y[n] += (bsp_y[1]) * Xme_1[n]")
expect_match2(scode, "mu_z[n] += (bsp_z[1]) * Xme_1[n]")
expect_match2(scode, "Xme_1 = meanme_1[1] + sdme_1[1] * zme_1;")
# noise-free terms with grouping factors
bform <- bf(y ~ me(x, xsd, ID) + (me(x, xsd, ID) | ID))
scode <- make_stancode(bform, dat)
expect_match2(scode, "vector[Nme_1] Xn_1;")
expect_match2(scode, "(bsp[1] + r_1_2[J_1[n]]) * Xme_1[Jme_1[n]]")
bform <- bform + set_mecor(FALSE)
scode <- make_stancode(bform, dat)
expect_match2(scode, "Xme_1 = meanme_1[1] + sdme_1[1] * zme_1;")
})
test_that("Stan code of multi-membership models is correct", {
dat <- data.frame(y = rnorm(10), g1 = sample(1:10, 10, TRUE),
g2 = sample(1:10, 10, TRUE), w1 = rep(1, 10),
w2 = rep(abs(rnorm(10))))
expect_match2(make_stancode(y ~ (1|mm(g1, g2)), data = dat),
paste0(" W_1_1[n] * r_1_1[J_1_1[n]] * Z_1_1_1[n]",
" + W_1_2[n] * r_1_1[J_1_2[n]] * Z_1_1_2[n]")
)
expect_match2(make_stancode(y ~ (1+w1|mm(g1,g2)), data = dat),
paste0(" W_1_1[n] * r_1_2[J_1_1[n]] * Z_1_2_1[n]",
" + W_1_2[n] * r_1_2[J_1_2[n]] * Z_1_2_2[n]")
)
expect_match2(make_stancode(y ~ (1+mmc(w1, w2)|mm(g1,g2)), data = dat),
" W_1_2[n] * r_1_2[J_1_2[n]] * Z_1_2_2[n];"
)
})
test_that("by variables in grouping terms are handled correctly", {
dat <- data.frame(
y = rnorm(100), x = rnorm(100),
g = rep(1:10, each = 10),
z = factor(rep(c(0, 4.5, 3, 2, 5), each = 20))
)
scode <- make_stancode(y ~ x + (1 | gr(g, by = z)), dat)
expect_match2(scode, "r_1_1 = (sd_1[1, Jby_1]' .* (z_1[1]));")
scode <- make_stancode(y ~ x + (x | gr(g, by = z)), dat)
expect_match2(scode, "r_1 = scale_r_cor_by(z_1, sd_1, L_1, Jby_1);")
expect_match2(scode, "target += student_t_lpdf(to_vector(sd_1) | 3, 0, 10);")
expect_match2(scode, "target += lkj_corr_cholesky_lpdf(L_1[5] | 1);")
})
test_that("Group syntax | and || is handled correctly,", {
data <- data.frame(y = rnorm(10), x = rnorm(10),
g1 = rep(1:5, each = 2), g2 = rep(1:2, 5))
scode <- make_stancode(y ~ x + (1+x||g1) + (I(x/4)|g2), data)
expect_match2(scode, "r_1_2 = (sd_1[2] * (z_1[2]));")
expect_match2(scode, "r_2_1 = r_2[, 1];")
expect_match2(scode, "r_2 = (diag_pre_multiply(sd_2, L_2) * z_2)';")
})
test_that("predicting zi and hu works correctly", {
scode <- make_stancode(bf(count ~ Trt, zi ~ Trt), epilepsy,
family = "zero_inflated_poisson")
expect_match2(scode,
"target += zero_inflated_poisson_log_logit_lpmf(Y[n] | mu[n], zi[n])"
)
expect_true(!grepl("inv_logit\\(", scode))
expect_true(!grepl("exp(mu[n])", scode, fixed = TRUE))
scode <- make_stancode(bf(count ~ Trt, zi ~ Trt), epilepsy,
family = zero_inflated_poisson(identity))
expect_match2(scode,
"target += zero_inflated_poisson_logit_lpmf(Y[n] | mu[n], zi[n])"
)
scode <- make_stancode(bf(count ~ Trt, zi ~ Trt), epilepsy,
family = "zero_inflated_binomial")
expect_match2(scode,
"target += zero_inflated_binomial_blogit_logit_lpmf(Y[n] | trials[n], mu[n], zi[n])"
)
expect_true(!grepl("inv_logit\\(", scode))
fam <- zero_inflated_binomial("probit", link_zi = "identity")
scode <- make_stancode(bf(count ~ Trt, zi ~ Trt), epilepsy,
family = fam)
expect_match2(scode,
"target += zero_inflated_binomial_lpmf(Y[n] | trials[n], mu[n], zi[n])"
)
expect_match2(scode, "mu[n] = Phi(mu[n]);")
scode <- make_stancode(
bf(count ~ Trt, zi ~ Trt), epilepsy,
family = zero_inflated_beta()
)
expect_match2(scode,
"target += zero_inflated_beta_logit_lpdf(Y[n] | mu[n], phi, zi[n])"
)
scode <- make_stancode(bf(count ~ Trt, hu ~ Trt), epilepsy,
family = "hurdle_negbinomial")
expect_match2(scode,
"target += hurdle_neg_binomial_log_logit_lpmf(Y[n] | mu[n], shape, hu[n])"
)
expect_true(!grepl("inv_logit\\(", scode))
expect_true(!grepl("exp(mu[n])", scode, fixed = TRUE))
scode <- make_stancode(bf(count ~ Trt, hu ~ Trt), epilepsy,
family = "hurdle_gamma")
expect_match2(scode,
"target += hurdle_gamma_logit_lpdf(Y[n] | shape, mu[n], hu[n])"
)
expect_true(!grepl("inv_logit\\(", scode))
expect_match2(scode, "mu[n] = shape * exp(-(mu[n]));")
scode <- make_stancode(
bf(count ~ Trt, hu ~ Trt), epilepsy,
family = hurdle_gamma(link_hu = "identity")
)
expect_match2(scode, "target += hurdle_gamma_lpdf(Y[n] | shape, mu[n], hu[n])")
expect_true(!grepl("inv_logit\\(", scode))
expect_match2(scode, "mu[n] = shape * exp(-(mu[n]));")
})
test_that("fixing auxiliary parameters is possible", {
scode <- make_stancode(bf(y ~ 1, sigma = 0.5), data = list(y = rnorm(10)))
expect_match(scode, "data \\{[^\\}]*real sigma;")
})
test_that("Stan code of quantile regression models is correct", {
data <- data.frame(y = rnorm(10), x = rnorm(10), c = 1)
scode <- make_stancode(y ~ x, data, family = asym_laplace())
expect_match2(scode, "target += asym_laplace_lpdf(Y[n] | mu[n], sigma, quantile)")
scode <- make_stancode(bf(y ~ x, quantile = 0.75), data, family = asym_laplace())
expect_match(scode, "data \\{[^\\}]*real quantile;")
scode <- make_stancode(y | cens(c) ~ x, data, family = asym_laplace())
expect_match2(scode, "target += asym_laplace_lccdf(Y[n] | mu[n], sigma, quantile)")
scode <- make_stancode(bf(y ~ x, sigma ~ x), data, family = asym_laplace())
expect_match2(scode, "target += asym_laplace_lpdf(Y[n] | mu[n], sigma[n], quantile)")
scode <- make_stancode(bf(y ~ x, quantile = 0.75), data,
family = brmsfamily("zero_inflated_asym_laplace"))
expect_match2(scode,
"target += zero_inflated_asym_laplace_lpdf(Y[n] | mu[n], sigma, quantile, zi)"
)
})
test_that("Stan code of addition term 'rate' is correct", {
data <- data.frame(y = rpois(10, 1), x = rnorm(10), time = 1:10)
scode <- make_stancode(y | rate(time) ~ x, data, poisson())
expect_match2(scode, "mu = Intercept + Xc * b + log_denom;")
scode <- make_stancode(
bf(y | rate(time) ~ a, a ~ x, nl = TRUE),
data = data, family = poisson(),
prior = prior(normal(0, 1), nlpar = "a")
)
expect_match2(scode, "mu[n] = nlp_a[n] + log_denom[n];")
scode <- make_stancode(
bf(y | rate(time) ~ a, a ~ x, nl = TRUE, loop = FALSE),
data = data, family = poisson(),
prior = prior(normal(0, 1), nlpar = "a")
)
expect_match2(scode, "mu = nlp_a + log_denom;")
})
test_that("Stan code of GEV models is correct", {
data <- data.frame(y = rnorm(10), x = rnorm(10), c = 1)
scode <- make_stancode(y ~ x, data, gen_extreme_value())
expect_match2(scode, "target += gen_extreme_value_lpdf(Y[n] | mu[n], sigma, xi)")
expect_match2(scode, "xi = scale_xi(tmp_xi, Y, mu, sigma)")
scode <- make_stancode(bf(y ~ x, sigma ~ x), data, gen_extreme_value())
expect_match2(scode, "xi = scale_xi_vector(tmp_xi, Y, mu, sigma)")
scode <- make_stancode(bf(y ~ x, xi ~ x), data, gen_extreme_value())
expect_match2(scode, "xi[n] = expm1(xi[n])")
scode <- make_stancode(bf(y ~ x, xi = 0), data, gen_extreme_value())
expect_match(scode, "data \\{[^\\}]*real xi; // shape parameter")
scode <- make_stancode(y | cens(c) ~ x, data, gen_extreme_value())
expect_match2(scode, "target += gen_extreme_value_lccdf(Y[n] | mu[n], sigma, xi)")
})
test_that("Stan code of Cox models is correct", {
data <- data.frame(y = rexp(100), ce = sample(0:1, 100, TRUE), x = rnorm(100))
bform <- bf(y | cens(ce) ~ x)
bprior <- prior(normal(0, 2), sbhaz)
scode <- make_stancode(bform, data, brmsfamily("cox"), prior = bprior)
expect_match2(scode, "target += cox_log_lpdf(Y[n] | mu[n], bhaz[n], cbhaz[n]);")
expect_match2(scode, "vector[N] cbhaz = Zcbhaz * sbhaz;")
expect_match2(scode, "target += normal_lpdf(sbhaz | 0, 2);")
expect_match2(scode, "vector[Kbhaz] sbhaz;")
scode <- make_stancode(bform, data, brmsfamily("cox", "identity"))
expect_match2(scode, "target += cox_lccdf(Y[n] | mu[n], bhaz[n], cbhaz[n]);")
expect_match2(scode, "target += normal_lpdf(sbhaz | 0, 1);")
})
test_that("offsets appear in the Stan code", {
data <- data.frame(y = rnorm(10), x = rnorm(10), c = 1)
scode <- make_stancode(y ~ x + offset(c), data)
expect_match2(scode, "Intercept + Xc * b + offsets;")
scode <- make_stancode(bf(y ~ a, a ~ offset(log(c + 1)), nl = TRUE),
data, prior = prior(normal(0,1), nlpar = a))
expect_match2(scode, "X_a * b_a + offsets_a;")
})
test_that("prior only models are correctly checked", {
data <- data.frame(y = rnorm(10), x = rnorm(10), c = 1)
prior <- prior(normal(0, 5), b) + prior("", Intercept)
expect_error(make_stancode(y ~ x, data, prior = prior,
sample_prior = "only"),
"Sampling from priors is not possible")
prior <- prior(normal(0, 5), b) + prior(normal(0, 10), Intercept)
scode <- make_stancode(y ~ x, data, prior = prior,
sample_prior = "only")
expect_match2(scode, "target += normal_lpdf(Intercept | 0, 10)")
})
test_that("Stan code of mixture model is correct", {
data <- data.frame(y = 1:10, x = rnorm(10), c = 1)
scode <- make_stancode(
bf(y ~ x, sigma2 ~ x), data,
family = mixture(gaussian, gaussian),
sample_prior = TRUE
)
expect_match2(scode, "ordered[2] ordered_Intercept;")
expect_match2(scode, "Intercept_mu2 = ordered_Intercept[2];")
expect_match2(scode, "target += dirichlet_lpdf(theta | con_theta);")
expect_match2(scode, "ps[1] = log(theta1) + normal_lpdf(Y[n] | mu1[n], sigma1);")
expect_match2(scode, "ps[2] = log(theta2) + normal_lpdf(Y[n] | mu2[n], sigma2[n]);")
expect_match2(scode, "target += log_sum_exp(ps);")
expect_match2(scode, "simplex[2] prior_theta = dirichlet_rng(con_theta);")
data$z <- abs(data$y)
scode <- make_stancode(bf(z | weights(c) ~ x, shape1 ~ x, theta1 = 1, theta2 = 2),
data = data, mixture(Gamma("log"), weibull))
expect_match(scode, "data \\{[^\\}]*real theta1;")
expect_match(scode, "data \\{[^\\}]*real theta2;")
expect_match2(scode, "ps[1] = log(theta1) + gamma_lpdf(Y[n] | shape1[n], mu1[n]);")
expect_match2(scode, "target += weights[n] * log_sum_exp(ps);")
scode <- make_stancode(bf(abs(y) | se(c) ~ x), data = data,
mixture(gaussian, student))
expect_match2(scode, "ps[1] = log(theta1) + normal_lpdf(Y[n] | mu1[n], se[n]);")
expect_match2(scode, "ps[2] = log(theta2) + student_t_lpdf(Y[n] | nu2, mu2[n], se[n]);")
fam <- mixture(gaussian, student, exgaussian)
scode <- make_stancode(bf(y ~ x), data = data, family = fam)
expect_match(scode, "parameters \\{[^\\}]*real Intercept_mu3;")
expect_match2(scode,
"ps[2] = log(theta2) + student_t_lpdf(Y[n] | nu2, mu2[n], sigma2);"
)
expect_match2(scode,
"ps[3] = log(theta3) + exp_mod_normal_lpdf(Y[n] | mu3[n] - beta3, sigma3, inv(beta3));"
)
scode <- make_stancode(bf(y ~ x, theta1 ~ x, theta3 ~ x),
data = data, family = fam)
expect_match2(scode, "log_sum_exp_theta = log(exp(theta1[n]) + exp(theta2[n]) + exp(theta3[n]));")
expect_match2(scode, "theta2 = rep_vector(0, N);")
expect_match2(scode, "theta3[n] = theta3[n] - log_sum_exp_theta;")
expect_match2(scode, "ps[1] = theta1[n] + normal_lpdf(Y[n] | mu1[n], sigma1);")
fam <- mixture(cumulative, sratio)
scode <- make_stancode(y ~ x, data, family = fam)
expect_match2(scode, "ordered_logistic_lpmf(Y[n] | mu1[n], Intercept_mu1);")
expect_match2(scode, "sratio_logit_lpmf(Y[n] | mu2[n], disc2, Intercept_mu2);")
# censored mixture model
fam <- mixture(gaussian, gaussian)
scode <- make_stancode(y | cens(2, y2 = 2) ~ x, data, fam)
expect_match2(scode,
"ps[2] = log(theta2) + normal_lccdf(Y[n] | mu2[n], sigma2);"
)
expect_match2(scode, paste0(
"ps[2] = log(theta2) + log_diff_exp(\n",
" normal_lcdf(rcens[n] | mu2[n], sigma2),"
))
# truncated mixture model
scode <- make_stancode(y | trunc(3) ~ x, data, fam)
expect_match2(scode, paste0(
"ps[1] = log(theta1) + normal_lpdf(Y[n] | mu1[n], sigma1) -\n",
" normal_lccdf(lb[n] | mu1[n], sigma1);"
))
# non-linear mixture model
bform <- bf(y ~ 1) +
nlf(mu1 ~ eta^2) +
nlf(mu2 ~ log(eta) + a) +
lf(eta + a ~ x) +
mixture(gaussian, nmix = 2)
bprior <- prior(normal(0, 1), nlpar = "eta") +
prior(normal(0, 1), nlpar = "a")
scode <- make_stancode(bform, data = data, prior = bprior)
expect_match2(scode, "mu1[n] = nlp_eta[n] ^ 2;")
expect_match2(scode, "mu2[n] = log(nlp_eta[n]) + nlp_a[n];")
})
test_that("sparse matrix multiplication is applied correctly", {
data <- data.frame(y = rnorm(10), x = rnorm(10))
# linear model
scode <- make_stancode(
bf(y ~ x, sparse = TRUE) + lf(sigma ~ x, sparse = TRUE),
data, prior = prior(normal(0, 5), coef = "Intercept")
)
expect_match2(scode, "wX = csr_extract_w(X);")
expect_match2(scode,
"mu = csr_matrix_times_vector(rows(X), cols(X), wX, vX, uX, b);"
)
expect_match2(scode,
"uX_sigma[size(csr_extract_u(X_sigma))] = csr_extract_u(X_sigma);"
)
expect_match2(scode,
paste0(
"sigma = csr_matrix_times_vector(rows(X_sigma), cols(X_sigma), ",
"wX_sigma, vX_sigma, uX_sigma, b_sigma);"
)
)
expect_match2(scode, "target += normal_lpdf(b[1] | 0, 5);")
expect_match2(scode, "target += normal_lpdf(Y | mu, sigma);")
# non-linear model
scode <- make_stancode(
bf(y ~ a, lf(a ~ x, sparse = TRUE), nl = TRUE),
data, prior = prior(normal(0, 1), nlpar = a)
)
expect_match2(scode,
"vX_a[size(csr_extract_v(X_a))] = csr_extract_v(X_a);"
)
expect_match2(scode,
"nlp_a = csr_matrix_times_vector(rows(X_a), cols(X_a), wX_a, vX_a, uX_a, b_a);"
)
})
test_that("QR decomposition is included in the Stan code", {
data <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10))
bform <- bf(y ~ x1 + x2, decomp = "QR") +
lf(sigma ~ 0 + x1 + x2, decomp = "QR")
# simple priors
scode <- make_stancode(bform, data, prior = prior(normal(0, 2)))
expect_match2(scode, "XQ = qr_thin_Q(Xc) * sqrt(N - 1);")
expect_match2(scode, "b = XR_inv * bQ;")
expect_match2(scode, "target += normal_lpdf(bQ | 0, 2);")
expect_match2(scode, "XQ * bQ")
expect_match2(scode, "XR_sigma = qr_thin_R(X_sigma) / sqrt(N - 1);")
# horseshoe prior
scode <- make_stancode(bform, data, prior = prior(horseshoe(1)))
expect_match2(scode, "target += normal_lpdf(zb | 0, 1);")
expect_match2(scode, "bQ = horseshoe(")
})
test_that("Stan code for Gaussian processes is correct", {
set.seed(1234)
dat <- data.frame(y = rnorm(40), x1 = rnorm(40), x2 = rnorm(40),
z = factor(rep(3:6, each = 10)))
prior <- prior(gamma(0.1, 0.1), sdgp)
scode <- make_stancode(y ~ gp(x1) + gp(x2, by = x1), dat, prior = prior)
expect_match2(scode, "target += inv_gamma_lpdf(lscale_1[1]")
expect_match2(scode, "target += gamma_lpdf(sdgp_1 | 0.1, 0.1)")
expect_match2(scode, "Cgp_2 .* gp(Xgp_2, sdgp_2[1], lscale_2[1], zgp_2)")
prior <- prior + prior(normal(0, 1), lscale, coef = gpx1)
scode <- make_stancode(y ~ gp(x1) + gp(x2, by = x1, gr = TRUE),
data = dat, prior = prior)
expect_match2(scode, "target += normal_lpdf(lscale_1[1][1] | 0, 1)")
expect_match2(scode, "+ Cgp_2 .* gp(Xgp_2, sdgp_2[1], lscale_2[1], zgp_2)[Jgp_2]")
# non-isotropic GP
scode <- make_stancode(y ~ gp(x1, x2, by = z, iso = FALSE), dat)
expect_match2(scode, "target += inv_gamma_lpdf(lscale_1[1][2]")
expect_match2(scode, "target += inv_gamma_lpdf(lscale_1[4][2]")
# Suppress Stan parser warnings that can currently not be avoided
scode <- make_stancode(y ~ gp(x1, x2) + gp(x1, by = z),
dat, silent = TRUE)
expect_match2(scode, "gp(Xgp_1, sdgp_1[1], lscale_1[1], zgp_1)")
expect_match2(scode, paste0(
"mu[Igp_2_2] = mu[Igp_2_2] + Cgp_2_2 .* gp(Xgp_2_2, ",
"sdgp_2[2], lscale_2[2], zgp_2_2);"
))
# approximate GPS
scode <- make_stancode(
y ~ gp(x1, k = 10, c = 5/4) + gp(x2, by = x1, k = 10, c = 5/4), dat
)
expect_match2(scode, "target += inv_gamma_lpdf(lscale_1")
expect_match2(scode,
"gpa(Xgp_1, sdgp_1[1], lscale_1[1], zgp_1, slambda_1)"
)
expect_match2(scode,
"Cgp_2 .* gpa(Xgp_2, sdgp_2[1], lscale_2[1], zgp_2, slambda_2)"
)
prior <- c(prior(normal(0, 10), lscale, coef = gpx1, nlpar = a),
prior(gamma(0.1, 0.1), sdgp, nlpar = a),
prior(normal(0, 1), b, nlpar = a))
scode <- make_stancode(bf(y ~ a, a ~ gp(x1), nl = TRUE),
data = dat, prior = prior)
expect_match2(scode, "target += normal_lpdf(lscale_a_1[1][1] | 0, 10)")
