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wN<_HHHH[(ܽ5_ - O Pt$@$@$@$@"@hIHHH|Jݧ& IENDB` brms/man/loo_moment_match.brmsfit.Rd 0000644 0001762 0000144 00000005563 14361545260 017267 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo_moment_match.R
\name{loo_moment_match.brmsfit}
\alias{loo_moment_match.brmsfit}
\alias{loo_moment_match}
\title{Moment matching for efficient approximate leave-one-out cross-validation}
\usage{
\method{loo_moment_match}{brmsfit}(
x,
loo,
k_threshold = 0.7,
newdata = NULL,
resp = NULL,
check = TRUE,
recompile = FALSE,
...
)
}
\arguments{
\item{x}{An object of class \code{brmsfit}.}
\item{loo}{An object of class \code{loo} originally created from \code{x}.}
\item{k_threshold}{The threshold at which Pareto \eqn{k}
estimates are treated as problematic. Defaults to \code{0.7}.
See \code{\link[loo:pareto-k-diagnostic]{pareto_k_ids}}
for more details.}
\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{check}{Logical; If \code{TRUE} (the default), some checks
check are performed if the \code{loo} object was generated
from the \code{brmsfit} object passed to argument \code{fit}.}
\item{recompile}{Logical, indicating whether the Stan model should be
recompiled. This may be necessary if you are running moment matching on
another machine than the one used to fit the model. No recompilation
is done by default.}
\item{...}{Further arguments passed to the underlying methods.
Additional arguments initially passed to \code{\link{loo}},
for example, \code{newdata} or \code{resp} need to be passed
again to \code{loo_moment_match} in order for the latter
to work correctly.}
}
\value{
An updated object of class \code{loo}.
}
\description{
Moment matching for efficient approximate leave-one-out cross-validation
(LOO-CV). See \code{\link[loo:loo_moment_match]{loo_moment_match}}
for more details.
}
\details{
The moment matching algorithm requires draws of all variables
defined in Stan's \code{parameters} block to be saved. Otherwise
\code{loo_moment_match} cannot be computed. Thus, please set
\code{save_pars = save_pars(all = TRUE)} in the call to \code{\link{brm}},
if you are planning to apply \code{loo_moment_match} to your models.
}
\examples{
\dontrun{
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = poisson(),
save_pars = save_pars(all = TRUE))
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
(mmloo1 <- loo_moment_match(fit1, loo = loo1))
}
}
\references{
Paananen, T., Piironen, J., Buerkner, P.-C., Vehtari, A. (2021).
Implicitly Adaptive Importance Sampling. Statistics and Computing.
}
brms/man/brmsformula-helpers.Rd 0000644 0001762 0000144 00000011141 14213413565 016252 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/brmsformula.R
\name{brmsformula-helpers}
\alias{brmsformula-helpers}
\alias{bf-helpers}
\alias{nlf}
\alias{lf}
\alias{set_nl}
\alias{set_rescor}
\alias{acformula}
\alias{set_mecor}
\title{Linear and Non-linear formulas in \pkg{brms}}
\usage{
nlf(formula, ..., flist = NULL, dpar = NULL, resp = NULL, loop = NULL)
lf(
...,
flist = NULL,
dpar = NULL,
resp = NULL,
center = NULL,
cmc = NULL,
sparse = NULL,
decomp = NULL
)
acformula(autocor, resp = NULL)
set_nl(nl = TRUE, dpar = NULL, resp = NULL)
set_rescor(rescor = TRUE)
set_mecor(mecor = TRUE)
}
\arguments{
\item{formula}{Non-linear formula for a distributional parameter.
The name of the distributional parameter can either be specified
on the left-hand side of \code{formula} or via argument \code{dpar}.}
\item{...}{Additional \code{formula} objects to specify predictors of
non-linear and distributional parameters. Formulas can either be named
directly or contain names on their left-hand side. Alternatively,
it is possible to fix parameters to certain values by passing
numbers or character strings in which case arguments have to be named
to provide the parameter names. See 'Details' for more information.}
\item{flist}{Optional list of formulas, which are treated in the
same way as formulas passed via the \code{...} argument.}
\item{dpar}{Optional character string specifying the distributional
parameter to which the formulas passed via \code{...} and
\code{flist} belong.}
\item{resp}{Optional character string specifying the response
variable to which the formulas passed via \code{...} and
\code{flist} belong. Only relevant in multivariate models.}
\item{loop}{Logical; Only used in non-linear models.