expect_match2(scode, "target += gamma_lpdf(sdgp_a_1 | 0.1, 0.1)")
expect_match2(scode, "gp(Xgp_a_1, sdgp_a_1[1], lscale_a_1[1], zgp_a_1)")
prior <- prior(gamma(2, 2), lscale, coef = gpx1z5, nlpar = "a")
scode <- make_stancode(bf(y ~ a, a ~ gp(x1, by = z, gr = TRUE), nl = TRUE),
data = dat, prior = prior, silent = TRUE)
expect_match2(scode,
"nlp_a[Igp_a_1_1] = nlp_a[Igp_a_1_1] + Cgp_a_1_1 .* gp(Xgp_a_1_1,"
)
expect_match2(scode,
"gp(Xgp_a_1_3, sdgp_a_1[3], lscale_a_1[3], zgp_a_1_3)[Jgp_a_1_3]"
)
expect_match2(scode, "target += gamma_lpdf(lscale_a_1[3][1] | 2, 2);")
expect_match2(scode, "target += normal_lpdf(zgp_a_1_3 | 0, 1);")
# test warnings
prior <- prior(normal(0, 1), lscale)
expect_warning(
make_stancode(y ~ gp(x1), data = dat, prior = prior),
"The global prior 'normal(0, 1)' of class 'lscale' will not be used",
fixed = TRUE
)
})
test_that("Stan code for SAR models is correct", {
dat <- data.frame(y = rnorm(10), x = rnorm(10))
W <- matrix(0, nrow = 10, ncol = 10)
scode <- make_stancode(
y ~ x + sar(W), data = dat,
prior = prior(normal(0.5, 1), lagsar)
)
expect_match2(scode,
"target += normal_lagsar_lpdf(Y | mu, sigma, lagsar, Msar, eigenMsar)"
)
expect_match2(scode, "target += normal_lpdf(lagsar | 0.5, 1)")
scode <- make_stancode(
y ~ x + sar(W, type = "lag"),
data = dat, family = student()
)
expect_match2(scode,
"target += student_t_lagsar_lpdf(Y | nu, mu, sigma, lagsar, Msar, eigenMsar)"
)
scode <- make_stancode(y ~ x + sar(W, type = "error"), data = dat)
expect_match2(scode,
"target += normal_errorsar_lpdf(Y | mu, sigma, errorsar, Msar, eigenMsar)"
)
scode <- make_stancode(
y ~ x + sar(W, "error"), data = dat, family = student(),
prior = prior(beta(2, 3), errorsar)
)
expect_match2(scode,
"target += student_t_errorsar_lpdf(Y | nu, mu, sigma, errorsar, Msar, eigenMsar)"
)
expect_match2(scode, "target += beta_lpdf(errorsar | 2, 3)")
expect_error(
make_stancode(bf(y ~ sar(W), sigma ~ x), data = dat),
"SAR models are not implemented when predicting 'sigma'"
)
})
test_that("Stan code for CAR models is correct", {
dat = data.frame(y = rnorm(10), x = rnorm(10))
edges <- cbind(1:10, 10:1)
W <- matrix(0, nrow = 10, ncol = 10)
for (i in seq_len(nrow(edges))) {
W[edges[i, 1], edges[i, 2]] <- 1
}
rownames(W) <- seq_len(nrow(W))
scode <- make_stancode(y ~ x + car(W), dat)
expect_match2(scode, "real sparse_car_lpdf(vector phi")
expect_match2(scode, "target += sparse_car_lpdf(")
expect_match2(scode, "mu[n] += rcar[Jloc[n]]")
scode <- make_stancode(y ~ x + car(W, type = "esicar"), dat)
expect_match2(scode, "real sparse_icar_lpdf(vector phi")
expect_match2(scode, "target += sparse_icar_lpdf(")
expect_match2(scode, "mu[n] += rcar[Jloc[n]]")
expect_match2(scode, "rcar[Nloc] = - sum(zcar)")
scode <- make_stancode(y ~ x + car(W, type = "icar"), dat)
expect_match2(scode, "target += -0.5 * dot_self(zcar[edges1] - zcar[edges2])")
expect_match2(scode, "target += normal_lpdf(sum(zcar) | 0, 0.001 * Nloc)")
expect_match2(scode, "mu[n] += rcar[Jloc[n]]")
expect_match2(scode, "rcar = zcar * sdcar")
scode <- make_stancode(y ~ x + car(W, type = "bym2"), dat)
expect_match2(scode, "target += -0.5 * dot_self(zcar[edges1] - zcar[edges2])")
expect_match2(scode, "target += normal_lpdf(sum(zcar) | 0, 0.001 * Nloc)")
expect_match2(scode, "mu[n] += rcar[Jloc[n]]")
expect_match2(scode, "target += beta_lpdf(rhocar | 1, 1)")
expect_match2(scode, paste0(
"rcar = (sqrt(1 - rhocar) * nszcar + ",
"sqrt(rhocar * inv(car_scale)) * zcar) * sdcar"
))
# apply a CAR term on a distributional parameter other than 'mu'
scode <- make_stancode(bf(y ~ x, sigma ~ car(W)), dat)
expect_match2(scode, "real sparse_car_lpdf(vector phi")
expect_match2(scode, "target += sparse_car_lpdf(")
expect_match2(scode, "sigma[n] += rcar_sigma[Jloc_sigma[n]]")
})
test_that("Stan code for skew_normal models is correct", {
dat = data.frame(y = rnorm(10), x = rnorm(10))
scode <- make_stancode(y ~ x, dat, skew_normal())
expect_match2(scode, "delta = alpha / sqrt(1 + alpha^2);")
expect_match2(scode, "omega = sigma / sqrt(1 - sqrt_2_div_pi^2 * delta^2);")
expect_match2(scode, "mu[n] = mu[n] - omega * delta * sqrt_2_div_pi;")
scode <- make_stancode(bf(y ~ x, sigma ~ x), dat, skew_normal())
expect_match2(scode, "omega[n] = sigma[n] / sqrt(1 - sqrt_2_div_pi^2 * delta^2);")
expect_match2(scode, "mu[n] = mu[n] - omega[n] * delta * sqrt_2_div_pi;")
scode <- make_stancode(bf(y | se(x) ~ x, alpha ~ x), dat, skew_normal())
expect_match2(scode, "delta[n] = alpha[n] / sqrt(1 + alpha[n]^2);")
expect_match2(scode, "omega[n] = se[n] / sqrt(1 - sqrt_2_div_pi^2 * delta[n]^2);")
expect_match2(scode, "mu[n] = mu[n] - omega[n] * delta[n] * sqrt_2_div_pi;")
scode <- make_stancode(y ~ x, dat, mixture(skew_normal, nmix = 2))
expect_match2(scode, "omega1 = sigma1 / sqrt(1 - sqrt_2_div_pi^2 * delta1^2);")
expect_match2(scode, "mu2[n] = mu2[n] - omega2 * delta2 * sqrt_2_div_pi;")
})
test_that("Stan code for missing value terms works correctly", {
dat = data.frame(y = rnorm(10), x = rnorm(10), g = 1:10, z = 1)
dat$x[c(1, 3, 9)] <- NA
bform <- bf(y ~ mi(x)*g) + bf(x | mi() ~ g) + set_rescor(FALSE)
scode <- make_stancode(bform, dat)
expect_match2(scode, "Yl_x[Jmi_x] = Ymi_x;")
expect_match2(scode, "(bsp_y[1]) * Yl_x[n] + (bsp_y[2]) * Yl_x[n] * Csp_y_1[n];")
expect_match2(scode, "target += normal_lpdf(Yl_x | mu_x, sigma_x);")
bform <- bf(y ~ mi(x) + (mi(x) | g)) + bf(x | mi() ~ 1) + set_rescor(FALSE)
scode <- make_stancode(bform, dat)
expect_match2(scode,
"(bsp_y[1] + r_1_y_2[J_1_y[n]]) * Yl_x[n] + r_1_y_1[J_1_y[n]] * Z_1_y_1[n];"
)
bform <- bf(y ~ a, a ~ mi(x), nl = TRUE) + bf(x | mi() ~ 1) + set_rescor(FALSE)
bprior <- prior(normal(0, 1), nlpar = "a", resp = "y")
scode <- make_stancode(bform, dat, prior = bprior)
expect_match2(scode, "nlp_y_a[n] += (bsp_y_a[1]) * Yl_x[n];")
expect_match2(scode, "target += normal_lpdf(bsp_y_a | 0, 1);")
bform <- bf(y ~ mi(x)*mo(g)) + bf(x | mi() ~ 1) + set_rescor(FALSE)
scode <- make_stancode(bform, dat)
expect_match2(scode, "(bsp_y[3]) * Yl_x[n] * mo(simo_y_2, Xmo_y_2[n]);")
bform <- bf(y ~ 1, sigma ~ 1) + bf(x | mi() ~ 1) + set_rescor(TRUE)
scode <- make_stancode(bform, dat)
expect_match2(scode, "Yl[n][2] = Yl_x[n];")
expect_match2(scode, "sigma[n] = [sigma_y[n], sigma_x]';")
expect_match2(scode, "LSigma[n] = diag_pre_multiply(sigma[n], Lrescor);")
bform <- bf(x | mi() ~ y, family = "lognormal")
scode <- make_stancode(bform, dat)
expect_match2(scode, "vector[Nmi] Ymi;")
bform <- bf(y ~ I(log(mi(x))) * g) +
bf(x | mi() + trunc(lb = 1) ~ y, family = "lognormal")
scode <- make_stancode(bform, dat)
expect_match2(scode, "vector[Nmi_x] Ymi_x;")
expect_match2(scode,
"(bsp_y[1]) * (log(Yl_x[n])) + (bsp_y[2]) * (log(Yl_x[n])) * Csp_y_1[n]"
)
bform <- bf(y ~ mi(x)*g) +
bf(x | mi() + cens(z) ~ y, family = "beta")
scode <- make_stancode(bform, dat)
expect_match2(scode, "vector[Nmi_x] Ymi_x;")
expect_match2(scode,
"target += beta_lpdf(Yl_x[n] | mu_x[n] * phi_x, (1 - mu_x[n]) * phi_x);"
)
})
test_that("Stan code for overimputation works correctly", {
dat = data.frame(y = rnorm(10), x_x = rnorm(10), g = 1:10, z = 1)
dat$x[c(1, 3, 9)] <- NA
bform <- bf(y ~ mi(x_x)*g) + bf(x_x | mi(g) ~ 1) + set_rescor(FALSE)
scode <- make_stancode(bform, dat, sample_prior = "yes")
expect_match2(scode, "target += normal_lpdf(Yl_xx | mu_xx, sigma_xx)")
expect_match2(scode,
"target += normal_lpdf(Y_xx[Jme_xx] | Yl_xx[Jme_xx], noise_xx[Jme_xx])"
)
expect_match2(scode, "vector[N_xx] Yl_xx;")
})
test_that("Stan code for advanced count data distribution is correct", {
scode <- make_stancode(
count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = brmsfamily("discrete_weibull")
)
expect_match2(scode, "mu[n] = inv_logit(mu[n]);")
expect_match2(scode, "target += discrete_weibull_lpmf(Y[n] | mu[n], shape);")
scode <- make_stancode(
count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = brmsfamily("com_poisson")
)
expect_match2(scode, "target += com_poisson_log_lpmf(Y[n] | mu[n], shape);")
})
test_that("argument 'stanvars' is handled correctly", {
bprior <- prior(normal(mean_intercept, 10), class = "Intercept")
mean_intercept <- 5
stanvars <- stanvar(mean_intercept)
scode <- make_stancode(count ~ Trt, data = epilepsy,
prior = bprior, stanvars = stanvars)
expect_match2(scode, "real mean_intercept;")
# define a multi_normal prior with known covariance matrix
bprior <- prior(multi_normal(M, V), class = "b")
stanvars <- stanvar(rep(0, 2), "M", scode = " vector[K] M;") +
stanvar(diag(2), "V", scode = " matrix[K, K] V;")
scode <- make_stancode(count ~ Trt + zBase, epilepsy,
prior = bprior, stanvars = stanvars)
expect_match2(scode, "vector[K] M;")
expect_match2(scode, "matrix[K, K] V;")
# define a hierachical prior on the regression coefficients
bprior <- set_prior("normal(0, tau)", class = "b") +
set_prior("target += normal_lpdf(tau | 0, 10)", check = FALSE)
stanvars <- stanvar(scode = "real tau;",
block = "parameters")
scode <- make_stancode(count ~ Trt + zBase, epilepsy,
prior = bprior, stanvars = stanvars)
expect_match2(scode, "real tau;")
expect_match2(scode, "target += normal_lpdf(b | 0, tau);")
# use the non-centered parameterization for 'b'
# unofficial feature not supported anymore for the time being
# bprior <- set_prior("target += normal_lpdf(zb | 0, 1)", check = FALSE) +
# set_prior("target += normal_lpdf(tau | 0, 10)", check = FALSE)
# stanvars <- stanvar(scode = "vector[Kc] zb;", block = "parameters") +
# stanvar(scode = "real tau;", block = "parameters") +
# stanvar(scode = "vector[Kc] b = zb * tau;",
# block="tparameters", name = "b")
# scode <- make_stancode(count ~ Trt, epilepsy,
# prior = bprior, stanvars = stanvars)
# expect_match2(scode, "vector[Kc] b = zb * tau;")
# stanvars <- stanvar(scode = "vector[Ksp] zbsp;", block = "parameters") +
# stanvar(scode = "real tau;", block = "parameters") +
# stanvar(scode = "vector[Ksp] bsp = zbsp * tau;",
# block = "tparameters", name = "bsp")
# scode <- make_stancode(count ~ mo(Base), epilepsy, stanvars = stanvars)
# expect_match2(scode, "vector[Ksp] bsp = zbsp * tau;")
})
test_that("custom families are handled correctly", {
dat <- data.frame(size = 10, y = sample(0:10, 20, TRUE), x = rnorm(20))
# define a custom beta-binomial family
log_lik_beta_binomial2 <- function(i, draws) {
mu <- draws$dpars$mu[, i]
tau <- draws$dpars$tau
trials <- draws$data$vint1[i]
y <- draws$data$Y[i]
beta_binomial2_lpmf(y, mu, tau, trials)
}
predict_beta_binomial2 <- function(i, draws, ...) {
mu <- draws$dpars$mu[, i]
tau <- draws$dpars$tau
trials <- draws$data$vint1[i]
beta_binomial2_rng(mu, tau, trials)
}
fitted_beta_binomial2 <- function(draws) {
mu <- draws$dpars$mu
trials <- draws$data$vint1
trials <- matrix(
trials, nrow = nrow(mu), ncol = ncol(mu), byrow = TRUE
)
mu * trials
}
beta_binomial2 <- custom_family(
"beta_binomial2",
dpars = c("mu", "tau"),
links = c("logit", "log"),
lb = c(NA, 0),
type = "int",
vars = c("vint1[n]", "vreal1[n]"),
log_lik = log_lik_beta_binomial2,
fitted = fitted_beta_binomial2,
predict = predict_beta_binomial2
)
# define custom stan functions
# real R is just to also test the vreal addition argument
stan_funs <- "
real beta_binomial2_lpmf(int y, real mu, real phi, int N, real R) {
return beta_binomial_lpmf(y | N, mu * phi, (1 - mu) * phi);
}
int beta_binomial2_rng(real mu, real phi, int N, real R) {
return beta_binomial_rng(N, mu * phi, (1 - mu) * phi);
}
"
stanvars <- stanvar(scode = stan_funs, block = "functions")
scode <- make_stancode(
y | vint(size) + vreal(size) ~ x, data = dat, family = beta_binomial2,
prior = prior(gamma(0.1, 0.1), class = "tau"),
stanvars = stanvars
)
expect_match2(scode, "int vint1[N];")
expect_match2(scode, "real tau;")
expect_match2(scode, "mu[n] = inv_logit(mu[n]);")
expect_match2(scode, "target += gamma_lpdf(tau | 0.1, 0.1);")
expect_match2(scode,
"target += beta_binomial2_lpmf(Y[n] | mu[n], tau, vint1[n], vreal1[n]);"
)
scode <- make_stancode(
bf(y | vint(size) + vreal(size) ~ x, tau ~ x),
data = dat, family = beta_binomial2, stanvars = stanvars
)
expect_match2(scode, "tau[n] = exp(tau[n]);")
expect_match2(scode,
"target += beta_binomial2_lpmf(Y[n] | mu[n], tau[n], vint1[n], vreal1[n]);"
)
# check custom families in mixture models
scode <- make_stancode(
y | vint(size) + vreal(size) + trials(size) ~ x, data = dat,
family = mixture(binomial, beta_binomial2),
stanvars = stanvars
)
expect_match2(scode,
"log(theta2) + beta_binomial2_lpmf(Y[n] | mu2[n], tau2, vint1[n], vreal1[n]);"
)
})
test_that("likelihood of distributional beta models is correct", {
# test issue #404
dat <- data.frame(prop = rbeta(100, shape1 = 2, shape2 = 2))
scode <- make_stancode(
bf(prop ~ 1, phi ~ 1), data = dat, family = Beta()
)
expect_match2(scode, "beta_lpdf(Y[n] | mu[n] * phi[n], (1 - mu[n]) * phi[n])")
})
test_that("student-t group-level effects work without errors", {
scode <- make_stancode(count ~ Trt + (1|gr(patient, dist = "st")), epilepsy)
expect_match2(scode, "dfm_1 = sqrt(df_1 * udf_1);")
expect_match2(scode, "dfm_1 .* (sd_1[1] * (z_1[1]));")
expect_match2(scode, "target += gamma_lpdf(df_1 | 2, 0.1);")
expect_match2(scode, "target += inv_chi_square_lpdf(udf_1 | df_1);")
bprior <- prior(normal(20, 5), class = df, group = patient)
scode <- make_stancode(
count ~ Trt + (Trt|gr(patient, dist = "st")),
epilepsy, prior = bprior
)
expect_match2(scode,
"rep_matrix(dfm_1, M_1) .* (diag_pre_multiply(sd_1, L_1) * z_1)';"
)
expect_match2(scode, "target += normal_lpdf(df_1 | 20, 5);")
})
test_that("centering design matrices can be changed correctly", {
dat <- data.frame(y = 1:10, x = 1:10)
scode <- make_stancode(
bf(y ~ x, center = FALSE), data = dat, family = weibull(),
prior = prior(normal(0,1), coef = Intercept)
)
expect_match2(scode, "mu = X * b;")
expect_match2(scode, "target += normal_lpdf(b[1] | 0, 1);")
bform <- bf(y ~ eta, nl = TRUE) + lf(eta ~ x, center = TRUE)
scode <- make_stancode(bform, data = dat)
expect_match2(scode, "nlp_eta = Intercept_eta + Xc_eta * b_eta;")
})
test_that("to_vector() is correctly removed from prior of SD parameters", {
# see https://discourse.mc-stan.org/t/prior-for-sd-generate-parsing-text-error/12292/5
dat <- data.frame(
y = rnorm(100),
ID = 1:10,
group = rep(1:2, each = 5)
)
bform <- bf(
y ~ 1 + (1 | p | gr(ID, by=group)),
sigma ~ 1 + (1 | p | gr(ID, by=group))
)
bprior <- c(
prior(normal(0, 0.1), class = sd) ,
prior(normal(0, 0.01), class = sd, dpar = sigma)
)
scode <- make_stancode(
bform,
data = dat,
prior = bprior,
sample_prior = TRUE
)
expect_match2(scode, "prior_sd_1_1 = normal_rng(0,0.1);")
expect_match2(scode, "prior_sd_1_2 = normal_rng(0,0.01);")
})
brms/tests/testthat/tests.brmsfit-helpers.R 0000644 0001762 0000144 00000007215 13522262066 020622 0 ustar ligges users context("Tests for brmsfit helper functions")
test_that("first_greater returns expected results", {
A <- cbind(1:10, 11:20, 21:30)
x <- c(5, 25, 7, 15, 7, 10, 15, 19, 3, 11)
expect_equal(first_greater(A, x), c(2, 3, 2, 3, 2, 2, 2, 3, 1, 2))
expect_equal(first_greater(A, x, i = 2), c(2, 3, 2, 3, 2, 2, 2, 3, 2, 2))
})
test_that("array2list performs correct conversion", {
A <- array(1:27, dim = c(3,3,3))
B <- list(matrix(1:9,3,3), matrix(10:18,3,3), matrix(19:27,3,3))
expect_equal(brms:::array2list(A), B)
})
test_that("probit and probit_approx produce similar results", {
expect_equal(brms:::ilink(-10:10, "probit"),
brms:::ilink(-10:10, "probit_approx"),
tolerance = 1e-3)
})
test_that("autocorrelation matrices are computed correctly", {
ar <- 0.5
ma <- 0.3
ar_mat <- brms:::get_cor_matrix_ar1(ar = matrix(ar), nobs = 4)
expected_ar_mat <- 1 / (1 - ar^2) *
cbind(c(1, ar, ar^2, ar^3),
c(ar, 1, ar, ar^2),
c(ar^2, ar, 1, ar),
c(ar^3, ar^2, ar, 1))
expect_equal(ar_mat[1, , ], expected_ar_mat)
ma_mat <- brms:::get_cor_matrix_ma1(ma = matrix(ma), nobs = 4)
expected_ma_mat <- cbind(c(1+ma^2, ma, 0, 0),
c(ma, 1+ma^2, ma, 0),
c(0, ma, 1+ma^2, ma),
c(0, 0, ma, 1+ma^2))
expect_equal(ma_mat[1, , ], expected_ma_mat)
arma_mat <- brms:::get_cor_matrix_arma1(
ar = matrix(ar), ma = matrix(ma), nobs = 4
)
g0 <- 1 + ma^2 + 2 * ar * ma
g1 <- (1 + ar * ma) * (ar + ma)
expected_arma_mat <- 1 / (1 - ar^2) *
cbind(c(g0, g1, g1 * ar, g1 * ar^2),
c(g1, g0, g1, g1 * ar),
c(g1 * ar, g1, g0, g1),
c(g1 * ar^2, g1 * ar, g1, g0))
expect_equal(arma_mat[1, , ], expected_arma_mat)
cosy <- 0.6
cosy_mat <- brms:::get_cor_matrix_cosy(cosy = as.matrix(cosy), nobs = 4)
expected_cosy_mat <- matrix(cosy, 4, 4)
diag(expected_cosy_mat) <- 1
expect_equal(cosy_mat[1, , ], expected_cosy_mat)
ident_mat <- brms:::get_cor_matrix_ident(nsamples = 10, nobs = 4)
expected_ident_mat <- diag(1, 4)
expect_equal(ident_mat[1, , ], expected_ident_mat)
})
test_that("evidence_ratio returns expected results", {
ps <- -4:10
prs <- -2:12
expect_true(evidence_ratio(ps, prior_samples = prs) > 1)
expect_true(is.na(evidence_ratio(ps)))
expect_equal(evidence_ratio(ps, cut = 0.5, wsign = "greater"), 10/5)
expect_equal(evidence_ratio(ps, cut = 0.5, wsign = "less"), 5/10)
})
test_that("find_vars finds all valid variable names in a string", {
string <- "x + b.x - .5 + abc(a__3) : 1/2 - 0.2"
expect_equal(find_vars(string), c("x", "b.x", "a__3"))
})
test_that(".predictor_arma runs without errors", {
ns <- 20
nobs <- 30
Y = rnorm(nobs)
J_lag = c(1:3, 3, 3, rep(c(0:3, 3), 4), 0:3, 0)
ar <- matrix(rnorm(ns * 3), nrow = ns, ncol = 3)
ma <- matrix(rnorm(ns * 1), nrow = ns, ncol = 1)
eta <- matrix(rnorm(ns * nobs), nrow = ns, ncol = nobs)
expect_equal(.predictor_arma(eta, Y = Y, J_lag = J_lag), eta)
expect_silent(.predictor_arma(eta, Y = Y, J_lag = J_lag, ar = ar))
expect_silent(.predictor_arma(eta, Y = Y, J_lag = J_lag, ma = ma))
expect_silent(.predictor_arma(eta, Y = Y, J_lag = J_lag, ar = ar, ma = ma))
})
test_that("make_conditions works correctly", {
conds <- make_conditions(epilepsy, c("zBase", "zAge"))
expect_equal(dim(conds), c(9, 3))
expect_equal(conds$cond__[3], "zBase = -1 & zAge = 1")
})
brms/tests/testthat/tests.priors.R 0000644 0001762 0000144 00000011436 13611527526 017036 0 ustar ligges users # most tests of prior related stuff can be found in tests.make_stancode.R
context("Tests for prior generating functions")
test_that("get_prior finds all classes for which priors can be specified", {
expect_equal(
sort(
get_prior(
count ~ zBase * Trt + (1|patient) + (1+Trt|visit),
data = epilepsy, family = "poisson"
)$class
),
sort(c(rep("b", 4), c("cor", "cor"), "Intercept", rep("sd", 6)))
)
expect_equal(
sort(
get_prior(
rating ~ treat + period + cse(carry), data = inhaler,
family = sratio(threshold = "equidistant")
)$class
),
sort(c(rep("b", 4), "delta", rep("Intercept", 1)))
)
})
test_that("set_prior allows arguments to be vectors", {
bprior <- set_prior("normal(0, 2)", class = c("b", "sd"))
expect_is(bprior, "brmsprior")
expect_equal(bprior$prior, rep("normal(0, 2)", 2))
expect_equal(bprior$class, c("b", "sd"))
})
test_that("print for class brmsprior works correctly", {
expect_output(print(set_prior("normal(0,1)")), fixed = TRUE,
"b ~ normal(0,1)")
expect_output(print(set_prior("normal(0,1)", coef = "x")),
"b_x ~ normal(0,1)", fixed = TRUE)
expect_output(print(set_prior("cauchy(0,1)", class = "sd", group = "x")),
"sd_x ~ cauchy(0,1)", fixed = TRUE)
expect_output(print(set_prior("increment_log_prob(normal_log(0,1))")),
"increment_log_prob(normal_log(0,1))", fixed = TRUE)
})
test_that("get_prior returns correct nlpar names for random effects pars", {
# reported in issue #47
data <- data.frame(y = rnorm(10), x = rnorm(10), g = rep(1:2, 5))
gp <- get_prior(bf(y ~ a - b^x, a + b ~ (1+x|g), nl = TRUE),
data = data)
expect_equal(sort(unique(gp$nlpar)), c("", "a", "b"))
})
test_that("get_prior returns correct fixed effect names for GAMMs", {
dat <- data.frame(y = rnorm(10), x = rnorm(10),
z = rnorm(10), g = rep(1:2, 5))
prior <- get_prior(y ~ z + s(x) + (1|g), data = dat)
expect_equal(prior[prior$class == "b", ]$coef,
c("", "sx_1", "z"))
prior <- get_prior(bf(y ~ lp, lp ~ z + s(x) + (1|g), nl = TRUE),
data = dat)
expect_equal(prior[prior$class == "b", ]$coef,
c("", "Intercept", "sx_1", "z"))
})
test_that("get_prior returns correct prior names for auxiliary parameters", {
dat <- data.frame(y = rnorm(10), x = rnorm(10),
z = rnorm(10), g = rep(1:2, 5))
prior <- get_prior(bf(y ~ 1, phi ~ z + (1|g)), data = dat, family = Beta())
prior <- prior[prior$dpar == "phi", ]
pdata <- data.frame(class = c("b", "b", "Intercept", rep("sd", 3)),
coef = c("", "z", "", "", "", "Intercept"),
group = c(rep("", 4), "g", "g"),
stringsAsFactors = FALSE)
pdata <- pdata[with(pdata, order(class, group, coef)), ]
expect_equivalent(prior[, c("class", "coef", "group")], pdata)
})
test_that("get_prior returns correct priors for multivariate models", {
dat <- data.frame(y1 = rnorm(10), y2 = c(1, rep(1:3, 3)),
x = rnorm(10), g = rep(1:2, 5))
bform <- bf(mvbind(y1, y2) ~ x + (x|ID1|g))
# check global priors
prior <- get_prior(bform, dat, family = gaussian())
expect_equal(prior[prior$resp == "y1" & prior$class == "b", "coef"], c("", "x"))
expect_equal(prior[prior$class == "rescor", "prior"], "lkj(1)")
# check family and autocor specific priors
family <- list(gaussian, Beta())
bform <- bf(y1 ~ x + (x|ID1|g) + ar()) + bf(y2 ~ 1)
prior <- get_prior(bform, dat, family = family)
expect_true(any(with(prior, class == "sigma" & resp == "y1")))
expect_true(any(with(prior, class == "ar" & resp == "y1")))
expect_true(any(with(prior, class == "phi" & resp == "y2")))
expect_true(!any(with(prior, class == "ar" & resp == "y2")))
})
test_that("get_prior returns correct priors for categorical models", {
# check global priors
dat <- data.frame(y2 = c(1, rep(1:3, 3)), x = rnorm(10), g = rep(1:2, 5))
prior <- get_prior(y2 ~ x + (x|ID1|g), data = dat, family = categorical())
expect_equal(prior[prior$dpar == "mu2" & prior$class == "b", "coef"], c("", "x"))
})
test_that("set_prior alias functions produce equivalent results", {
expect_equal(set_prior("normal(0, 1)", class = "sd"),
prior(normal(0, 1), class = sd))
expect_equal(set_prior("normal(0, 1)", class = "sd", nlpar = "a"),
prior(normal(0, 1), class = "sd", nlpar = a))
expect_equal(set_prior("normal(0, 1)", class = "sd", nlpar = "a"),
prior_(~normal(0, 1), class = ~sd, nlpar = quote(a)))
expect_equal(set_prior("normal(0, 1)", class = "sd"),
prior_string("normal(0, 1)", class = "sd"))
})
brms/tests/testthat/tests.brmsformula.R 0000644 0001762 0000144 00000004201 13552555374 020047 0 ustar ligges users context("Tests for brmsformula")
test_that("brmsformula validates formulas of non-linear parameters", {
expect_error(bf(y ~ a, ~ 1, a ~ 1),
"Additional formulas must be named")
expect_error(bf(y ~ a^x, a.b ~ 1),
"not contain dots or underscores")
expect_error(bf(y ~ a^(x+b), a_b ~ 1),
"not contain dots or underscores")
})
test_that("brmsformula validates formulas of auxiliary parameters", {
expect_error(bf(y ~ a, ~ 1, sigma ~ 1),
"Additional formulas must be named")
})
test_that("brmsformula detects use if '~~'", {
# checks fix of issue #749
expect_error(bf(y~~x), "~~")
})
test_that("brmsformula does not change a 'brmsformula' object", {
form <- bf(y ~ a, sigma ~ 1)
expect_identical(form, bf(form))
form <- bf(y ~ a, sigma ~ 1, a ~ x, nl = TRUE)
expect_identical(form, bf(form))
})
test_that("brmsformula detects auxiliary parameter equations", {
expect_error(bf(y~x, sigma1 = "sigmaa2"),
"Can only equate parameters of the same class")
expect_error(bf(y~x, mu3 = "mu2"),
"Equating parameters of class 'mu' is not allowed")
expect_error(bf(y~x, sigma1 = "sigma1"),
"Equating 'sigma1' with itself is not meaningful")
expect_error(bf(y~x, shape1 ~ x, shape2 = "shape1"),
"Cannot use predicted parameters on the right-hand side")
expect_error(bf(y~x, shape1 = "shape3", shape2 = "shape1"),
"Cannot use fixed parameters on the right-hand side")
})
test_that("update_adterms works correctly", {
form <- y | trials(size) ~ x
expect_equal(
update_adterms(form, ~ trials(10)),
y | trials(10) ~ x
)
expect_equal(
update_adterms(form, ~ weights(w)),
y | trials(size) + weights(w) ~ x
)
expect_equal(
update_adterms(form, ~ weights(w), action = "replace"),
y | weights(w) ~ x
)
expect_equal(
update_adterms(y ~ x, ~ trials(10)),
y | trials(10) ~ x
)
})
test_that("deprecated 'cbind' syntax still works", {
expect_equal(suppressWarnings(bf(cbind(y1, y2) ~ x)), bf(mvbind(y1, y2) ~ x))
})
brms/tests/testthat/tests.make_standata.R 0000644 0001762 0000144 00000114045 13616060272 020307 0 ustar ligges users context("Tests for make_standata")
test_that(paste("make_standata returns correct data names ",
"for fixed and random effects"), {
expect_equal(names(make_standata(rating ~ treat + period + carry
+ (1|subject), data = inhaler)),
c("N", "Y", "K", "X", "Z_1_1",
"J_1", "N_1", "M_1", "NC_1", "prior_only"))
expect_equal(names(make_standata(rating ~ treat + period + carry
+ (1+treat|id|subject), data = inhaler,
family = "categorical")),
c("N", "Y", "ncat", "K_mu2", "X_mu2", "Z_1_mu2_1",
"Z_1_mu2_2", "K_mu3", "X_mu3", "Z_1_mu3_3", "Z_1_mu3_4",
"K_mu4", "X_mu4", "Z_1_mu4_5", "Z_1_mu4_6",
"J_1", "N_1", "M_1", "NC_1", "prior_only"))
expect_equal(names(make_standata(rating ~ treat + period + carry
+ (1+treat|subject), data = inhaler)),
c("N", "Y", "K", "X", "Z_1_1", "Z_1_2", "J_1", "N_1", "M_1",
"NC_1", "prior_only"))
dat <- data.frame(y = 1:10, g = 1:10, h = 11:10, x = rep(0,10))
expect_equal(names(make_standata(y ~ x + (1|g) + (1|h), dat, "poisson")),
c("N", "Y", "K", "X", "Z_1_1", "Z_2_1",
"J_1", "J_2", "N_1", "M_1", "NC_1", "N_2", "M_2", "NC_2",
"prior_only"))
expect_true(all(c("Z_1_1", "Z_1_2", "Z_2_1", "Z_2_2") %in%
names(make_standata(y ~ x + (1+x|g/h), dat))))
expect_equal(make_standata(y ~ x + (1+x|g+h), dat),
make_standata(y ~ x + (1+x|g) + (1+x|h), dat))
})
test_that(paste("make_standata handles variables used as fixed effects",
"and grouping factors at the same time"), {
data <- data.frame(y = 1:9, x = factor(rep(c("a","b","c"), 3)))
standata <- make_standata(y ~ x + (1|x), data = data)
expect_equal(colnames(standata$X), c("Intercept", "xb", "xc"))
expect_equal(standata$J_1, as.array(rep(1:3, 3)))
standata2 <- make_standata(y ~ x + (1|x), data = data,
control = list(not4stan = TRUE))
expect_equal(colnames(standata2$X), c("Intercept", "xb", "xc"))
})
test_that("make_standata returns correct data names for addition terms", {
dat <- data.frame(y = 1:10, w = 1:10, t = 1:10, x = rep(0,10),
c = sample(-1:1,10,TRUE))
expect_equal(names(make_standata(y | se(w) ~ x, dat, gaussian())),
c("N", "Y", "se", "K", "X", "sigma", "prior_only"))
expect_equal(names(make_standata(y | weights(w) ~ x, dat, "gaussian")),
c("N", "Y", "weights", "K", "X", "prior_only"))
expect_equal(names(make_standata(y | cens(c) ~ x, dat, "student")),
c("N", "Y", "cens", "K", "X", "prior_only"))
expect_equal(names(make_standata(y | trials(t) ~ x, dat, "binomial")),
c("N", "Y", "trials", "K", "X", "prior_only"))
expect_equal(names(make_standata(y | trials(10) ~ x, dat, "binomial")),
c("N", "Y", "trials", "K", "X", "prior_only"))
expect_equal(names(make_standata(y | thres(11) ~ x, dat, "acat")),
c("N", "Y", "nthres", "K", "X", "disc", "prior_only"))
expect_equal(names(make_standata(y | thres(10) ~ x, dat, cumulative())),
c("N", "Y", "nthres", "K", "X", "disc", "prior_only"))
sdata <- make_standata(y | trunc(0,20) ~ x, dat, "gaussian")
expect_true(all(sdata$lb == 0) && all(sdata$ub == 20))
sdata <- make_standata(y | trunc(ub = 21:30) ~ x, dat)
expect_true(all(all(sdata$ub == 21:30)))
})
test_that(paste("make_standata accepts correct response variables",
"depending on the family"), {
expect_equal(make_standata(y ~ 1, data = data.frame(y = seq(-9.9,0,0.1)),
family = "student")$Y, as.array(seq(-9.9,0,0.1)))
expect_equal(make_standata(y | trials(10) ~ 1, data = data.frame(y = 1:10),
family = "binomial")$Y, as.array(1:10))
expect_equal(make_standata(y ~ 1, data = data.frame(y = 10:20),
family = "poisson")$Y, as.array(10:20))
expect_equal(make_standata(y ~ 1, data = data.frame(y = rep(-c(1:2),5)),
family = "bernoulli")$Y, as.array(rep(1:0,5)))
expect_equal(make_standata(y ~ 1, data = data.frame(y = rep(c(TRUE, FALSE),5)),
family = "bernoulli")$Y, as.array(rep(1:0,5)))
expect_equal(make_standata(y ~ 1, data = data.frame(y = rep(1:10,5)),
family = "categorical")$Y, as.array(rep(1:10,5)))
expect_equal(make_standata(y ~ 1, data = data.frame(y = rep(11:20,5)),
family = "categorical")$Y, as.array(rep(1:10,5)))
expect_equal(make_standata(y ~ 1, data = data.frame(y = factor(rep(11:20,5))),
family = "categorical")$Y, as.array(rep(1:10,5)))
expect_equal(make_standata(y ~ 1, data = data.frame(y = rep(1:10,5)),
family = "cumulative")$Y, as.array(rep(1:10,5)))
dat <- data.frame(y = factor(rep(-4:5,5), order = TRUE))
expect_equal(make_standata(y ~ 1, data = dat, family = "acat")$Y,
as.array(rep(1:10,5)))
expect_equal(make_standata(y ~ 1, data = data.frame(y = seq(1,10,0.1)),
family = "exponential")$Y, as.array(seq(1,10,0.1)))
dat <- data.frame(y1 = 1:10, y2 = 11:20, x = rep(0,10))
sdata <- make_standata(mvbind(y1, y2) ~ x, data = dat)
expect_equal(sdata$Y_y1, as.array(1:10))
expect_equal(sdata$Y_y2, as.array(11:20))
})
test_that(paste("make_standata rejects incorrect response variables",
"depending on the family"), {
expect_error(make_standata(y ~ 1, data = data.frame(y = factor(1:10)),
family = "student"),
"Family 'student' requires numeric responses")
expect_error(make_standata(y ~ 1, data = data.frame(y = -5:5),
family = "geometric"),
"Family 'geometric' requires response greater than or equal to 0")
expect_error(make_standata(y ~ 1, data = data.frame(y = -1:1),
family = "bernoulli"),
"contain only two different values")
expect_error(make_standata(y ~ 1, data = data.frame(y = factor(-1:1)),
family = "cratio"),
"Family 'cratio' requires either positive integers or ordered factors")
expect_error(make_standata(y ~ 1, data = data.frame(y = rep(0.5:7.5), 2),
family = "sratio"),
"Family 'sratio' requires either positive integers or ordered factors")
expect_error(make_standata(y ~ 1, data = data.frame(y = rep(-7.5:7.5), 2),
family = "gamma"),
"Family 'gamma' requires response greater than 0")
expect_error(make_standata(y ~ 1, data = data.frame(y = c(0.1, 0.5, 1)),
family = Beta()),
"Family 'beta' requires response smaller than 1")
expect_error(make_standata(y ~ 1, data = data.frame(y = c(0, 0.5, 4)),
family = von_mises()),
"Family 'von_mises' requires response smaller than or equal to 3.14")
expect_error(make_standata(y ~ 1, data = data.frame(y = c(-1, 2, 5)),
family = hurdle_gamma()),
"Family 'hurdle_gamma' requires response greater than or equal to 0")
})
test_that("make_standata suggests using family bernoulli if appropriate", {
expect_message(make_standata(y | trials(1) ~ 1, data = list(y = rep(0:1,5)),
family = "binomial"),
"family 'bernoulli' might be a more efficient choice.")
expect_message(make_standata(y ~ 1, data = data.frame(y = rep(1:2, 5)),
family = "acat"),
"family 'bernoulli' might be a more efficient choice.")
expect_message(make_standata(y ~ 1, data = data.frame(y = rep(0:1,5)),
family = "categorical"),
"family 'bernoulli' might be a more efficient choice.")