Indicates if the computation of the non-linear formula should be
done inside (\code{TRUE}) or outside (\code{FALSE}) a loop
over observations. Defaults to \code{TRUE}.}
\item{center}{Logical; Indicates if the population-level design
matrix should be centered, which usually increases sampling efficiency.
See the 'Details' section for more information.
Defaults to \code{TRUE} for distributional parameters
and to \code{FALSE} for non-linear parameters.}
\item{cmc}{Logical; Indicates whether automatic cell-mean coding
should be enabled when removing the intercept by adding \code{0}
to the right-hand of model formulas. Defaults to \code{TRUE} to
mirror the behavior of standard \R formula parsing.}
\item{sparse}{Logical; indicates whether the population-level design matrices
should be treated as sparse (defaults to \code{FALSE}). For design matrices
with many zeros, this can considerably reduce required memory. Sampling
speed is currently not improved or even slightly decreased.}
\item{decomp}{Optional name of the decomposition used for the
population-level design matrix. Defaults to \code{NULL} that is
no decomposition. Other options currently available are
\code{"QR"} for the QR decomposition that helps in fitting models
with highly correlated predictors.}
\item{autocor}{A one sided formula containing autocorrelation
terms. All none autocorrelation terms in \code{autocor} will
be silently ignored.}
\item{nl}{Logical; Indicates whether \code{formula} should be
treated as specifying a non-linear model. By default, \code{formula}
is treated as an ordinary linear model formula.}
\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.
Only relevant in multivariate models.}
\item{mecor}{Logical; Indicates if correlations between latent variables
defined by \code{\link{me}} terms should be modeled. Defaults to \code{TRUE}.}
}
\value{
For \code{lf} and \code{nlf} a \code{list} that can be
passed to \code{\link[brms:brmsformula]{brmsformula}} or added
to an existing \code{brmsformula} or \code{mvbrmsformula} object.
For \code{set_nl} and \code{set_rescor} a logical value that can be
added to an existing \code{brmsformula} or \code{mvbrmsformula} object.
}
\description{
Helper functions to specify linear and non-linear
formulas for use with \code{\link[brms:brmsformula]{brmsformula}}.
}
\examples{
# add more formulas to the model
bf(y ~ 1) +
nlf(sigma ~ a * exp(b * x)) +
lf(a ~ x, b ~ z + (1|g)) +
gaussian()
# specify 'nl' later on
bf(y ~ a * inv_logit(x * b)) +
lf(a + b ~ z) +
set_nl(TRUE)
# specify a multivariate model
bf(y1 ~ x + (1|g)) +
bf(y2 ~ z) +
set_rescor(TRUE)
# add autocorrelation terms
bf(y ~ x) + acformula(~ arma(p = 1, q = 1) + car(W))
}
\seealso{
\code{\link{brmsformula}}, \code{\link{mvbrmsformula}}
}
brms/man/autocor.brmsfit.Rd 0000644 0001762 0000144 00000001327 14213413565 015407 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/autocor.R
\name{autocor.brmsfit}
\alias{autocor.brmsfit}
\alias{autocor}
\title{(Deprecated) Extract Autocorrelation Objects}
\usage{
\method{autocor}{brmsfit}(object, resp = NULL, ...)
autocor(object, ...)
}
\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{cor_brms} object or a list of such objects for multivariate
models. Not supported for models fitted with brms 2.11.1 or higher.