})
test_that("make_standata returns correct values for addition terms", {
dat <- data.frame(y = rnorm(9), s = 1:9, w = 1:9, c1 = rep(-1:1, 3),
c2 = rep(c("left","none","right"), 3),
c3 = c(rep(c(TRUE, FALSE), 4), FALSE),
c4 = c(sample(-1:1, 5, TRUE), rep(2, 4)),
t = 11:19)
expect_equivalent(make_standata(y | se(s) ~ 1, data = dat)$se,
as.array(1:9))
expect_equal(make_standata(y | weights(w) ~ 1, data = dat)$weights,
as.array(1:9))
expect_equal(make_standata(y | cens(c1) ~ 1, data = dat)$cens,
as.array(rep(-1:1, 3)))
expect_equal(make_standata(y | cens(c2) ~ 1, data = dat)$cens,
as.array(rep(-1:1, 3)))
expect_equal(make_standata(y | cens(c3) ~ 1, data = dat)$cens,
as.array(c(rep(1:0, 4), 0)))
expect_equal(make_standata(y | cens(c4, y + 2) ~ 1, data = dat)$rcens,
as.array(c(rep(0, 5), dat$y[6:9] + 2)))
sdata <- suppressWarnings(make_standata(s ~ 1, dat, family = "binomial"))
expect_equal(sdata$trials, as.array(rep(9, 9)))
expect_equal(make_standata(s | trials(10) ~ 1, dat,
family = "binomial")$trials,
as.array(rep(10, 9)))
expect_equal(make_standata(s | trials(t) ~ 1, data = dat,
family = "binomial")$trials,
as.array(11:19))
expect_equal(SW(make_standata(s | cat(19) ~ 1, data = dat,
family = "cumulative"))$nthres,
18)
})
test_that("make_standata rejects incorrect addition terms", {
dat <- data.frame(y = rnorm(9), s = -(1:9), w = -(1:9),
c = rep(-2:0, 3), t = 9:1, z = 1:9)
expect_error(make_standata(y | se(s) ~ 1, data = dat),
"Standard errors must be non-negative")
expect_error(make_standata(y | weights(w) ~ 1, data = dat),
"Weights must be non-negative")
expect_error(make_standata(y | cens(c) ~ 1, data = dat))
expect_error(make_standata(z | trials(t) ~ 1, data = dat,
family = "binomial"),
"Number of trials is smaller than the number of events")
})
test_that("make_standata handles multivariate models", {
dat <- data.frame(
y1 = 1:10, y2 = 11:20,
x = rep(0, 10), g = rep(1:2, 5),
censi = sample(0:1, 10, TRUE),
tim = 10:1, w = 1:10
)
sdata <- make_standata(mvbind(y1, y2) | weights(w) ~ x, data = dat)
expect_equal(sdata$Y_y1, as.array(dat$y1))
expect_equal(sdata$Y_y2, as.array(dat$y2))
expect_equal(sdata$weights_y1, as.array(1:10))
expect_error(make_standata(mvbind(y1, y2, y2) ~ x, data = dat),
"Cannot use the same response variable twice")
sdata <- make_standata(mvbind(y1 / y2, y2, y1 * 3) ~ x, data = dat)
expect_equal(sdata$Y_y1y2, as.array(dat$y1 / dat$y2))
sdata <- suppressWarnings(
make_standata(mvbind(y1, y2) ~ x, dat, autocor = cor_ar(~ tim | g))
)
target1 <- c(seq(9, 1, -2), seq(10, 2, -2))
expect_equal(sdata$Y_y1, as.array(target1))
target2 <- c(seq(19, 11, -2), seq(20, 12, -2))
expect_equal(sdata$Y_y2, as.array(target2))
# models without residual correlations
expect_warning(
bform <- bf(y1 | cens(censi) ~ x + y2 + (x|2|g)) +
gaussian() + cor_ar() +
(bf(x ~ 1) + mixture(poisson, nmix = 2)) +
(bf(y2 ~ s(y2) + (1|2|g)) + skew_normal()),
"Using 'cor_brms' objects for 'autocor' is deprecated"
)
bprior <- prior(normal(0, 5), resp = y1) +
prior(normal(0, 10), resp = y2) +
prior(dirichlet(2, 1), theta, resp = x)
sdata <- make_standata(bform, dat, prior = bprior)
sdata_names <- c(
"N", "J_1_y1", "cens_y1", "Kma_y1", "Z_1_y2_3",
"Zs_y2_1_1", "Y_y2", "con_theta_x", "X_mu2_x"
)
expect_true(all(sdata_names %in% names(sdata)))
expect_equal(sdata$con_theta_x, c(2, 1))
# test addition argument 'subset'
bform <- bf(y1 | subset(censi) ~ x + y2 + (x|2|g)) +
(bf(y2 ~ s(y2) + (1|2|g)) + skew_normal())
sdata <- make_standata(bform, dat)
nsub <- sum(dat$censi)
expect_equal(sdata$N_y1, nsub)
expect_equal(sdata$N_y2, nrow(dat))
expect_equal(length(sdata$Y_y1), nsub)
expect_equal(nrow(sdata$X_y1), nsub)
expect_equal(length(sdata$Z_1_y1_2), nsub)
})
test_that("make_standata returns correct data for ARMA terms", {
dat <- data.frame(y = 1:10, x = rep(0, 10), tim = 10:1, g = rep(3:4, 5))
sdata <- make_standata(y ~ x + ma(tim, g), data = dat)
expect_equal(sdata$J_lag, as.array(c(1, 1, 1, 1, 0, 1, 1, 1, 1, 0)))
sdata <- make_standata(y ~ x + ar(tim, g, p = 2), data = dat)
expect_equal(sdata$J_lag, as.array(c(1, 2, 2, 2, 0, 1, 2, 2, 2, 0)))
sdata <- make_standata(y ~ x + ar(tim, g, cov = TRUE), data = dat)
expect_equal(sdata$begin_tg, as.array(c(1, 6)))
expect_equal(sdata$nobs_tg, as.array(c(5, 5)))
bform <- bf(y ~ exp(b * x), b ~ 1, nl = TRUE, autocor = ~arma())
sdata <- make_standata(bform, dat)
})
test_that("make_standata allows to retrieve the initial data order", {
dat <- data.frame(y1 = rnorm(100), y2 = rnorm(100),
id = sample(1:10, 100, TRUE),
time = sample(1:100, 100))
# univariate model
sdata1 <- make_standata(y1 ~ ar(time, id), data = dat,
control = list(save_order = TRUE))
expect_equal(dat$y1, as.numeric(sdata1$Y[attr(sdata1, "old_order")]))
# multivariate model
sdata2 <- make_standata(mvbind(y1, y2) ~ ma(time, id), data = dat,
control = list(save_order = TRUE))
expect_equal(sdata2$Y_y1[attr(sdata2, "old_order")], as.array(dat$y1))
expect_equal(sdata2$Y_y2[attr(sdata2, "old_order")], as.array(dat$y2))
})
test_that("make_standata handles covariance matrices correctly", {
A <- structure(diag(1, 4), dimnames = list(1:4, NULL))
expect_equivalent(make_standata(count ~ Trt + (1|visit), data = epilepsy,
cov_ranef = list(visit = A))$Lcov_1, A)
B <- diag(1, 4)
expect_error(make_standata(count ~ Trt + (1|visit), data = epilepsy,
cov_ranef = list(visit = B)),
"Row names are required")
B <- structure(diag(1, 4), dimnames = list(2:5, NULL))
expect_error(make_standata(count ~ Trt + (1|visit), data = epilepsy,
cov_ranef = list(visit = B)),
"Row names .* do not match")
B <- structure(diag(1:5), dimnames = list(c(1,5,2,4,3), NULL))
expect_equivalent(make_standata(count ~ Trt + (1|visit), data = epilepsy,
cov_ranef = list(visit = B))$Lcov_1,
t(chol(B[c(1,3,5,4), c(1,3,5,4)])))
B <- A
B[1,2] <- 0.5
expect_error(make_standata(count ~ Trt + (1|visit), data = epilepsy,
cov_ranef = list(visit = B)),
"not symmetric")
})
test_that("make_standata correctly prepares data for non-linear models", {
flist <- list(a ~ x + (1|1|g), b ~ mo(z) + (1|1|g))
dat <- data.frame(
y = rnorm(9), x = rnorm(9), z = sample(1:9, 9), g = rep(1:3, 3)
)
bform <- bf(y ~ a - b^z, flist = flist, nl = TRUE)
sdata <- make_standata(bform, data = dat)
expect_equal(names(sdata),
c("N", "Y", "C_1", "K_a", "X_a", "Z_1_a_1",
"K_b", "X_b", "Ksp_b", "Imo_b", "Xmo_b_1", "Jmo_b",
"con_simo_b_1", "Z_1_b_2", "J_1", "N_1",
"M_1", "NC_1", "prior_only")
)
expect_equal(colnames(sdata$X_a), c("Intercept", "x"))
expect_equal(sdata$J_1, as.array(dat$g))
bform <- bf(y ~ x) +
nlf(sigma ~ a1 * exp(-x/(a2 + z))) +
lf(a1 ~ 1, a2 ~ z + (x|g)) +
lf(alpha ~ x)
sdata <- make_standata(bform, dat, family = skew_normal())
sdata_names <- c("C_sigma_1", "X_a2", "Z_1_a2_1")
expect_true(all(sdata_names %in% names(sdata)))
})
test_that("make_standata correctly prepares data for monotonic effects", {
data <- data.frame(
y = rpois(120, 10), x1 = rep(1:4, 30), z = rnorm(10),
x2 = factor(rep(c("a", "b", "c"), 40), ordered = TRUE)
)
sdata <- make_standata(y ~ mo(x1)*mo(x2)*y, data = data)
sdata_names <- c("Xmo_1", "Imo", "Jmo", "con_simo_8", "con_simo_5")
expect_true(all(sdata_names %in% names(sdata)))
expect_equivalent(sdata$Xmo_1, as.array(data$x1 - 1))
expect_equivalent(sdata$Xmo_2, as.array(as.numeric(data$x2) - 1))
expect_equal(
as.vector(unname(sdata$Jmo)),
rep(c(max(data$x1) - 1, length(unique(data$x2)) - 1), 4)
)
expect_equal(sdata$con_simo_1, as.array(rep(1, 3)))
prior <- set_prior("dirichlet(1:3)", coef = "mox11",
class = "simo", dpar = "sigma")
sdata <- make_standata(bf(y ~ 1, sigma ~ mo(x1)),
data = data, prior = prior)
expect_equal(sdata$con_simo_sigma_1, as.array(1:3))
prior <- c(
set_prior("normal(0,1)", class = "b", coef = "mox1"),
set_prior("dirichlet(c(1, 0.5, 2))", class = "simo", coef = "mox11"),
prior_(~dirichlet(c(1, 0.5, 2)), class = "simo", coef = "mox1:mox21")
)
sdata <- make_standata(y ~ mo(x1)*mo(x2), data = data, prior = prior)
expect_equal(sdata$con_simo_1, as.array(c(1, 0.5, 2)))
expect_equal(sdata$con_simo_3, as.array(c(1, 0.5, 2)))
expect_error(
make_standata(y ~ mo(z), data = data),
"Monotonic predictors must be integers or ordered factors"
)
prior <- c(set_prior("dirichlet(c(1,0.5,2))", class = "simo", coef = "mox21"))
expect_error(
make_standata(y ~ mo(x2), data = data, prior = prior),
"Invalid Dirichlet prior for the simplex of coefficient 'mox21'",
fixed = TRUE
)
})
test_that("make_standata returns FCOR covariance matrices", {
data <- data.frame(y = 1:5)
data2 <- list(V = diag(5))
expect_equal(make_standata(y ~ fcor(V), data, data2 = data2)$Mfcor,
data2$V, check.attributes = FALSE)
expect_warning(
expect_error(
make_standata(y~1, data, autocor = cor_fixed(diag(2))),
"Dimensions of 'M' for FCOR terms must be equal"
),
"Using 'cor_brms' objects for 'autocor' is deprecated"
)
})
test_that("make_standata returns data for GAMMs", {
dat <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10),
x3 = rnorm(10), z = rnorm(10), g = factor(rep(1:2, 5)))
sdata <- make_standata(y ~ s(x1) + z + s(x2, by = x3), data = dat)
expect_equal(sdata$nb_1, 1)
expect_equal(as.vector(sdata$knots_2), 8)
expect_equal(dim(sdata$Zs_1_1), c(10, 8))
expect_equal(dim(sdata$Zs_2_1), c(10, 8))
bform <- bf(y ~ lp, lp ~ s(x1) + z + s(x2, by = x3), nl = TRUE)
sdata <- make_standata(bform, dat)
expect_equal(sdata$nb_lp_1, 1)
expect_equal(as.vector(sdata$knots_lp_2), 8)
expect_equal(dim(sdata$Zs_lp_1_1), c(10, 8))
expect_equal(dim(sdata$Zs_lp_2_1), c(10, 8))
sdata <- make_standata(y ~ g + s(x2, by = g), data = dat)
expect_true(all(c("knots_1", "knots_2") %in% names(sdata)))
# test issue #562
dat$g <- as.character(dat$g)
sdata <- make_standata(y ~ g + s(x2, by = g), data = dat)
expect_true(all(c("knots_1", "knots_2") %in% names(sdata)))
sdata <- make_standata(y ~ t2(x1, x2), data = dat)
expect_equal(sdata$nb_1, 3)
expect_equal(as.vector(sdata$knots_1), c(9, 6, 6))
expect_equal(dim(sdata$Zs_1_1), c(10, 9))
expect_equal(dim(sdata$Zs_1_3), c(10, 6))
expect_error(make_standata(y ~ te(x1, x2), data = dat),
"smooths 'te' and 'ti' are not yet implemented")
})
test_that("make_standata returns correct group ID data", {
form <- bf(count ~ Trt + (1+Trt|3|visit) + (1|patient),
shape ~ (1|3|visit) + (Trt||patient))
sdata <- make_standata(form, data = epilepsy, family = negbinomial())
expect_true(all(c("Z_1_1", "Z_2_2", "Z_3_shape_1", "Z_2_shape_3") %in%
names(sdata)))
form <- bf(count ~ a, sigma ~ (1|3|visit) + (Trt||patient),
a ~ Trt + (1+Trt|3|visit) + (1|patient), nl = TRUE)
sdata <- make_standata(form, data = epilepsy, family = student())
expect_true(all(c("Z_1_sigma_1", "Z_2_a_3", "Z_2_sigma_1",
"Z_3_a_1") %in% names(sdata)))
})
test_that("make_standata handles population-level intercepts", {
dat <- data.frame(y = 10:1, x = 1:10)
sdata <- make_standata(y ~ 0 + x, data = dat)
expect_equal(unname(sdata$X[, 1]), dat$x)
sdata <- make_standata(y ~ x, dat, cumulative(),
control = list(not4stan = TRUE))
expect_equal(unname(sdata$X[, 1]), dat$x)
sdata <- make_standata(y ~ 0 + Intercept + x, data = dat)
expect_equal(unname(sdata$X), cbind(1, dat$x))
})
test_that("make_standata handles category specific effects", {
sdata <- make_standata(rating ~ period + carry + cse(treat),
data = inhaler, family = sratio())
expect_equivalent(sdata$Xcs, matrix(inhaler$treat))
sdata <- make_standata(rating ~ period + carry + cse(treat) + (cse(1)|subject),
data = inhaler, family = acat())
expect_equivalent(sdata$Z_1_3, as.array(rep(1, nrow(inhaler))))
sdata <- make_standata(rating ~ period + carry + (cse(treat)|subject),
data = inhaler, family = cratio())
expect_equivalent(sdata$Z_1_4, as.array(inhaler$treat))
expect_error(make_standata(rating ~ 1 + cse(treat), data = inhaler,
family = "cumulative"), "require families")
expect_error(make_standata(rating ~ 1 + (treat + cse(1)|subject),
data = inhaler, family = "cratio"),
"category specific effects in separate group-level terms")
})
test_that("make_standata handles wiener diffusion models", {
dat <- data.frame(q = 1:10, resp = sample(0:1, 10, TRUE), x = rnorm(10))
dat$dec <- ifelse(dat$resp == 0, "lower", "upper")
dat$test <- "a"
sdata <- make_standata(q | dec(resp) ~ x, data = dat, family = wiener())
expect_equal(sdata$dec, as.array(dat$resp))
sdata <- make_standata(q | dec(dec) ~ x, data = dat, family = wiener())
expect_equal(sdata$dec, as.array(dat$resp))
expect_error(make_standata(q | dec(test) ~ x, data = dat, family = wiener()),
"Decisions should be 'lower' or 'upper'")
})
test_that("make_standata handles noise-free terms", {
N <- 30
dat <- data.frame(
y = rnorm(N), x = rnorm(N), z = rnorm(N),
xsd = abs(rnorm(N, 1)), zsd = abs(rnorm(N, 1)),
ID = rep(1:5, each = N / 5)
)
sdata <- make_standata(
bf(y ~ me(x, xsd)*me(z, zsd)*x, sigma ~ me(x, xsd)),
data = dat
)
expect_equal(sdata$Xn_1, as.array(dat$x))
expect_equal(sdata$noise_2, as.array(dat$zsd))
expect_equal(unname(sdata$Csp_3), as.array(dat$x))
expect_equal(sdata$Ksp, 6)
expect_equal(sdata$NCme_1, 1)
})
test_that("make_standata handles noise-free terms with grouping factors", {
dat <- data.frame(
y = rnorm(10),
x1 = rep(1:5, each = 2),
sdx = rep(1:5, each = 2),
g = rep(c("b", "c", "a", "d", 1), each = 2)
)
sdata <- make_standata(y ~ me(x1, sdx, gr = g), dat)
expect_equal(unname(sdata$Xn_1), as.array(c(5, 3, 1, 2, 4)))
expect_equal(unname(sdata$noise_1), as.array(c(5, 3, 1, 2, 4)))
dat$sdx[2] <- 10
expect_error(
make_standata(y ~ me(x1, sdx, gr = g), dat),
"Measured values and measurement error should be unique"
)
})
test_that("make_standata handles missing value terms", {
dat = data.frame(y = rnorm(10), x = rnorm(10), g = 1:10)
miss <- c(1, 3, 9)
dat$x[miss] <- NA
bform <- bf(y ~ mi(x)*g) + bf(x | mi() ~ g) + set_rescor(FALSE)
sdata <- make_standata(bform, dat)
expect_equal(sdata$Jmi_x, as.array(miss))
expect_true(all(is.infinite(sdata$Y_x[miss])))
# dots in variable names are correctly handled #452
dat$x.2 <- dat$x
bform <- bf(y ~ mi(x.2)*g) + bf(x.2 | mi() ~ g) + set_rescor(FALSE)
sdata <- make_standata(bform, dat)
expect_equal(sdata$Jmi_x, as.array(miss))
dat$z <- rbeta(10, 1, 1)
dat$z[miss] <- NA
bform <- bf(exp(y) ~ mi(z)*g) + bf(z | mi() ~ g, family = Beta()) +
set_rescor(FALSE)
sdata <- make_standata(bform, dat)
expect_equal(sdata$Jmi_z, as.array(miss))
})
test_that("make_standata handles overimputation", {
dat = data.frame(y = rnorm(10), x = rnorm(10), g = 1:10, sdy = 1)
miss <- c(1, 3, 9)
dat$x[miss] <- dat$sdy[miss] <- NA
bform <- bf(y ~ mi(x)*g) + bf(x | mi(sdy) ~ g) + set_rescor(FALSE)
sdata <- make_standata(bform, dat)
expect_equal(sdata$Jme_x, as.array(setdiff(1:10, miss)))
expect_true(all(is.infinite(sdata$Y_x[miss])))
expect_true(all(is.infinite(sdata$noise_x[miss])))
})
test_that("make_standata handles multi-membership models", {
dat <- data.frame(y = rnorm(10), g1 = c(7:2, rep(10, 4)),
g2 = 1:10, w1 = rep(1, 10),
w2 = rep(abs(rnorm(10))))
sdata <- make_standata(y ~ (1|mm(g1,g2,g1,g2)), data = dat)
expect_true(all(paste0(c("W_1_", "J_1_"), 1:4) %in% names(sdata)))
expect_equal(sdata$W_1_4, as.array(rep(0.25, 10)))
expect_equal(unname(sdata$Z_1_1_1), as.array(rep(1, 10)))
expect_equal(unname(sdata$Z_1_1_2), as.array(rep(1, 10)))
# this checks whether combintation of factor levels works as intended
expect_equal(sdata$J_1_1, as.array(c(6, 5, 4, 3, 2, 1, 7, 7, 7, 7)))
expect_equal(sdata$J_1_2, as.array(c(8, 1, 2, 3, 4, 5, 6, 9, 10, 7)))
sdata <- make_standata(y ~ (1|mm(g1,g2, weights = cbind(w1, w2))), dat)
expect_equal(sdata$W_1_1, as.array(dat$w1 / (dat$w1 + dat$w2)))
# tests mmc terms
sdata <- make_standata(y ~ (1+mmc(w1, w2)|mm(g1,g2)), data = dat)
expect_equal(unname(sdata$Z_1_2_1), as.array(dat$w1))
expect_equal(unname(sdata$Z_1_2_2), as.array(dat$w2))
expect_error(
make_standata(y ~ (mmc(w1, w2, y)|mm(g1,g2)), data = dat),
"Invalid term 'mmc(w1, w2, y)':", fixed = TRUE
)
expect_error(
make_standata(y ~ (mmc(w1, w2)*y|mm(g1,g2)), data = dat),
"The term 'mmc(w1,w2):y' is invalid", fixed = TRUE
)
# tests if ":" works in multi-membership models
sdata <- make_standata(y ~ (1|mm(w1:g1,w1:g2)), dat)
expect_true(all(c("J_1_1", "J_1_2") %in% names(sdata)))
})
test_that("by variables in grouping terms are handled correctly", {
gvar <- c("1A", "1B", "2A", "2B", "3A", "3B", "10", "100", "2", "3")
dat <- data.frame(
y = rnorm(100), x = rnorm(100),
g = rep(gvar, each = 10),
z = factor(rep(c(0, 4.5, 3, 2, "x 1"), each = 20)),
z2 = factor(1:2)
)
sdata <- make_standata(y ~ x + (x | gr(g, by = z)), dat)
expect_equal(sdata$Nby_1, 5)
expect_equal(sdata$Jby_1, as.array(c(2, 2, 1, 1, 5, 4, 4, 5, 3, 3)))
expect_error(make_standata(y ~ x + (1|gr(g, by = z2)), dat),
"Some levels of 'g' correspond to multiple levels of 'z2'")
})
test_that("make_standata handles calls to the 'poly' function", {
dat <- data.frame(y = rnorm(10), x = rnorm(10))
expect_equal(colnames(make_standata(y ~ 1 + poly(x, 3), dat)$X),
c("Intercept", "polyx31", "polyx32", "polyx33"))
})
test_that("make_standata allows fixed distributional parameters", {
dat <- list(y = 1:10)
expect_equal(make_standata(bf(y ~ 1, nu = 3), dat, student())$nu, 3)
expect_equal(make_standata(y ~ 1, dat, acat())$disc, 1)
expect_error(make_standata(bf(y ~ 1, bias = 0.5), dat),
"Invalid fixed parameters: 'bias'")
})
test_that("Cell-mean coding can be disabled", {
df <- data.frame(y = 1:10, g = rep(c("a", "b"), 5))
bform <- bf(y ~ g) +
lf(disc ~ 0 + g + (0 + g | y), cmc = FALSE) +
cumulative()
sdata <- make_standata(bform, df)
target <- matrix(rep(0:1, 5), dimnames = list(1:10, "gb"))
expect_equal(sdata$X_disc, target)
expect_equal(unname(sdata$Z_1_disc_1), as.array(rep(0:1, 5)))
expect_true(!"Z_1_disc_2" %in% names(sdata))
bform <- bf(y ~ 0 + g + (1 | y), cmc = FALSE)
sdata <- make_standata(bform, df)
expect_equal(sdata$X, target)
expect_equal(unname(sdata$Z_1_1), as.array(rep(1, 10)))
})
test_that("make_standata correctly includes offsets", {
data <- data.frame(y = rnorm(10), x = rnorm(10), c = 1)
sdata <- make_standata(bf(y ~ x + offset(c), sigma ~ offset(c + 1)), data)
expect_equal(sdata$offsets, as.array(data$c))
expect_equal(sdata$offsets_sigma, as.array(data$c + 1))
sdata <- make_standata(y ~ x + offset(c) + offset(x), data)
expect_equal(sdata$offsets, as.array(data$c + data$x))
})
test_that("make_standata includes data for mixture models", {
data <- data.frame(y = rnorm(10), x = rnorm(10), c = 1)
form <- bf(y ~ x, mu1 ~ 1, family = mixture(gaussian, gaussian))
sdata <- make_standata(form, data)
expect_equal(sdata$con_theta, c(1, 1))
expect_equal(dim(sdata$X_mu1), c(10, 1))
expect_equal(dim(sdata$X_mu2), c(10, 2))
form <- bf(y ~ x, family = mixture(gaussian, gaussian))
sdata <- make_standata(form, data, prior = prior(dirichlet(10, 2), theta))
expect_equal(sdata$con_theta, c(10, 2))
form <- bf(y ~ x, theta1 = 1, theta2 = 3, family = mixture(gaussian, gaussian))
sdata <- make_standata(form, data)
expect_equal(sdata$theta1, 1/4)
expect_equal(sdata$theta2, 3/4)
})
test_that("make_standata includes data for Gaussian processes", {
dat <- data.frame(y = rnorm(10), x1 = sample(1:10, 10),
z = factor(c(2, 2, 2, 3, 4, rep(5, 5))))
sdata <- make_standata(y ~ gp(x1), dat)
expect_equal(max(sdata$Xgp_1) - min(sdata$Xgp_1), 1)
sdata <- make_standata(y ~ gp(x1, scale = FALSE), dat)
expect_equal(max(sdata$Xgp_1) - min(sdata$Xgp_1), 9)
sdata <- make_standata(y ~ gp(x1, by = z, gr = TRUE), dat)
expect_equal(sdata$Igp_1_2, as.array(4))
expect_equal(sdata$Jgp_1_4, as.array(1:5))
expect_equal(sdata$Igp_1_4, as.array(6:10))
sdata <- make_standata(y ~ gp(x1, by = y, gr = TRUE), dat)
expect_equal(sdata$Cgp_1, as.array(dat$y))
})
test_that("make_standata includes data for approximate Gaussian processes", {
dat <- data.frame(y = rnorm(10), x1 = sample(1:10, 10),
z = factor(c(2, 2, 2, 3, 4, rep(5, 5))))
sdata <- make_standata(y ~ gp(x1, k = 5, c = 5/4), dat)
expect_equal(sdata$NBgp_1, 5)
expect_equal(dim(sdata$Xgp_1), c(10, 5))
expect_equal(dim(sdata$slambda_1), c(5, 1))
sdata <- make_standata(y ~ gp(x1, by = z, k = 5, c = 5/4), dat)
expect_equal(sdata$Igp_1_2, as.array(4))
expect_equal(sdata$Cgp_1_2, as.array(1))
expect_equal(sdata$Igp_1_4, as.array(6:10))
})
test_that("make_standata includes data for SAR models", {
dat <- data.frame(y = rnorm(10), x = rnorm(10))
W <- matrix(0, nrow = 10, ncol = 10)
dat2 <- list(W = W)
sdata <- make_standata(y ~ x + sar(W), data = dat, data2 = dat2)
expect_equal(dim(sdata$M), rep(nrow(W), 2))
dat2 <- list(W = matrix(0, 2, 2))
expect_error(
make_standata(y ~ x + sar(W), data = dat, data2 = dat2),
"Dimensions of 'M' for SAR terms must be equal"
)
})
test_that("make_standata includes data for CAR models", {
dat = data.frame(y = rnorm(10), x = rnorm(10), obs = 1:10)
edges <- cbind(1:10, 10:1)
W <- matrix(0, nrow = 10, ncol = 10)
for (i in seq_len(nrow(edges))) {
W[edges[i, 1], edges[i, 2]] <- 1
}
rownames(W) <- 1:nrow(W)
dat2 <- list(W = W)
sdata <- make_standata(y ~ x + car(W, gr = obs), dat, data2 = dat2)
expect_equal(sdata$Nloc, 10)
expect_equal(unname(sdata$Nneigh), rep(1, 10))
expect_equal(unname(sdata$edges1), as.array(10:6))
expect_equal(unname(sdata$edges2), as.array(1:5))
sdata_old <- SW(make_standata(y ~ x, dat, autocor = cor_car(W)))
expect_equal(sdata, sdata_old)
rownames(dat2$W) <- c("a", 2:9, "b")
dat$group <- rep(c("a", "b"), each = 5)
sdata <- make_standata(y ~ x + car(W, gr = group), dat, data2 = dat2)
expect_equal(sdata$Nloc, 2)
expect_equal(sdata$edges1, as.array(2))
expect_equal(sdata$edges2, as.array(1))
sdata <- make_standata(y ~ x + car(W, group, type = "bym2"),
data = dat, data2 = dat2)
expect_equal(length(sdata$car_scale), 1L)
# test error messages
rownames(dat2$W) <- c(1:9, "a")
expect_error(make_standata(y ~ car(W, gr = group), dat, data2 = dat2),
"Row names of 'M' for CAR terms do not match")
rownames(dat2$W) <- NULL
expect_error(make_standata(y ~ car(W, gr = group), dat, data2 = dat2),
"Row names are required for 'M'")
dat2$W[1, 10] <- 0
expect_error(make_standata(y ~ car(W), dat, data2 = dat2),
"'M' for CAR terms must be symmetric")
dat2$W[10, 1] <- 0
expect_error(SW(make_standata(y ~ x + car(W), dat, data2 = dat2)),
"all locations should have at least one neighbor")
})
test_that("make_standata includes data of special priors", {
dat <- data.frame(y = 1:10, x1 = rnorm(10), x2 = rnorm(10))
# horseshoe prior
hs <- horseshoe(7, scale_global = 2, df_global = 3,
df_slab = 6, scale_slab = 3)
sdata <- make_standata(y ~ x1*x2, data = dat,
prior = set_prior(hs))
expect_equal(sdata$hs_df, 7)
expect_equal(sdata$hs_df_global, 3)
expect_equal(sdata$hs_df_slab, 6)
expect_equal(sdata$hs_scale_global, 2)
expect_equal(sdata$hs_scale_slab, 3)
hs <- horseshoe(par_ratio = 0.1)
sdata <- make_standata(y ~ x1*x2, data = dat, prior = set_prior(hs))
expect_equal(sdata$hs_scale_global, 0.1 / sqrt(nrow(dat)))
# lasso prior
sdata <- make_standata(y ~ x1*x2, data = dat,
prior = prior(lasso(2, scale = 10)))
expect_equal(sdata$lasso_df, 2)
expect_equal(sdata$lasso_scale, 10)
# horseshoe and lasso prior applied in a non-linear model
hs_a1 <- horseshoe(7, scale_global = 2, df_global = 3)
lasso_a2 <- lasso(2, scale = 10)
sdata <- make_standata(
bf(y ~ a1 + a2, a1 ~ x1, a2 ~ 0 + x2, nl = TRUE),
data = dat, sample_prior = TRUE,
prior = c(set_prior(hs_a1, nlpar = "a1"),
set_prior(lasso_a2, nlpar = "a2"))
)
expect_equal(sdata$hs_df_a1, 7)
expect_equal(sdata$lasso_df_a2, 2)
})
test_that("dots in formula are correctly expanded", {
dat <- data.frame(y = 1:10, x1 = 1:10, x2 = 1:10)
sdata <- make_standata(y ~ ., dat)
expect_equal(colnames(sdata$X), c("Intercept", "x1", "x2"))
})
test_that("argument 'stanvars' is handled correctly", {
bprior <- prior(normal(mean_intercept, 10), class = "Intercept")
mean_intercept <- 5
stanvars <- stanvar(mean_intercept)
sdata <- make_standata(count ~ Trt, data = epilepsy,
prior = bprior, stanvars = stanvars)
expect_equal(sdata$mean_intercept, 5)
# define a multi_normal prior with known covariance matrix
bprior <- prior(multi_normal(M, V), class = "b")
stanvars <- stanvar(rep(0, 2), "M", scode = " vector[K] M;") +
stanvar(diag(2), "V", scode = " matrix[K, K] V;")
sdata <- make_standata(count ~ Trt + zBase, epilepsy,
prior = bprior, stanvars = stanvars)
expect_equal(sdata$M, rep(0, 2))
expect_equal(sdata$V, diag(2))
})
test_that("addition arguments 'vint' and 'vreal' work correctly", {
dat <- data.frame(size = 10, y = sample(0:10, 20, TRUE), x = rnorm(20))
beta_binomial2 <- custom_family(
"beta_binomial2",
dpars = c("mu", "tau"),
links = c("logit", "log"),
lb = c(NA, 0),
type = "int",
vars = c("vint1[n]", "vreal1[n]")
)
sdata <- make_standata(
y | vint(size) + vreal(x, size) ~ 1,
data = dat, family = beta_binomial2,
)
expect_equal(sdata$vint1, as.array(rep(10, 20)))
expect_equal(sdata$vreal1, as.array(dat$x))
expect_equal(sdata$vreal2, as.array(rep(10, 20)))
})
test_that("reserved variables 'Intercept' is handled correctly", {
dat <- data.frame(y = 1:10)
expect_warning(
sdata <- make_standata(y ~ 0 + intercept, dat),
"Reserved variable name 'intercept' is deprecated."
)
expect_true(all(sdata$X[, "intercept"] == 1))
sdata <- make_standata(y ~ 0 + Intercept, dat)
expect_true(all(sdata$X[, "Intercept"] == 1))
})
test_that("data for multinomial and dirichlet models is correct", {
N <- 15
dat <- as.data.frame(rdirichlet(N, c(3, 2, 1)))
names(dat) <- c("y1", "y2", "y3")
dat$t1 <- round(dat$y1 * rpois(N, 10))
dat$t2 <- round(dat$y2 * rpois(N, 10))
dat$t3 <- round(dat$y3 * rpois(N, 10))
dat$x <- rnorm(N)
dat$y <- with(dat, cbind(y1, y2, y3))
dat$t <- with(dat, cbind(t1, t2, t3))
dat$size <- rowSums(dat$t)
sdata <- make_standata(t | trials(size) ~ x, dat, multinomial())
expect_equal(sdata$trials, as.array(dat$size))
expect_equal(sdata$ncat, 3)
expect_equal(sdata$Y, unname(dat$t))
sdata <- make_standata(y ~ x, data = dat, family = dirichlet())
expect_equal(sdata$ncat, 3)
expect_equal(sdata$Y, unname(dat$y))
expect_error(
make_standata(t | trials(10) ~ x, data = dat, family = multinomial()),
"Number of trials does not match the number of events"
)
expect_error(make_standata(t ~ x, data = dat, family = dirichlet()),
"Response values in dirichlet models must sum to 1")
})
test_that("make_standata handles cox models correctly", {
data <- data.frame(y = rexp(100), x = rnorm(100))
bform <- bf(y ~ x)
sdata <- make_standata(bform, data, brmsfamily("cox"))
expect_equal(dim(sdata$Zbhaz), c(100, 4))
expect_equal(dim(sdata$Zcbhaz), c(100, 4))
sdata <- make_standata(bform, data, brmsfamily("cox", bhaz = list(df = 6)))
expect_equal(dim(sdata$Zbhaz), c(100, 6))
expect_equal(dim(sdata$Zcbhaz), c(100, 6))
})
test_that("make_standata handles addition term 'rate' is correctly", {
data <- data.frame(y = rpois(10, 1), x = rnorm(10), time = 1:10)
sdata <- make_standata(y | rate(time) ~ x, data, poisson())
expect_equal(sdata$denom, as.array(data$time))
})
test_that("make_standata handles grouped ordinal thresholds correctly", {
dat <- data.frame(
y = c(1:5, 1:4, 4),
gr = rep(c("a", "b"), each = 5),
th = rep(5:6, each = 5),
x = rnorm(10)
)
# thresholds without a grouping factor
sdata <- make_standata(y ~ x, dat, cumulative())
expect_equal(sdata$nthres, 4)
sdata <- make_standata(y | thres(5) ~ x, dat, cumulative())
expect_equal(sdata$nthres, 5)
expect_error(
make_standata(y | thres(th) ~ x, dat, cumulative()),
"Number of thresholds needs to be a single value"
)
# thresholds with a grouping factor
sdata <- make_standata(y | thres(th, gr) ~ x, dat, cumulative())
expect_equal(sdata$nthres, as.array(c(5, 6)))
expect_equal(sdata$ngrthres, 2)
expect_equal(unname(sdata$Jthres[1, ]), c(1, 5))
expect_equal(unname(sdata$Jthres[10, ]), c(6, 11))
sdata <- make_standata(y | thres(gr = gr) ~ x, dat, cumulative())
expect_equal(sdata$nthres, as.array(c(4, 3)))
expect_equal(sdata$ngrthres, 2)
sdata <- make_standata(y | thres(6, gr = gr) ~ x, dat, cumulative())
expect_equal(sdata$nthres, as.array(c(6, 6)))
expect_equal(sdata$ngrthres, 2)
})
brms/tests/testthat/tests.distributions.R 0000644 0001762 0000144 00000016251 13277540752 020426 0 ustar ligges users context("Tests for distribution functions")
test_that("student distribution works correctly", {
expect_equal(integrate(dstudent_t, -100, 100, df = 15, mu = 10, sigma = 5)$value, 1)
expect_equal(dstudent_t(1, df = 10, mu = 0, sigma = 5), dt(1/5, df = 10)/5)
expect_equal(pstudent_t(2, df = 20, mu = 2, sigma = 0.4), pt(0, df = 20))
expect_equal(qstudent_t(0.7, df = 5, mu = 2, sigma = 3), 2 + 3*qt(0.7, df = 5))
expect_equal(length(rstudent_t(10, df = 10, mu = rnorm(10), sigma = 1:10)), 10)
})
test_that("multivariate normal and student distributions work correctly", {
mu <- rnorm(3)
Sigma <- cov(matrix(rnorm(300), ncol = 3))
expect_equal(dmulti_normal(1:3, mu = mu, Sigma = Sigma),
mnormt::dmnorm(1:3, mu, Sigma))
expect_equal(dmulti_student_t(1:3, mu = mu, Sigma = Sigma, df = 10, log = TRUE),
mnormt::dmt(1:3, df = 10, mean = mu, S = Sigma, log = TRUE))
expect_equal(dim(rmulti_normal(7, mu = mu, Sigma = Sigma)), c(7, 3))
expect_equal(dim(rmulti_student_t(7, mu = mu, Sigma = Sigma, df = 10)),
c(7, 3))
# test errors
expect_error(dmulti_normal(1:3, mu = rnorm(2), Sigma = Sigma, check = TRUE),
"Dimension of mu is incorrect")
expect_error(dmulti_normal(1:3, mu = mu, Sigma = Sigma[1:2, 1:2],
check = TRUE),
"Dimension of Sigma is incorrect")
expect_error(dmulti_normal(1:3, mu = mu, Sigma = Sigma[1:3, 3:1],
check = TRUE),
"Sigma must be a symmetric matrix")
expect_error(rmulti_normal(1.5, mu = mu, Sigma = Sigma, check = TRUE),
"n must be a positive integer")
expect_error(rmulti_normal(10, mu = mu, Sigma = Sigma[1:3, 3:1],
check = TRUE),
"Sigma must be a symmetric matrix")
expect_error(dmulti_student_t(rnorm(3), mu = mu, Sigma = Sigma,
df = -1, check = TRUE),
"df must be greater than 0")
expect_error(dmulti_student_t(rnorm(3), mu = mu, Sigma = Sigma[1:3, 3:1],
df = 30, check = TRUE),
"Sigma must be a symmetric matrix")
expect_error(rmulti_student_t(10, mu = mu, Sigma = Sigma,
df = -1, check = TRUE),
"df must be greater than 0")
})
test_that("von_mises distribution functions run without errors", {
n <- 10
res <- dvon_mises(runif(n, -pi, pi), mu = 1, kappa = 1:n)
expect_true(length(res) == n)
res <- pvon_mises(runif(n, -pi, pi), mu = rnorm(n), kappa = 0:(n-1))
expect_true(length(res) == n)
res <- rvon_mises(n, mu = rnorm(n), kappa = 0:(n-1))
expect_true(length(res) == n)
})
test_that("skew_normal distribution functions run without errors", {
n <- 10
x <- rnorm(n, 10, 3)
res <- dskew_normal(x, mu = 1, sigma = 2, alpha = 1)
expect_true(length(res) == n)
res <- pskew_normal(x, mu = rnorm(n), sigma = 1:n,
alpha = 3, log.p = TRUE)
expect_true(length(res) == n)
res <- qskew_normal(x, mu = rnorm(n), sigma = 1:n,
alpha = 3, log.p = TRUE)
expect_true(length(res) == n)
res <- rskew_normal(n, mu = rnorm(n), sigma = 10, alpha = -4:5)
expect_true(length(res) == n)
})
test_that("exgaussian distribution functions run without errors", {
n <- 10
x <- rnorm(n, 10, 3)
res <- dexgaussian(x, mu = 1, sigma = 2, beta = 1)
expect_true(length(res) == n)
res <- pexgaussian(x, mu = rnorm(n), sigma = 1:n,
beta = 3, log.p = TRUE)
expect_true(length(res) == n)
res <- rexgaussian(n, mu = rnorm(n), sigma = 10, beta = 1:10)
expect_true(length(res) == n)
})
test_that("frechet distribution functions run without errors", {
n <- 10
x <- 21:30
res <- dfrechet(x, loc = 1, scale = 2, shape = 1, log = TRUE)
expect_true(length(res) == n)
loc <- 1:10
res <- pfrechet(x, loc = loc, scale = 1:n, shape = 3)
expect_true(length(res) == n)
q <- qfrechet(res, loc = loc, scale = 1:n, shape = 3)
expect_equal(x, q)
res <- rfrechet(n, loc = loc, scale = 10, shape = 1:10)
expect_true(length(res) == n)
})
test_that("inv_gaussian distribution functions run without errors", {
n <- 10
x <- rgamma(n, 10, 3)
res <- dinv_gaussian(x, mu = 1, shape = 1)
expect_true(length(res) == n)
res <- pinv_gaussian(x, mu = abs(rnorm(n)), shape = 3)
expect_true(length(res) == n)
res <- rinv_gaussian(n, mu = abs(rnorm(n)), shape = 1:10)
expect_true(length(res) == n)
})
test_that("gen_extreme_value distribution functions run without errors", {
n <- 10
x <- rgamma(n, 10, 3)
res <- dgen_extreme_value(x, mu = 1, sigma = 2, xi = 1)
expect_true(length(res) == n)
res <- pgen_extreme_value(x, mu = rnorm(n), sigma = 1:n, xi = 3)
expect_true(length(res) == n)
res <- rgen_extreme_value(n, mu = rnorm(n), sigma = 10, xi = 1:10)
expect_true(length(res) == n)
})
test_that("asym_laplace distribution functions run without errors", {
n <- 10
x <- rnorm(n, 10, 3)
res <- dasym_laplace(x, mu = 1, sigma = 2, quantile = 0.5)
expect_true(length(res) == n)
res <- pasym_laplace(x, mu = rnorm(n), sigma = 1:n, quantile = 0.3)
expect_true(length(res) == n)
res <- rasym_laplace(n, mu = rnorm(n), sigma = 10,
quantile = runif(n, 0, 1))
expect_true(length(res) == n)
})
test_that("zero-inflated distribution functions run without errors", {
n <- 10
x <- rpois(n, lambda = 1)
res <- dzero_inflated_poisson(x, lambda = 1, zi = 0.1)
expect_true(length(res) == n)
res <- pzero_inflated_poisson(x, lambda = 1, zi = 0.1)
expect_true(length(res) == n)
res <- dzero_inflated_negbinomial(x, mu = 2, shape = 5, zi = 0.1)
expect_true(length(res) == n)
res <- pzero_inflated_negbinomial(x, mu = 2, shape = 5, zi = 0.1)
expect_true(length(res) == n)
res <- dzero_inflated_binomial(x, size = c(2, 10), prob = 0.4, zi = 0.1)
expect_true(length(res) == n)
res <- pzero_inflated_binomial(x, size = c(2, 10), prob = 0.4, zi = 0.1)
expect_true(length(res) == n)
x <- c(rbeta(n - 2, shape1 = 2, shape2 = 3), 0, 0)
res <- dzero_inflated_beta(x, shape1 = 2, shape2 = 3, zi = 0.1)
expect_true(length(res) == n)
res <- pzero_inflated_beta(x, shape1 = 2, shape2 = 3, zi = 0.1)
expect_true(length(res) == n)
})
test_that("hurdle distribution functions run without errors", {
n <- 10
x <- rpois(n, lambda = 1)
res <- dhurdle_poisson(x, lambda = 1, hu = 0.1)
expect_true(length(res) == n)
res <- phurdle_poisson(x, lambda = 1, hu = 0.1)
expect_true(length(res) == n)
res <- dhurdle_negbinomial(x, mu = 2, shape = 5, hu = 0.1)
expect_true(length(res) == n)
res <- phurdle_negbinomial(x, mu = 2, shape = 5, hu = 0.1)
expect_true(length(res) == n)
res <- dhurdle_gamma(x, shape = 1, scale = 3, hu = 0.1)
expect_true(length(res) == n)
res <- phurdle_gamma(x, shape = 1, scale = 3, hu = 0.1)
expect_true(length(res) == n)
res <- dhurdle_lognormal(x, mu = 2, sigma = 5, hu = 0.1)
expect_true(length(res) == n)
res <- phurdle_lognormal(x, mu = 2, sigma = 5, hu = 0.1)
expect_true(length(res) == n)
})
brms/tests/testthat/tests.posterior_predict.R 0000644 0001762 0000144 00000032370 13611527526 021260 0 ustar ligges users context("Tests for posterior_predict helper functions")
test_that("posterior_predict for location shift models runs without errors", {
ns <- 30
nobs <- 10
draws <- structure(list(nsamples = ns), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rnorm(ns * nobs), ncol = nobs),
sigma = rchisq(ns, 3), nu = rgamma(ns, 4)
)
i <- sample(nobs, 1)
pred <- brms:::posterior_predict_gaussian(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_student(i, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for various skewed models runs without errors", {
ns <- 50
nobs <- 2
draws <- structure(list(nsamples = ns), class = "brmsdraws")
draws$dpars <- list(
sigma = rchisq(ns, 3), beta = rchisq(ns, 3),
mu = matrix(rnorm(ns * nobs), ncol = nobs),
alpha = rnorm(ns), ndt = 1
)
pred <- brms:::posterior_predict_lognormal(1, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_shifted_lognormal(1, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_exgaussian(1, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_skew_normal(1, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for aysm_laplace models runs without errors", {
ns <- 50
draws <- structure(list(nsamples = ns), class = "brmsdraws")
draws$dpars <- list(
sigma = rchisq(ns, 3),
quantile = rbeta(ns, 2, 1),
mu = matrix(rnorm(ns*2), ncol = 2),
zi = rbeta(ns, 10, 10)
)
pred <- brms:::posterior_predict_asym_laplace(1, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_zero_inflated_asym_laplace(1, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for multivariate linear models runs without errors", {
ns <- 10
nvars <- 3
ncols <- 4
nobs <- nvars * ncols
Sigma = array(cov(matrix(rnorm(300), ncol = 3)), dim = c(3, 3, 10))
draws <- structure(list(nsamples = ns), class = "mvbrmsdraws")
draws$mvpars <- list(
Mu = array(rnorm(ns*nobs*nvars), dim = c(ns, nobs, nvars)),
Sigma = aperm(Sigma, c(3, 1, 2))
)
draws$dpars <- list(nu = rgamma(ns, 5))
draws$data <- list(N = nobs, N_trait = ncols)
pred <- brms:::posterior_predict_gaussian_mv(1, draws = draws)
expect_equal(dim(pred), c(ns, nvars))
pred <- brms:::posterior_predict_student_mv(2, draws = draws)
expect_equal(dim(pred), c(ns, nvars))
})
test_that("posterior_predict for ARMA covariance models runs without errors", {
ns <- 20
nobs <- 15
draws <- structure(list(nsamples = ns), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rnorm(ns*nobs), ncol = nobs),
sigma = rchisq(ns, 3),
nu = rgamma(ns, 5)
)
draws$ac <- list(
ar = matrix(rbeta(ns, 0.5, 0.5), ncol = 1),
ma = matrix(rnorm(ns, 0.2, 1), ncol = 1),
begin_tg = c(1, 5, 12), end_tg = c(4, 11, 15)
)
draws$data <- list(se = rgamma(ns, 10))
draws$family$fun <- "gaussian_time"
pred <- brms:::posterior_predict_gaussian_time(1, draws = draws)
expect_equal(length(pred), ns * 4)
draws$family$fun <- "student_time"
pred <- brms:::posterior_predict_student_time(2, draws = draws)
expect_equal(length(pred), ns * 7)
})
test_that("loglik for SAR models runs without errors", {
ns = 3
draws <- structure(list(nsamples = ns, nobs = 10), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rnorm(30), nrow = ns),
nu = rep(2, ns),
sigma = rep(10, ns)
)
draws$ac <- list(lagsar = matrix(c(0.3, 0.5, 0.7)), Msar = diag(10))
pred <- brms:::posterior_predict_gaussian_lagsar(1, draws = draws)
expect_equal(dim(pred), c(3, 10))
pred <- brms:::posterior_predict_student_lagsar(1, draws = draws)
expect_equal(dim(pred), c(3, 10))
draws$ac$errorsar <- draws$ac$lagsar
draws$ac$lagsar <- NULL
pred <- brms:::posterior_predict_gaussian_errorsar(1, draws = draws)
expect_equal(dim(pred), c(3, 10))
pred <- brms:::posterior_predict_student_errorsar(1, draws = draws)
expect_equal(dim(pred), c(3, 10))
})
test_that("posterior_predict for FCOR models runs without errors", {
ns <- 3
nobs <- 10
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rnorm(nobs * ns), nrow = ns),
sigma = rep(1, ns), nu = rep(2, ns)
)
draws$ac <- list(Mfcor = diag(nobs))
pred <- brms:::posterior_predict_gaussian_fcor(1, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
pred <- brms:::posterior_predict_student_fcor(1, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
})
test_that("posterior_predict for count and survival models runs without errors", {
ns <- 25
nobs <- 10
trials <- sample(10:30, nobs, replace = TRUE)
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
eta = matrix(rnorm(ns*nobs), ncol = nobs),
shape = rgamma(ns, 4), xi = 0
)
draws$dpars$nu <- draws$dpars$sigma <- draws$dpars$shape + 1
draws$data <- list(trials = trials)
i <- sample(nobs, 1)
draws$dpars$mu <- brms:::inv_cloglog(draws$dpars$eta)
pred <- brms:::posterior_predict_binomial(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_discrete_weibull(i, draws = draws)
expect_equal(length(pred), ns)
draws$dpars$mu <- exp(draws$dpars$eta)
pred <- brms:::posterior_predict_poisson(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_negbinomial(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_geometric(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_com_poisson(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_exponential(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_gamma(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_frechet(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_inverse.gaussian(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_gen_extreme_value(i, draws = draws)
expect_equal(length(pred), ns)
draws$family$link <- "log"
pred <- brms:::posterior_predict_weibull(i, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for bernoulli and beta models works correctly", {
ns <- 17
nobs <- 10
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = brms:::inv_logit(matrix(rnorm(ns * nobs * 2), ncol = 2 * nobs)),
phi = rgamma(ns, 4)
)
i <- sample(1:nobs, 1)
pred <- brms:::posterior_predict_bernoulli(i, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_beta(i, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for circular models runs without errors", {
ns <- 15
nobs <- 10
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = 2 * atan(matrix(rnorm(ns * nobs * 2), ncol = nobs * 2)),
kappa = rgamma(ns, 4)
)
i <- sample(seq_len(nobs), 1)
pred <- brms:::posterior_predict_von_mises(i, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for zero-inflated and hurdle models runs without erros", {
ns <- 50
nobs <- 8
trials <- sample(10:30, nobs, replace = TRUE)
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
eta = matrix(rnorm(ns * nobs * 2), ncol = nobs * 2),
shape = rgamma(ns, 4), phi = rgamma(ns, 1),
zi = rbeta(ns, 1, 1), coi = rbeta(ns, 5, 7)
)
draws$dpars$hu <- draws$dpars$zoi <- draws$dpars$zi
draws$data <- list(trials = trials)
draws$dpars$mu <- exp(draws$dpars$eta)
pred <- brms:::posterior_predict_hurdle_poisson(1, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_hurdle_negbinomial(2, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_hurdle_gamma(5, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_zero_inflated_poisson(3, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_zero_inflated_negbinomial(6, draws = draws)
expect_equal(length(pred), ns)
draws$dpars$mu <- brms:::inv_logit(draws$dpars$eta)
pred <- brms:::posterior_predict_zero_inflated_binomial(4, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_zero_inflated_beta(8, draws = draws)
expect_equal(length(pred), ns)
pred <- brms:::posterior_predict_zero_one_inflated_beta(7, draws = draws)
expect_equal(length(pred), ns)
})
test_that("posterior_predict for ordinal models runs without erros", {
ns <- 50
nobs <- 8
nthres <- 3
ncat <- nthres + 1
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = array(rnorm(ns * nobs), dim = c(ns, nobs)),
disc = rexp(ns)
)
draws$thres$thres <- array(0, dim = c(ns, nthres))
draws$data <- list(Y = rep(1:ncat, 2), ncat = ncat)
draws$family$link <- "logit"
draws$family$family <- "cumulative"
pred <- sapply(1:nobs, brms:::posterior_predict_cumulative, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$family$family <- "sratio"
pred <- sapply(1:nobs, brms:::posterior_predict_sratio, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$family$family <- "cratio"
pred <- sapply(1:nobs, brms:::posterior_predict_cratio, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$family$family <- "acat"
pred <- sapply(1:nobs, brms:::posterior_predict_acat, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$family$link <- "probit"
pred <- sapply(1:nobs, brms:::posterior_predict_acat, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
})
test_that("posterior_predict for categorical and related models runs without erros", {
ns <- 50
nobs <- 8
ncat <- 3
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu1 = array(rnorm(ns*nobs, 0, 0.1), dim = c(ns, nobs)),
mu2 = array(rnorm(ns*nobs, 0, 0.1), dim = c(ns, nobs))
)
draws$data <- list(Y = rep(1:ncat, 2), ncat = ncat)
draws$family <- categorical()
pred <- sapply(1:nobs, brms:::posterior_predict_categorical, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$data$trials <- sample(1:20, nobs)
draws$family <- multinomial()
pred <- brms:::posterior_predict_multinomial(i = sample(1:nobs, 1), draws = draws)
expect_equal(dim(pred), c(ns, ncat))
draws$dpars$phi <- rexp(ns, 1)
draws$family <- dirichlet()
pred <- brms:::posterior_predict_dirichlet(i = sample(1:nobs, 1), draws = draws)
expect_equal(dim(pred), c(ns, ncat))
expect_equal(rowSums(pred), rep(1, nrow(pred)))
})
test_that("truncated posterior_predict run without errors", {
ns <- 30
nobs <- 15
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rnorm(ns * nobs), ncol = nobs),
sigma = rchisq(ns, 3)
)
draws$data <- list(lb = sample(-(4:7), nobs, TRUE))
pred <- sapply(1:nobs, brms:::posterior_predict_gaussian, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$dpars$mu <- exp(draws$dpars$mu)
draws$data <- list(ub = sample(70:80, nobs, TRUE))
pred <- sapply(1:nobs, brms:::posterior_predict_poisson, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
draws$data <- list(lb = rep(0, nobs), ub = sample(70:75, nobs, TRUE))
pred <- sapply(1:nobs, brms:::posterior_predict_poisson, draws = draws)
expect_equal(dim(pred), c(ns, nobs))
})
test_that("posterior_predict for the wiener diffusion model runs without errors", {
skip("skip as long as RWiener fails on R-devel for 3.6.0")
ns <- 5
nobs <- 3
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rnorm(ns * nobs), ncol = nobs),
bs = rchisq(ns, 3), ndt = rep(0.5, ns),
bias = rbeta(ns, 1, 1)
)
draws$data <- list(Y = abs(rnorm(ns)) + 0.5, dec = c(1, 0, 1))
i <- sample(1:nobs, 1)
expect_equal(nrow(brms:::posterior_predict_wiener(i, draws)), ns)
})
test_that("posterior_predict_custom runs without errors", {
ns <- 15
nobs <- 10
draws <- structure(list(nsamples = ns, nobs = nobs), class = "brmsdraws")
draws$dpars <- list(
mu = matrix(rbeta(ns * nobs * 2, 1, 1), ncol = nobs * 2)
)
draws$data <- list(trials = rep(1, nobs))
draws$family <- custom_family(
"beta_binomial2", dpars = c("mu", "tau"),
links = c("logit", "log"), lb = c(NA, 0),
type = "int", vars = "trials[n]"
)
posterior_predict_beta_binomial2 <- function(i, draws) {
mu <- draws$dpars$mu[, i]
rbinom(draws$nsamples, size = draws$data$trials[i], prob = mu)
}
expect_equal(length(brms:::posterior_predict_custom(sample(1:nobs, 1), draws)), ns)
})
brms/tests/testthat/tests.brm.R 0000644 0001762 0000144 00000006017 13611527526 016277 0 ustar ligges users # calling context() avoids a strange bug in testthat 2.0.0
# cannot actually run brms models in tests as it takes way too long
context("Tests for brms error messages")
test_that("brm produces expected errors", {
dat <- data.frame(y = rnorm(10), x = rnorm(10), g = rep(1:5, 2))
# formula parsing
expect_error(brm(~ x + (1|g), dat, file = "test"),
"Response variable is missing")
expect_error(brm(bf(y ~ a, nl = TRUE)),
"No non-linear parameters specified")
expect_error(brm(bf(y | se(sei) ~ x, sigma ~ x), dat),
"Cannot predict or fix 'sigma' in this model")
expect_error(brm(y | se(sei) ~ x, dat, family = weibull()),
"Argument 'se' is not supported for family")
expect_error(brm(y | se(sei) + se(sei2) ~ x, dat, family = gaussian()),
"Each addition argument may only be defined once")
expect_error(brm(y | abc(sei) ~ x, family = gaussian()),
"The following addition terms are invalid:\n'abc(sei)'",
fixed = TRUE)
expect_error(brm(y | disp(sei) ~ x, dat, family = gaussian()),
"The following addition terms are invalid:")
expect_error(brm(bf(y ~ x, shape ~ x), family = gaussian()),
"The parameter 'shape' is not a valid distributional")
expect_error(brm(y ~ x + (1|abc|g/x), dat),
"Can only combine group-level terms")
expect_error(brm(y ~ x + (1|g) + (x|g), dat),
"Duplicated group-level effects are not allowed")
expect_error(brm(y~mo(g)*t2(x), dat), fixed = TRUE,
"The term 'mo(g):t2(x)' is invalid")
expect_error(brm(y~x*cs(g), dat), fixed = TRUE,
"The term 'x:cs(g)' is invalid")
expect_error(brm(y~me(x, 2 * g)*me(x, g), dat),
"Variable 'x' is used in different calls to 'me'")
# autocorrelation
expect_error(brm(y ~ ar(x+y, g), dat),
"Cannot coerce 'x \\+ y' to a single variable name")
expect_error(brm(y ~ ar(gr = g1/g2), dat),
"Illegal grouping term 'g1/g2'")
expect_error(brm(y ~ ma(x), dat, poisson()),
"Please set cov = TRUE")
# ordinal models
expect_error(brm(rating ~ treat + (cs(period)|subject),
data = inhaler, family = categorical()),
"Category specific effects require families")
# families and links
expect_error(brm(y ~ x, dat, family = gaussian("logit")),
"'logit' is not a supported link for family 'gaussian'")
expect_error(brm(y ~ x, dat, family = poisson("inverse")),
"'inverse' is not a supported link for family 'poisson'")
expect_error(brm(y ~ x, dat, family = c("weibull", "sqrt")),
"'sqrt' is not a supported link for family 'weibull'")
expect_error(brm(y ~ x, dat, family = c("categorical", "probit")),
"'probit' is not a supported link for family 'categorical'")
expect_error(brm(y ~ x, dat, family = "ordinal"),
"ordinal is not a supported family")
})
brms/tests/testthat/tests.brmsfit-methods.R 0000644 0001762 0000144 00000076002 13617520155 020624 0 ustar ligges users context("Tests for brmsfit methods")
expect_range <- function(object, lower = -Inf, upper = Inf, ...) {
testthat::expect_true(all(object >= lower & object <= upper), ...)
}
expect_ggplot <- function(object, ...) {
testthat::expect_true(is(object, "ggplot"), ...)
}
SM <- suppressMessages
SW <- suppressWarnings
fit1 <- brms:::rename_pars(brms:::brmsfit_example1)
fit2 <- brms:::rename_pars(brms:::brmsfit_example2)
fit3 <- brms:::rename_pars(brms:::brmsfit_example3)
fit4 <- brms:::rename_pars(brms:::brmsfit_example4)
fit5 <- brms:::rename_pars(brms:::brmsfit_example5)
fit6 <- brms:::rename_pars(brms:::brmsfit_example6)
# test S3 methods in alphabetical order
test_that("as.data.frame has reasonable ouputs", {
ps <- as.data.frame(fit1)
expect_true(is(ps, "data.frame"))
expect_equal(dim(ps), c(nsamples(fit1), length(parnames(fit1))))
})
test_that("as.matrix has reasonable ouputs", {
ps <- as.matrix(fit1)
expect_true(is(ps, "matrix"))
expect_equal(dim(ps), c(nsamples(fit1), length(parnames(fit1))))
})
test_that("as.array has reasonable ouputs", {
ps <- as.array(fit1)
expect_true(is.array(ps))
chains <- fit1$fit@sim$chains
ps_dim <- c(nsamples(fit1) / chains, chains, length(parnames(fit1)))
expect_equal(dim(ps), ps_dim)
})
test_that("as.mcmc has reasonable ouputs", {
chains <- fit1$fit@sim$chains
mc <- as.mcmc(fit1)
expect_equal(length(mc), chains)
expect_equal(dim(mc[[1]]), c(nsamples(fit1) / chains, length(parnames(fit1))))
mc <- as.mcmc(fit1, combine_chains = TRUE)
expect_equal(dim(mc), c(nsamples(fit1), length(parnames(fit1))))
# test assumes thin = 1
expect_equal(dim(as.mcmc(fit1, inc_warmup = TRUE)[[1]]),
c(fit1$fit@sim$iter, length(parnames(fit1))))
})
test_that("autocor has reasonable ouputs", {
expect_true(is.null(SW(autocor(fit1))))
expect_true(is.null(SW(autocor(fit6, resp = "count"))))
})
test_that("bayes_R2 has reasonable ouputs", {
fit1 <- add_criterion(fit1, "bayes_R2")
R2 <- bayes_R2(fit1, summary = FALSE)
expect_equal(dim(R2), c(nsamples(fit1), 1))
R2 <- bayes_R2(fit2, newdata = model.frame(fit2)[1:5, ], re_formula = NA)
expect_equal(dim(R2), c(1, 4))
R2 <- bayes_R2(fit6)
expect_equal(dim(R2), c(2, 4))
})
test_that("bayes_factor has reasonable ouputs", {
# don't test for now as it requires calling Stan's C++ code
})
test_that("bridge_sampler has reasonable ouputs", {
# don't test for now as it requires calling Stan's C++ code
})
test_that("coef has reasonable ouputs", {
coef1 <- SM(coef(fit1))
expect_equal(dim(coef1$visit), c(4, 4, 8))
coef1 <- SM(coef(fit1, summary = FALSE))
expect_equal(dim(coef1$visit), c(nsamples(fit1), 4, 8))
coef2 <- SM(coef(fit2))
expect_equal(dim(coef2$patient), c(59, 4, 4))
coef4 <- SM(coef(fit4))
expect_equal(dim(coef4$subject), c(10, 4, 8))
})
test_that("combine_models has reasonable ouputs", {
expect_equal(nsamples(combine_models(fit1, fit1)), nsamples(fit1) * 2)
})
test_that("conditional_effects has reasonable ouputs", {
me <- conditional_effects(fit1, resp = "count")
expect_equal(nrow(me[[2]]), 100)
meplot <- plot(me, points = TRUE, rug = TRUE,
ask = FALSE, plot = FALSE)
expect_ggplot(meplot[[1]])
me <- conditional_effects(fit1, "Trt", select_points = 0.1)
expect_lt(nrow(attr(me[[1]], "points")), nobs(fit1))
me <- conditional_effects(fit1, "Exp:Age", surface = TRUE,
resolution = 15, too_far = 0.2)
meplot <- plot(me, plot = FALSE)
expect_ggplot(meplot[[1]])
meplot <- plot(me, stype = "raster", plot = FALSE)
expect_ggplot(meplot[[1]])
me <- conditional_effects(fit1, "Age", spaghetti = TRUE, nsamples = 10)
expect_equal(nrow(attr(me$Age, "spaghetti")), 1000)
meplot <- plot(me, plot = FALSE)
expect_ggplot(meplot[[1]])
expect_error(
conditional_effects(fit1, "Age", spaghetti = TRUE, surface = TRUE),
"Cannot use 'spaghetti' and 'surface' at the same time"
)
mdata = data.frame(
Age = c(-0.3, 0, 0.3),
count = c(10, 20, 30),
Exp = c(1, 3, 5)
)
exp_nrow <- nrow(mdata) * 100
me <- conditional_effects(fit1, effects = "Age", conditions = mdata)
expect_equal(nrow(me[[1]]), exp_nrow)
mdata$visit <- 1:3
me <- conditional_effects(fit1, re_formula = NULL, conditions = mdata)
expect_equal(nrow(me$Age), exp_nrow)
me <- conditional_effects(
fit1, "Age:Trt", int_conditions = list(Age = rnorm(5))
)
expect_equal(nrow(me[[1]]), 200)
me <- conditional_effects(
fit1, "Age:Trt", int_conditions = list(Age = quantile)
)
expect_equal(nrow(me[[1]]), 200)
expect_error(conditional_effects(fit1, effects = "Trtc"),
"All specified effects are invalid for this model")
expect_warning(conditional_effects(fit1, effects = c("Trtc", "Trt")),
"Some specified effects are invalid for this model")
expect_error(conditional_effects(fit1, effects = "Trtc:a:b"),
"please use the 'conditions' argument")
mdata$visit <- NULL
mdata$patient <- 1
expect_equal(nrow(conditional_effects(fit2)[[2]]), 100)
me <- conditional_effects(fit2, re_formula = NULL, conditions = mdata)
expect_equal(nrow(me$Age), exp_nrow)
expect_warning(
me4 <- conditional_effects(fit4),
"Predictions are treated as continuous variables"
)
expect_true(is(me4, "brms_conditional_effects"))
me4 <- conditional_effects(fit4, "x2", categorical = TRUE)
expect_true(is(me4, "brms_conditional_effects"))
me5 <- conditional_effects(fit5)
expect_true(is(me5, "brms_conditional_effects"))
me6 <- conditional_effects(fit6, nsamples = 40)
expect_true(is(me6, "brms_conditional_effects"))
})
test_that("plot of conditional_effects has reasonable outputs", {
ggplot2::theme_set(theme_black())
N <- 90
marg_results <- data.frame(
effect1__ = rpois(N, 20),
effect2__ = factor(rep(1:3, each = N / 3)),
estimate__ = rnorm(N, sd = 5),
se__ = rt(N, df = 10),
cond__ = rep(1:2, each = N / 2),
cats__ = factor(rep(1:3, each = N / 3))
)
marg_results[["lower__"]] <- marg_results$estimate__ - 2
marg_results[["upper__"]] <- marg_results$estimate__ + 2
marg_results <- list(marg_results[order(marg_results$effect1__), ])
class(marg_results) <- "brmsMarginalEffects"
attr(marg_results[[1]], "response") <- "count"
# test with 1 numeric predictor
attr(marg_results[[1]], "effects") <- "P1"
marg_plot <- plot(marg_results, plot = FALSE)
expect_ggplot(marg_plot[[1]])
# test with 1 categorical predictor
attr(marg_results[[1]], "effects") <- "P2"
marg_plot <- plot(marg_results, plot = FALSE)
expect_ggplot(marg_plot[[1]])
# test with 1 numeric and 1 categorical predictor
attr(marg_results[[1]], "effects") <- c("P1", "P2")
marg_plot <- plot(marg_results, plot = FALSE)
expect_ggplot(marg_plot[[1]])
# test ordinal raster plot
attr(marg_results[[1]], "effects") <- c("P1", "cats__")
attr(marg_results[[1]], "ordinal") <- TRUE
marg_plot <- plot(marg_results, plot = FALSE)
expect_ggplot(marg_plot[[1]])
})
test_that("conditional_smooths has reasonable ouputs", {
ms <- conditional_smooths(fit1)
expect_equal(nrow(ms[[1]]), 100)
expect_true(is(ms, "brms_conditional_effects"))
ms <- conditional_smooths(fit1, spaghetti = TRUE, nsamples = 10)
expect_equal(nrow(attr(ms[[1]], "spaghetti")), 1000)
expect_error(conditional_smooths(fit1, smooths = "s3"),
"No valid smooth terms found in the model")
expect_error(conditional_smooths(fit2),
"No valid smooth terms found in the model")
})
test_that("family has reasonable ouputs", {
expect_is(family(fit1), "brmsfamily")
expect_is(family(fit6, resp = "count"), "brmsfamily")
expect_output(print(family(fit1), links = TRUE), "student.*log.*logm1")
expect_output(print(family(fit5)), "Mixture.*gaussian.*exponential")
})
test_that("fitted has reasonable outputs", {
skip_on_cran()
fi <- fitted(fit1)
expect_equal(dim(fi), c(nobs(fit1), 4))
expect_equal(colnames(fi), c("Estimate", "Est.Error", "Q2.5", "Q97.5"))
newdata <- data.frame(
Age = c(0, -0.2), visit = c(1, 4), Trt = c(0, 1),
count = c(20, 13), patient = c(1, 42), Exp = c(2, 4)
)
fi <- fitted(fit1, newdata = newdata)
expect_equal(dim(fi), c(2, 4))
newdata$visit <- c(1, 6)
fi <- fitted(fit1, newdata = newdata,
allow_new_levels = TRUE)
expect_equal(dim(fi), c(2, 4))
# fitted values with new_levels
newdata <- data.frame(
Age = 0, visit = paste0("a", 1:100), Trt = 0,
count = 20, patient = 1, Exp = 2
)
fi <- fitted(fit1, newdata = newdata, allow_new_levels = TRUE,
sample_new_levels = "old_levels", nsamples = 10)
expect_equal(dim(fi), c(100, 4))
fi <- fitted(fit1, newdata = newdata, allow_new_levels = TRUE,
sample_new_levels = "gaussian", nsamples = 1)
expect_equal(dim(fi), c(100, 4))
# fitted values of auxiliary parameters
newdata <- data.frame(
Age = 0, visit = c("a", "b"), Trt = 0,
count = 20, patient = 1, Exp = 2
)
fi <- fitted(fit1, dpar = "sigma")
expect_equal(dim(fi), c(nobs(fit1), 4))
expect_true(all(fi > 0))
fi_lin <- fitted(fit1, dpar = "sigma", scale = "linear")
expect_equal(dim(fi_lin), c(nobs(fit1), 4))
expect_true(!isTRUE(all.equal(fi, fi_lin)))
expect_error(fitted(fit1, dpar = "inv"),
"Invalid argument 'dpar'")
fi <- fitted(fit2)
expect_equal(dim(fi), c(nobs(fit2), 4))
fi <- fitted(fit2, newdata = newdata,
allow_new_levels = TRUE)
expect_equal(dim(fi), c(2, 4))
fi <- fitted(fit2, dpar = "shape")
expect_equal(dim(fi), c(nobs(fit2), 4))
expect_equal(fi[1, ], fi[2, ])
fi <- fitted(fit2, nlpar = "a")
expect_equal(dim(fi), c(nobs(fit2), 4))
fi <- fitted(fit3, newdata = fit3$data[1:10, ])
expect_equal(dim(fi), c(10, 4))
fi <- fitted(fit4)
expect_equal(dim(fi), c(nobs(fit4), 4, 4))
fi <- fitted(fit4, newdata = fit4$data[1, ])
expect_equal(dim(fi), c(1, 4, 4))
fi <- fitted(fit4, newdata = fit4$data[1, ], scale = "linear")
expect_equal(dim(fi), c(1, 4, 3))
fi <- fitted(fit5)
expect_equal(dim(fi), c(nobs(fit5), 4))
fi <- fitted(fit6)
expect_equal(dim(fi), c(nobs(fit6), 4, 2))
expect_equal(dimnames(fi)[[3]], c("volume", "count"))
})
test_that("fixef has reasonable ouputs", {
fixef1 <- SM(fixef(fit1))
expect_equal(rownames(fixef1),
c("Intercept", "sigma_Intercept", "Trt1", "Age",
"Trt1:Age", "sigma_Trt1", "sAge_1", "moExp")
)
fixef1 <- SM(fixef(fit1, pars = c("Age", "sAge_1")))
expect_equal(rownames(fixef1), c("Age", "sAge_1"))
})
test_that("formula has reasonable ouputs", {
expect_true(is.brmsformula(formula(fit1)))
})
test_that("hypothesis has reasonable ouputs", {
hyp <- hypothesis(fit1, c("Age > Trt1", "Trt1:Age = -1"))
expect_equal(dim(hyp$hypothesis), c(2, 8))
expect_output(print(hyp), "(Age)-(Trt1) > 0", fixed = TRUE)
expect_ggplot(plot(hyp, plot = FALSE)[[1]])
hyp <- hypothesis(fit1, "Intercept = 0", class = "sd", group = "visit")
expect_true(is.numeric(hyp$hypothesis$Evid.Ratio[1]))
expect_output(print(hyp), "class sd_visit:", fixed = TRUE)
expect_ggplot(plot(hyp, ignore_prior = TRUE, plot = FALSE)[[1]])
hyp <- hypothesis(fit1, "0 > r_visit[4,Intercept]", class = "", alpha = 0.01)
expect_equal(dim(hyp$hypothesis), c(1, 8))
expect_output(print(hyp, chars = NULL), "r_visit[4,Intercept]", fixed = TRUE)
expect_output(print(hyp), "99%-CI", fixed = TRUE)
hyp <- hypothesis(
fit1, c("Intercept = 0", "Intercept + exp(Trt1) = 0"),
group = "visit", scope = "coef"
)
expect_equal(dim(hyp$hypothesis), c(8, 9))
expect_equal(hyp$hypothesis$Group[1], factor(1, levels = 1:4))
expect_error(hypothesis(fit1, "Intercept > x"), fixed = TRUE,
"cannot be found in the model: \n'b_x'")
expect_error(hypothesis(fit1, 1),
"Argument 'hypothesis' must be a character vector")
expect_error(hypothesis(fit2, "b_Age = 0", alpha = 2),
"Argument 'alpha' must be a single value in [0,1]",
fixed = TRUE)
expect_error(hypothesis(fit3, "b_Age x 0"),
"Every hypothesis must be of the form 'left (= OR < OR >) right'",
fixed = TRUE)
# test hypothesis.default method
hyp <- hypothesis(as.data.frame(fit3), "bsp_meAgeAgeSD > sigma")
expect_equal(dim(hyp$hypothesis), c(1, 8))
hyp <- hypothesis(fit3$fit, "bsp_meAgeAgeSD > sigma")
expect_equal(dim(hyp$hypothesis), c(1, 8))
})
test_that("launch_shinystan has reasonable ouputs", {
# requires running shiny which is not reasonable in automated tests
})
test_that("log_lik has reasonable ouputs", {
expect_equal(dim(log_lik(fit1)), c(nsamples(fit1), nobs(fit1)))
expect_equal(dim(logLik(fit1)), c(nsamples(fit1), nobs(fit1)))
expect_equal(dim(log_lik(fit2)), c(nsamples(fit2), nobs(fit2)))
})
test_that("loo has reasonable outputs", {
skip_on_cran()
loo1 <- SW(LOO(fit1, cores = 1))
expect_true(is.numeric(loo1$estimates))
expect_output(print(loo1), "looic")
loo_compare1 <- SW(loo(fit1, fit1, cores = 1))
expect_equal(names(loo_compare1$loos), c("fit1", "fit1"))
expect_equal(dim(loo_compare1$ic_diffs__), c(1, 2))
expect_output(print(loo_compare1), "'fit1':")
expect_is(loo_compare1$diffs, "compare.loo")
loo2 <- SW(loo(fit2, cores = 1))
expect_true(is.numeric(loo2$estimates))
loo3 <- SW(loo(fit3, cores = 1))
expect_true(is.numeric(loo3$estimates))
loo3 <- SW(loo(fit3, pointwise = TRUE, cores = 1))
expect_true(is.numeric(loo3$estimates))
loo4 <- SW(loo(fit4, cores = 1))
expect_true(is.numeric(loo4$estimates))
# fails because of too small effective sample size
# loo5 <- SW(loo(fit5, cores = 1))
# expect_true(is.numeric(loo5$estimates))
loo6_1 <- SW(loo(fit6, cores = 1))
expect_true(is.numeric(loo6_1$estimates))
loo6_2 <- SW(loo(fit6, cores = 1, newdata = fit6$data))
expect_true(is.numeric(loo6_2$estimates))
loo_compare <- loo_compare(loo6_1, loo6_2)
expect_range(loo_compare[2, 1], -1, 1)
})
test_that("loo_subsample has reasonable outputs", {
skip_on_cran()
loo2 <- SW(loo_subsample(fit2, observations = 50))
expect_true(is.numeric(loo2$estimates))
expect_equal(nrow(loo2$pointwise), 50)
expect_output(print(loo2), "looic")
})
test_that("loo_R2 has reasonable outputs", {
skip_on_cran()
R2 <- SW(loo_R2(fit1))
expect_equal(length(R2), 1)
# fails on travis for some strange reason
# R2 <- SW(loo_R2(fit6))
# expect_equal(length(R2), 2)
})
test_that("loo_linpred has reasonable outputs", {
skip_on_cran()
llp <- SW(loo_linpred(fit1))
expect_equal(length(llp), nobs(fit1))
expect_error(loo_linpred(fit4), "Method 'loo_linpred'")
llp <- SW(loo_linpred(fit2, scale = "response", type = "var"))
expect_equal(length(llp), nobs(fit2))
})
test_that("loo_predict has reasonable outputs", {
skip_on_cran()
llp <- SW(loo_predict(fit1))
expect_equal(length(llp), nobs(fit1))
newdata <- data.frame(
Age = 0, visit = c("a", "b"), Trt = 0,
count = 20, patient = 1, Exp = 2
)
llp <- SW(loo_predict(
fit1, newdata = newdata,
type = "quantile", probs = c(0.25, 0.75),
allow_new_levels = TRUE
))
expect_equal(dim(llp), c(2, nrow(newdata)))
llp <- SW(loo_predict(fit4))
expect_equal(length(llp), nobs(fit4))
})
test_that("loo_predictive_interval has reasonable outputs", {
skip_on_cran()
llp <- SW(loo_predictive_interval(fit3))
expect_equal(dim(llp), c(nobs(fit3), 2))
})
test_that("loo_model_weights has reasonable outputs", {
skip_on_cran()
llw <- SW(loo_model_weights(fit1, fit1))
expect_is(llw[1:2], "numeric")
expect_equal(names(llw), c("fit1", "fit1"))
})
test_that("model.frame has reasonable ouputs", {
expect_equal(model.frame(fit1), fit1$data)
})
test_that("model_weights has reasonable ouputs", {
mw <- model_weights(fit1, fit1, weights = "waic")
expect_equal(mw, setNames(c(0.5, 0.5), c("fit1", "fit1")))
})
test_that("ngrps has reasonable ouputs", {
expect_equal(ngrps(fit1), list(visit = 4))
expect_equal(ngrps(fit2), list(patient = 59))
})
test_that("nobs has reasonable ouputs", {
expect_equal(nobs(fit1), nrow(epilepsy))
})
test_that("nsamples has reasonable ouputs", {
expect_equal(nsamples(fit1), 50)
expect_equal(nsamples(fit1, subset = 10:1), 10)
expect_equal(nsamples(fit1, incl_warmup = TRUE), 200)
})
test_that("pairs has reasonable outputs", {
expect_s3_class(SW(pairs(fit1, pars = parnames(fit1)[1:3])),
"bayesplot_grid")
})
test_that("parnames has reasonable ouputs", {
expect_true(all(
c("b_Intercept", "bsp_moExp", "ar[1]", "cor_visit__Intercept__Trt1",
"nu", "simo_moExp1[2]", "r_visit[4,Trt1]", "s_sAge_1[8]",
"prior_sd_visit", "prior_cor_visit", "lp__") %in%
parnames(fit1)
))
expect_true(all(
c("b_a_Intercept", "b_b_Age", "sd_patient__b_Intercept",
"cor_patient__a_Intercept__b_Intercept",
"r_patient__a[1,Intercept]", "r_patient__b[4,Intercept]",
"prior_b_a") %in%
parnames(fit2)
))
expect_true(all(
c("lscale_volume_gpAgeTrt0", "lscale_volume_gpAgeTrt1") %in%
parnames(fit6)
))
})
test_that("plot has reasonable outputs", {
expect_silent(p <- plot(fit1, plot = FALSE))
expect_silent(p <- plot(fit1, pars = "^b", plot = FALSE))
expect_silent(p <- plot(fit1, pars = "^sd", plot = FALSE))
expect_error(plot(fit1, pars = "123"), "No valid parameters selected")
})
test_that("post_prob has reasonable ouputs", {
# only test error messages for now
expect_error(post_prob(fit1, fit2, model_names = "test1"),
"Number of model names is not equal to the number of models")
})
test_that("posterior_average has reasonable outputs", {
pnames <- c("b_Age", "nu")
ps <- posterior_average(fit1, fit1, pars = pnames, weights = c(0.3, 0.7))
expect_equal(dim(ps), c(nsamples(fit1), 2))
expect_equal(names(ps), pnames)
weights <- rexp(3)
ps <- brms:::SW(posterior_average(
fit1, fit2, fit3, pars = "nu", weights = rexp(3),
missing = 1, nsamples = 10
))
expect_equal(dim(ps), c(10, 1))
expect_equal(names(ps), "nu")
})
test_that("posterior_samples has reasonable outputs", {
ps <- posterior_samples(fit1)
expect_equal(dim(ps), c(nsamples(fit1), length(parnames(fit1))))
expect_equal(names(ps), parnames(fit1))
expect_equal(names(posterior_samples(fit1, pars = "^b_")),
c("b_Intercept", "b_sigma_Intercept", "b_Trt1",
"b_Age", "b_Trt1:Age", "b_sigma_Trt1"))
# test default method
ps <- posterior_samples(fit1$fit, "^b_Intercept$")
expect_equal(dim(ps), c(nsamples(fit1), 1))
})
test_that("posterior_summary has reasonable outputs", {
ps <- posterior_summary(fit1, "^b_")
expect_equal(dim(ps), c(6, 4))
})
test_that("posterior_interval has reasonable outputs", {
expect_equal(dim(posterior_interval(fit1)),
c(length(parnames(fit1)), 2))
})
test_that("posterior_predict has reasonable outputs", {
expect_equal(dim(posterior_predict(fit1)),
c(nsamples(fit1), nobs(fit1)))
})
test_that("posterior_linpred has reasonable outputs", {
expect_equal(dim(posterior_linpred(fit1)),
c(nsamples(fit1), nobs(fit1)))
})
test_that("pp_average has reasonable outputs", {
ppa <- pp_average(fit1, fit1, weights = "waic")
expect_equal(dim(ppa), c(nobs(fit1), 4))
ppa <- pp_average(fit1, fit1, weights = c(1, 4))
expect_equal(attr(ppa, "weights"), c(fit1 = 0.2, fit1 = 0.8))
ns <- c(fit1 = nsamples(fit1) / 5, fit1 = 4 * nsamples(fit1) / 5)
expect_equal(attr(ppa, "nsamples"), ns)
})
test_that("pp_check has reasonable outputs", {
expect_ggplot(pp_check(fit1))
expect_ggplot(pp_check(fit1, newdata = fit1$data[1:100, ]))
expect_ggplot(pp_check(fit1, "stat", nsamples = 5))
expect_ggplot(pp_check(fit1, "error_binned"))
pp <- pp_check(fit1, "ribbon_grouped", group = "visit", x = "Age")
expect_ggplot(pp)
pp <- pp_check(fit1, type = "violin_grouped",
group = "visit", newdata = fit1$data[1:100, ])
expect_ggplot(pp)
pp <- SW(pp_check(fit1, type = "loo_pit", cores = 1))
expect_ggplot(pp)
expect_ggplot(pp_check(fit3))
expect_ggplot(pp_check(fit2, "ribbon", x = "Age"))
expect_error(pp_check(fit2, "ribbon", x = "x"),
"Variable 'x' could not be found in the data")
expect_error(pp_check(fit1, "wrong_type"))
expect_error(pp_check(fit2, "violin_grouped"), "group")
expect_error(pp_check(fit1, "stat_grouped", group = "g"),
"Variable 'g' could not be found in the data")
expect_ggplot(pp_check(fit4))
expect_ggplot(pp_check(fit5))
expect_error(pp_check(fit4, "error_binned"),
"Type 'error_binned' is not available")
})
test_that("pp_expect has reasonable outputs", {
expect_equal(dim(pp_expect(fit1)), c(nsamples(fit1), nobs(fit1)))
})
test_that("pp_mixture has reasonable outputs", {
expect_equal(dim(pp_mixture(fit5)), c(nobs(fit5), 4, 2))
expect_error(pp_mixture(fit1),
"Method 'pp_mixture' can only be applied to mixture models"
)
})
test_that("predict has reasonable outputs", {
pred <- predict(fit1)
expect_equal(dim(pred), c(nobs(fit1), 4))
expect_equal(colnames(pred), c("Estimate", "Est.Error", "Q2.5", "Q97.5"))
pred <- predict(fit1, nsamples = 10, probs = c(0.2, 0.5, 0.8))
expect_equal(dim(pred), c(nobs(fit1), 5))
newdata <- data.frame(
Age = c(0, -0.2), visit = c(1, 4), Trt = c(1, 0),
count = c(2, 10), patient = c(1, 42), Exp = c(1, 2)
)
pred <- predict(fit1, newdata = newdata)
expect_equal(dim(pred), c(2, 4))
newdata$visit <- c(1, 6)
pred <- predict(fit1, newdata = newdata, allow_new_levels = TRUE)
expect_equal(dim(pred), c(2, 4))
# predict NA responses in ARMA models
df <- fit1$data[1:10, ]
df$count[8:10] <- NA
pred <- predict(fit1, newdata = df, nsamples = 1)
expect_true(!anyNA(pred[, "Estimate"]))
pred <- predict(fit2)
expect_equal(dim(pred), c(nobs(fit2), 4))
pred <- predict(fit2, newdata = newdata, allow_new_levels = TRUE)
expect_equal(dim(pred), c(2, 4))
# check if grouping factors with a single level are accepted
newdata$patient <- factor(2)
pred <- predict(fit2, newdata = newdata)
expect_equal(dim(pred), c(2, 4))
pred <- predict(fit4)
expect_equal(dim(pred), c(nobs(fit4), 4))
expect_equal(colnames(pred), paste0("P(Y = ", 1:4, ")"))
pred <- predict(fit4, newdata = fit4$data[1, ])
expect_equal(dim(pred), c(1, 4))
pred <- predict(fit5)
expect_equal(dim(pred), c(nobs(fit5), 4))
newdata <- fit5$data[1:10, ]
newdata$patient <- "a"
pred <- predict(fit5, newdata, allow_new_levels = TRUE,
sample_new_levels = "old_levels")
expect_equal(dim(pred), c(10, 4))
pred <- predict(fit5, newdata, allow_new_levels = TRUE,
sample_new_levels = "gaussian")
expect_equal(dim(pred), c(10, 4))
})
test_that("predictive_error has reasonable outputs", {
expect_equal(dim(predictive_error(fit1)),
c(nsamples(fit1), nobs(fit1)))
})
test_that("print has reasonable outputs", {
expect_output(SW(print(fit1)), "Group-Level Effects:")
})
test_that("prior_samples has reasonable outputs", {
prs1 <- prior_samples(fit1)
prior_names <- c(
"Intercept", "b", "bsp", paste0("simo_moExp1[", 1:4, "]"),
"bs", "sds_sAge_1", "b_sigma", "Intercept_sigma", "nu",
"sd_visit", "cor_visit"
)
expect_equal(colnames(prs1), prior_names)
prs2 <- prior_samples(fit1, pars = "b_Trt1")
expect_equal(dimnames(prs2), list(as.character(1:nsamples(fit1)), "b_Trt1"))
expect_equal(sort(prs1$b), sort(prs2$b_Trt))
# test default method
prs <- prior_samples(fit1$fit, pars = "^sd_visit")
expect_equal(names(prs), "prior_sd_visit")
})
test_that("prior_summary has reasonable outputs", {
expect_true(is(prior_summary(fit1), "brmsprior"))
})
test_that("ranef has reasonable outputs", {
ranef1 <- SM(ranef(fit1))
expect_equal(dim(ranef1$visit), c(4, 4, 2))
ranef1 <- SM(ranef(fit1, pars = "Trt1"))
expect_equal(dimnames(ranef1$visit)[[3]], "Trt1")
ranef1 <- SM(ranef(fit1, groups = "a"))
expect_equal(length(ranef1), 0L)
ranef2 <- SM(ranef(fit2, summary = FALSE))
expect_equal(dim(ranef2$patient), c(nsamples(fit2), 59, 2))
})
test_that("residuals has reasonable outputs", {
res1 <- SW(residuals(fit1, type = "pearson", probs = c(0.65)))
expect_equal(dim(res1), c(nobs(fit1), 3))
newdata <- cbind(epilepsy[1:10, ], Exp = rep(1:5, 2))
res2 <- residuals(fit1, newdata = newdata)
expect_equal(dim(res2), c(10, 4))
newdata$visit <- rep(1:5, 2)
res3 <- residuals(fit1, newdata = newdata, allow_new_levels = TRUE)
expect_equal(dim(res3), c(10, 4))
res4 <- residuals(fit2)
expect_equal(dim(res4), c(nobs(fit2), 4))
expect_error(residuals(fit4), "Predictive errors are not defined")
res6 <- residuals(fit6)
expect_equal(dim(res6), c(nobs(fit6), 4, 2))
expect_equal(dimnames(res6)[[3]], c("volume", "count"))
})
test_that("stancode has reasonable outputs", {
expect_true(is.character(stancode(fit1)))
expect_output(print(stancode(fit1)), "generated quantities")
})
test_that("standata has reasonable outputs", {
expect_equal(sort(names(standata(fit1))),
sort(c("N", "Y", "Kar", "Kma", "J_lag", "K", "X", "Ksp", "Imo",
"Xmo_1", "Jmo", "con_simo_1", "Z_1_1", "Z_1_2", "nb_1",
"knots_1", "Zs_1_1", "Ks", "Xs", "offsets", "K_sigma",
"X_sigma", "J_1", "N_1", "M_1", "NC_1", "prior_only"))
)
expect_equal(sort(names(standata(fit2))),
sort(c("N", "Y", "weights", "C_1", "K_a", "X_a", "Z_1_a_1",
"K_b", "X_b", "Z_1_b_2", "J_1", "N_1", "M_1",
"NC_1", "prior_only"))
)
})
test_that("mcmc_plot has reasonable outputs", {
expect_ggplot(mcmc_plot(fit1))
expect_ggplot(mcmc_plot(fit1, pars = "^b"))
expect_ggplot(SM(mcmc_plot(fit1, type = "trace", pars = "^b_")))
expect_ggplot(mcmc_plot(fit1, type = "hist", pars = "^sd_"))
expect_ggplot(mcmc_plot(fit1, type = "dens"))
expect_ggplot(mcmc_plot(fit1, type = "scatter",
pars = parnames(fit1)[2:3],
fixed = TRUE))
expect_ggplot(SW(mcmc_plot(fit1, type = "rhat", pars = "^b_")))
expect_ggplot(SW(mcmc_plot(fit1, type = "neff")))
expect_ggplot(mcmc_plot(fit1, type = "acf"))
expect_silent(p <- mcmc_plot(fit1, type = "nuts_divergence"))
expect_error(mcmc_plot(fit1, type = "density"), "Invalid plot type")
expect_error(mcmc_plot(fit1, type = "hex"),
"Exactly 2 parameters must be selected")
})
test_that("summary has reasonable outputs", {
summary1 <- SW(summary(fit1, priors = TRUE))
expect_true(is.numeric(summary1$fixed))
expect_equal(rownames(summary1$fixed),
c("Intercept", "sigma_Intercept", "Trt1", "Age",
"Trt1:Age", "sigma_Trt1", "sAge_1", "moExp"))
expect_equal(colnames(summary1$fixed),
c("Estimate", "Est.Error", "l-95% CI",
"u-95% CI", "Rhat", "Bulk_ESS", "Tail_ESS"))
expect_equal(rownames(summary1$random$visit),
c("sd(Intercept)", "sd(Trt1)", "cor(Intercept,Trt1)"))
expect_output(print(summary1), "Population-Level Effects:")
expect_output(print(summary1), "Priors:")
summary5 <- SW(summary(fit5))
expect_output(print(summary5), "sigma1")
expect_output(print(summary5), "theta1")
summary6 <- SW(summary(fit6))
expect_output(print(summary6), "sdgp")
})
test_that("update has reasonable outputs", {
# Do not actually refit the model as is causes CRAN checks to fail.
# Some tests are commented out as they fail when updating Stan code
# of internal example models because of Stan code mismatches. Refitting
# these example models is slow especially when done repeatedly and
# leads the git repo to blow up eventually due the size of the models.
up <- update(fit1, testmode = TRUE)
expect_true(is(up, "brmsfit"))
new_data <- data.frame(
Age = rnorm(18), visit = rep(c(3, 2, 4), 6),
Trt = rep(0:1, 9), count = rep(c(5, 17, 28), 6),
patient = 1, Exp = 4
)
up <- update(fit1, newdata = new_data, save_ranef = FALSE, testmode = TRUE)
expect_true(is(up, "brmsfit"))
expect_equal(up$data.name, "new_data")
# expect_equal(attr(up$ranef, "levels")$visit, c("2", "3", "4"))
# expect_true("r_1_1" %in% up$exclude)
expect_error(update(fit1, data = new_data), "use argument 'newdata'")
up <- update(fit1, formula = ~ . + I(exp(Age)), testmode = TRUE,
prior = set_prior("normal(0,10)"))
expect_true(is(up, "brmsfit"))
up <- update(fit1, ~ . - Age + factor(Age), testmode = TRUE)
expect_true(is(up, "brmsfit"))
up <- update(fit1, formula = ~ . + I(exp(Age)), newdata = new_data,
sample_prior = FALSE, testmode = TRUE)
expect_true(is(up, "brmsfit"))
expect_error(update(fit1, formula. = ~ . + wrong_var),
"New variables found: 'wrong_var'")
up <- update(fit1, save_ranef = FALSE, testmode = TRUE)
expect_true(is(up, "brmsfit"))
# expect_true("r_1_1" %in% up$exclude)
up <- update(fit3, save_mevars = FALSE, testmode = TRUE)
expect_true(is(up, "brmsfit"))
# expect_true("Xme_1" %in% up$exclude)
up <- update(fit2, algorithm = "fullrank", testmode = TRUE)
expect_true(is(up, "brmsfit"))
# expect_equal(up$algorithm, "fullrank")
up <- update(fit2, formula. = bf(. ~ ., a + b ~ 1, nl = TRUE),
testmode = TRUE)
expect_true(is(up, "brmsfit"))
up <- update(fit2, formula. = bf(count ~ a + b, nl = TRUE), testmode = TRUE)
expect_true(is(up, "brmsfit"))
up <- update(fit3, family = acat(), testmode = TRUE)
expect_true(is(up, "brmsfit"))
up <- update(fit3, bf(~., family = acat()), testmode = TRUE)
expect_true(is(up, "brmsfit"))
})
test_that("VarCorr has reasonable outputs", {
vc <- VarCorr(fit1)
expect_equal(names(vc), c("visit"))
Names <- c("Intercept", "Trt1")
expect_equal(dimnames(vc$visit$cov)[c(1, 3)], list(Names, Names))
vc <- VarCorr(fit2)
expect_equal(names(vc), c("patient"))
expect_equal(dim(vc$patient$cor), c(2, 4, 2))
vc <- VarCorr(fit2, summary = FALSE)
expect_equal(dim(vc$patient$cor), c(nsamples(fit2), 2, 2))
expect_equal(dim(VarCorr(fit6)$residual__$sd), c(1, 4))
vc <- VarCorr(fit5)
expect_equal(dim(vc$patient$sd), c(2, 4))
})
test_that("vcov has reasonable outputs", {
expect_equal(dim(vcov(fit1)), c(8, 8))
expect_equal(dim(vcov(fit1, cor = TRUE)), c(8, 8))
})
test_that("waic has reasonable outputs", {
waic1 <- SW(WAIC(fit1))
expect_true(is.numeric(waic1$estimates))
expect_equal(waic1, SW(waic(fit1)))
fit1 <- SW(add_criterion(fit1, "waic"))
expect_equal(waic(fit1), fit1$criteria$waic)
waic_compare <- SW(waic(fit1, fit1))
expect_equal(length(waic_compare$loos), 2)
expect_equal(dim(waic_compare$ic_diffs__), c(1, 2))
waic2 <- SW(waic(fit2))
expect_true(is.numeric(waic2$estimates))
waic_pointwise <- SW(waic(fit2, pointwise = TRUE))
expect_equal(waic2, waic_pointwise)
expect_warning(compare_ic(waic1, waic2),
"Model comparisons are likely invalid")
waic4 <- SW(waic(fit4))
expect_true(is.numeric(waic4$estimates))
})
test_that("diagnostic convenience functions have reasonable outputs", {
expect_true(is(log_posterior(fit1), "data.frame"))
expect_true(is(nuts_params(fit1), "data.frame"))
expect_true(is(rhat(fit1), "numeric"))
expect_true(is(neff_ratio(fit1), "numeric"))
})
test_that("contrasts of grouping factors are not stored #214", {
expect_true(is.null(attr(fit1$data$patient, "contrasts")))
})
brms/tests/testthat/tests.data-helpers.R 0000644 0001762 0000144 00000002211 13611527526 020060 0 ustar ligges users context("Tests for data helper functions")
test_that("validate_newdata handles factors correctly", {
fit <- brms:::rename_pars(brms:::brmsfit_example1)
fit$data$fac <- factor(sample(1:3, nrow(fit$data), TRUE))
newdata <- fit$data[1:5, ]
expect_silent(brms:::validate_newdata(newdata, fit))
newdata$visit <- 1:5
expect_error(brms:::validate_newdata(newdata, fit),
"Levels '5' of grouping factor 'visit' cannot")
newdata$fac <- 1:5
expect_error(brms:::validate_newdata(newdata, fit),
"New factor levels are not allowed")
})
test_that("validate_data returns correct model.frames", {
dat <- data.frame(y = 1:5, x = 1:5, z = 6:10, g = 5:1)
bterms <- parse_bf(y ~ as.numeric(x) + (as.factor(z) | g),
family = gaussian())
mf <- brms:::validate_data(dat, bterms = bterms)
expect_true(all(c("x", "z") %in% names(mf)))
bterms <- parse_bf(y ~ 1 + (1|g/x/z), family = gaussian())
mf <- brms:::validate_data(dat, bterms = bterms)
expect_equal(mf[["g:x"]], paste0(dat$g, "_", dat$x))
expect_equal(mf[["g:x:z"]], paste0(dat$g, "_", dat$x, "_", dat$z))
})
brms/tests/testthat.R 0000644 0001762 0000144 00000000070 13234633711 014342 0 ustar ligges users library(testthat)
library(brms)
test_check("brms")
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[
](http://mc-stan.org)
brms
====
[](https://travis-ci.org/paul-buerkner/brms)
[](https://codecov.io/github/paul-buerkner/brms?branch=master)
[](https://cran.r-project.org/package=brms)
[](https://CRAN.R-project.org/package=brms)
Overview
--------
The **brms** package provides an interface to fit Bayesian generalized
(non-)linear multivariate multilevel models using Stan, which is a C++
package for performing full Bayesian inference (see
http://mc-stan.org/). The
formula syntax is very similar to that of the package lme4 to provide a
familiar and simple interface for performing regression analyses. A wide
range of response distributions are supported, allowing users to fit –
among others – linear, robust linear, count data, survival, response
times, ordinal, zero-inflated, and even self-defined mixture models all
in a multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, missing value
imputation, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform
distributional regression. Multivariate models (i.e., models with
multiple response variables) can be fit, as well. Prior specifications
are flexible and explicitly encourage users to apply prior distributions
that actually reflect their beliefs. Model fit can easily be assessed
and compared with posterior predictive checks, cross-validation, and
Bayes factors.
Resources
---------
- [Introduction to
brms](https://www.jstatsoft.org/article/view/v080i01) (Journal of
Statistical Software)
- [Advanced multilevel modeling with
brms](https://journal.r-project.org/archive/2018/RJ-2018-017/index.html)
(The R Journal)
- [Website](https://paul-buerkner.github.io/brms) (Website of brms
with documentation and vignettes)
- [Blog posts](https://paul-buerkner.github.io/blog/brms-blogposts/)
(List of blog posts about brms)
- [Ask a question](http://discourse.mc-stan.org/) (Stan Forums on
Discourse)
- [Open an issue](https://github.com/paul-buerkner/brms/issues)
(GitHub issues for bug reports and feature requests)
How to use brms
---------------
``` r
library(brms)
```
As a simple example, we use poisson regression to model the seizure
counts in epileptic patients to investigate whether the treatment
(represented by variable `Trt`) can reduce the seizure counts and
whether the effect of the treatment varies with the (standardized)
baseline number of seizures a person had before treatment (variable
`zBase`). As we have multiple observations per person, a group-level
intercept is incorporated to account for the resulting dependency in the
data.
``` r
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = poisson())
```
The results (i.e., posterior samples) can be investigated using
``` r
summary(fit1)
#> Family: poisson
#> Links: mu = log
#> Formula: count ~ zAge + zBase * Trt + (1 | patient)
#> Data: epilepsy (Number of observations: 236)
#> Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup samples = 4000
#>
#> Group-Level Effects:
#> ~patient (Number of levels: 59)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 0.58 0.07 0.46 0.73 1.01 757 1812
#>
#> Population-Level Effects:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 1.77 0.12 1.54 2.01 1.01 719 946
#> zAge 0.10 0.08 -0.07 0.27 1.00 778 1318
#> zBase 0.71 0.12 0.47 0.95 1.01 593 1323
#> Trt1 -0.27 0.17 -0.61 0.05 1.01 756 1270
#> zBase:Trt1 0.05 0.16 -0.26 0.37 1.01 764 1131
#>
#> Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
#> is a crude measure of effective sample size, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
```
On the top of the output, some general information on the model is
given, such as family, formula, number of iterations and chains. Next,
group-level effects are displayed seperately for each grouping factor in
terms of standard deviations and (in case of more than one group-level
effect per grouping factor; not displayed here) correlations between
group-level effects. On the bottom of the output, population-level
effects (i.e. regression coefficients) are displayed. If incorporated,
autocorrelation effects and family specific parameters (e.g. the
residual standard deviation ‘sigma’ in normal models) are also given.
In general, every parameter is summarized using the mean (‘Estimate’)
and the standard deviation (‘Est.Error’) of the posterior distribution
as well as two-sided 95% credible intervals (‘l-95% CI’ and ‘u-95% CI’)
based on quantiles. We see that the coefficient of `Trt` is negative
with a zero overlapping 95%-CI. This indicates that, on average, the
treatment may reduce seizure counts by some amount but the evidence
based on the data and applied model is not very strong and still
insufficient by standard decision rules. Further, we find little
evidence that the treatment effect varies with the baseline number of
seizures.
The last two values (‘Eff.Sample’ and ‘Rhat’) provide information on how
well the algorithm could estimate the posterior distribution of this
parameter. If ‘Rhat’ is considerably greater than 1, the algorithm has
not yet converged and it is necessary to run more iterations and / or
set stronger priors.
To visually investigate the chains as well as the posterior
distributions, we can use the `plot` method. If we just want to see
results of the regression coefficients of `Trt` and `zBase`, we go for
``` r
plot(fit1, pars = c("Trt", "zBase"))
```
A more detailed investigation can be performed by running
`launch_shinystan(fit1)`. To better understand the relationship of the
predictors with the response, I recommend the `conditional_effects`
method:
``` r
plot(conditional_effects(fit1, effects = "zBase:Trt"))
```
This method uses some prediction functionality behind the scenes, which
can also be called directly. Suppose that we want to predict responses
(i.e. seizure counts) of a person in the treatment group (`Trt = 1`) and
in the control group (`Trt = 0`) with average age and average number of
previous seizures. Than we can use
``` r
newdata <- data.frame(Trt = c(0, 1), zAge = 0, zBase = 0)
predict(fit1, newdata = newdata, re_formula = NA)
#> Estimate Est.Error Q2.5 Q97.5
#> [1,] 5.92175 2.487403 2 11
#> [2,] 4.57475 2.168084 1 9
```
We need to set `re_formula = NA` in order not to condition of the
group-level effects. While the `predict` method returns predictions of
the responses, the `fitted` method returns predictions of the regression
line.
``` r
fitted(fit1, newdata = newdata, re_formula = NA)
#> Estimate Est.Error Q2.5 Q97.5
#> [1,] 5.932893 0.6985409 4.663774 7.447649
#> [2,] 4.545819 0.5293890 3.574081 5.647261
```
Both methods return the same estimate (up to random error), while the
latter has smaller variance, because the uncertainty in the regression
line is smaller than the uncertainty in each response. If we want to
predict values of the original data, we can just leave the `newdata`
argument empty.
Suppose, we want to investigate whether there is overdispersion in the
model, that is residual variation not accounted for by the response
distribution. For this purpose, we include a second group-level
intercept that captures possible overdispersion.
``` r
fit2 <- brm(count ~ zAge + zBase * Trt + (1|patient) + (1|obs),
data = epilepsy, family = poisson())
```
We can then go ahead and compare both models via approximate
leave-one-out (LOO) cross-validation.
``` r
loo(fit1, fit2)
#> Output of model 'fit1':
#>
#> Computed from 4000 by 236 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -672.1 36.5
#> p_loo 94.5 14.2
#> looic 1344.2 73.1
#> ------
#> Monte Carlo SE of elpd_loo is NA.
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. n_eff
#> (-Inf, 0.5] (good) 209 88.6% 368
#> (0.5, 0.7] (ok) 19 8.1% 128
#> (0.7, 1] (bad) 6 2.5% 35
#> (1, Inf) (very bad) 2 0.8% 5
#> See help('pareto-k-diagnostic') for details.
#>
#> Output of model 'fit2':
#>
#> Computed from 4000 by 236 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -594.9 13.8
#> p_loo 107.5 6.9
#> looic 1189.8 27.5
#> ------
#> Monte Carlo SE of elpd_loo is NA.
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. n_eff
#> (-Inf, 0.5] (good) 84 35.6% 675
#> (0.5, 0.7] (ok) 100 42.4% 209
#> (0.7, 1] (bad) 45 19.1% 42
#> (1, Inf) (very bad) 7 3.0% 11
#> See help('pareto-k-diagnostic') for details.
#>
#> Model comparisons:
#> elpd_diff se_diff
#> fit2 0.0 0.0
#> fit1 -77.2 27.1
```
The `loo` output when comparing models is a little verbose. We first see
the individual LOO summaries of the two models and then the comparison
between them. Since higher `elpd` (i.e., expected log posterior density)
values indicate better fit, we see that the model accounting for
overdispersion (i.e., `fit2`) fits substantially better. However, we
also see in the individual LOO outputs that there are several
problematic observations for which the approximations may have not have
been very accurate. To deal with this appropriately, we need to fall
back to other methods such as `reloo` or `kfold` but this requires the
model to be refit several times which takes too long for the purpose of
a quick example. The post-processing methods we have shown above are
just the tip of the iceberg. For a full list of methods to apply on
fitted model objects, type `methods(class = "brmsfit")`.
Citing brms and related software
--------------------------------
Developing and maintaining open source software is an important yet
often underappreciated contribution to scientific progress. Thus,
whenever you are using open source software (or software in general),
please make sure to cite it appropriately so that developers get credit
for their work.
When using brms, please cite one or more of the following publications:
- Bürkner P. C. (2017). brms: An R Package for Bayesian Multilevel
Models using Stan. *Journal of Statistical Software*. 80(1), 1-28.
doi.org/10.18637/jss.v080.i01
- Bürkner P. C. (2018). Advanced Bayesian Multilevel Modeling with the
R Package brms. *The R Journal*. 10(1), 395-411.
doi.org/10.32614/RJ-2018-017
As brms is a high-level interface to Stan, please additionally cite
Stan:
- Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B.,
Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017).
Stan: A probabilistic programming language. *Journal of Statistical
Software*. 76(1). 10.18637/jss.v076.i01
Further, brms relies on several other R packages and, of course, on R
itself. To find out how to cite R and its packages, use the `citation`
function. There are some features of brms which specifically rely on
certain packages. The **rstan** package together with **Rcpp** makes
Stan conveniently accessible in R. Visualizations and
posterior-predictive checks are based on **bayesplot** and **ggplot2**.
Approximate leave-one-out cross-validation using `loo` and related
methods is done via the **loo** package. Marginal likelihood based
methods such as `bayes_factor` are realized by means of the
**bridgesampling** package. Splines specified via the `s` and `t2`
functions rely on **mgcv**. If you use some of these features, please
also consider citing the related packages.
FAQ
---
### How do I install brms?
To install the latest release version from CRAN use
``` r
install.packages("brms")
```
The current developmental version can be downloaded from github via
``` r
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
remotes::install_github("paul-buerkner/brms")
```
Because brms is based on Stan, a C++ compiler is required. The program
Rtools (available on
https://cran.r-project.org/bin/windows/Rtools/)
comes with a C++ compiler for Windows. On Mac, you should install Xcode.
For further instructions on how to get the compilers running, see the
prerequisites section on
https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.
### I am new to brms. Where can I start?
Detailed instructions and case studies are given in the package’s
extensive vignettes. See `vignette(package = "brms")` for an overview.
For documentation on formula syntax, families, and prior distributions
see `help("brm")`.
### Where do I ask questions, propose a new feature, or report a bug?
Questions can be asked on the [Stan
forums](http://discourse.mc-stan.org/) on Discourse. To propose a new
feature or report a bug, please open an issue on
[GitHub](https://github.com/paul-buerkner/brms).
### How can I extract the generated Stan code?
If you have already fitted a model, just apply the `stancode` method on
the fitted model object. If you just want to generate the Stan code
without any model fitting, use the `make_stancode` function.
### Can I avoid compiling models?
When you fit your model for the first time with brms, there is currently
no way to avoid compilation. However, if you have already fitted your
model and want to run it again, for instance with more samples, you can
do this without recompilation by using the `update` method. For more
details see `help("update.brmsfit")`.
### What is the difference between brms and rstanarm?
The rstanarm package is similar to brms in that it also allows to fit
regression models using Stan for the backend estimation. Contrary to
brms, rstanarm comes with precompiled code to save the compilation time
(and the need for a C++ compiler) when fitting a model. However, as brms
generates its Stan code on the fly, it offers much more flexibility in
model specification than rstanarm. Also, multilevel models are currently
fitted a bit more efficiently in brms. For detailed comparisons of brms
with other common R packages implementing multilevel models, see
`vignette("brms_multilevel")` and `vignette("brms_overview")`.
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