}
\description{
(Deprecated) Extract Autocorrelation Objects
}
brms/man/Wiener.Rd 0000644 0001762 0000144 00000003716 14275473342 013532 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{Wiener}
\alias{Wiener}
\alias{dwiener}
\alias{rwiener}
\title{The Wiener Diffusion Model Distribution}
\usage{
dwiener(
x,
alpha,
tau,
beta,
delta,
resp = 1,
log = FALSE,
backend = getOption("wiener_backend", "Rwiener")
)
rwiener(
n,
alpha,
tau,
beta,
delta,
types = c("q", "resp"),
backend = getOption("wiener_backend", "Rwiener")
)
}
\arguments{
\item{x}{Vector of quantiles.}
\item{alpha}{Boundary separation parameter.}
\item{tau}{Non-decision time parameter.}
\item{beta}{Bias parameter.}
\item{delta}{Drift rate parameter.}
\item{resp}{Response: \code{"upper"} or \code{"lower"}.
If no character vector, it is coerced to logical
where \code{TRUE} indicates \code{"upper"} and
\code{FALSE} indicates \code{"lower"}.}
\item{log}{Logical; If \code{TRUE}, values are returned on the log scale.}
\item{backend}{Name of the package to use as backend for the computations.
Either \code{"Rwiener"} (the default) or \code{"rtdists"}.
Can be set globally for the current \R session via the
\code{"wiener_backend"} option (see \code{\link{options}}).}
\item{n}{Number of draws to sample from the distribution.}
\item{types}{Which types of responses to return? By default,
return both the response times \code{"q"} and the dichotomous
responses \code{"resp"}. If either \code{"q"} or \code{"resp"},
return only one of the two types.}
}
\description{
Density function and random generation for the Wiener
diffusion model distribution with boundary separation \code{alpha},
non-decision time \code{tau}, bias \code{beta} and
drift rate \code{delta}.
}
\details{
These are wrappers around functions of the \pkg{RWiener} or \pkg{rtdists}
package (depending on the chosen \code{backend}). See
\code{vignette("brms_families")} for details on the parameterization.
}
\seealso{
\code{\link[RWiener:wienerdist]{wienerdist}},
\code{\link[rtdists:Diffusion]{Diffusion}}
}
brms/man/add_ic.Rd 0000644 0001762 0000144 00000002176 14213413565 013474 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{add_loo}
\alias{add_loo}
\alias{add_waic}
\alias{add_ic}
\alias{add_ic.brmsfit}
\alias{add_ic<-}
\title{Add model fit criteria to model objects}
\usage{
add_loo(x, model_name = NULL, ...)
add_waic(x, model_name = NULL, ...)
add_ic(x, ...)
\method{add_ic}{brmsfit}(x, ic = "loo", model_name = NULL, ...)
add_ic(x, ...) <- value
}
\arguments{
\item{x}{An \R object typically of class \code{brmsfit}.}
\item{model_name}{Optional name of the model. If \code{NULL}
(the default) the name is taken from the call to \code{x}.}
\item{...}{Further arguments passed to the underlying
functions computing the model fit criteria.}
\item{ic, value}{Names of model fit criteria
to compute. Currently supported are \code{"loo"},
\code{"waic"}, \code{"kfold"}, \code{"R2"} (R-squared), and
\code{"marglik"} (log marginal likelihood).}
}
\value{
An object of the same class as \code{x}, but
with model fit criteria added for later usage.
Previously computed criterion objects will be overwritten.
}
\description{
Deprecated aliases of \code{\link{add_criterion}}.
}
brms/man/compare_ic.Rd 0000644 0001762 0000144 00000003033 14213413565 014363 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{compare_ic}
\alias{compare_ic}
\title{Compare Information Criteria of Different Models}
\usage{
compare_ic(..., x = NULL, ic = c("loo", "waic", "kfold"))
}
\arguments{
\item{...}{At least two objects returned by
\code{\link{waic}} or \code{\link{loo}}.
Alternatively, \code{brmsfit} objects with information
criteria precomputed via \code{\link{add_ic}}
may be passed, as well.}
\item{x}{A \code{list} containing the same types of objects as
can be passed via \code{...}.}
\item{ic}{The name of the information criterion to be extracted
from \code{brmsfit} objects. Ignored if information
criterion objects are only passed directly.}
}
\value{
An object of class \code{iclist}.
}
\description{
Compare information criteria of different models fitted
with \code{\link{waic}} or \code{\link{loo}}.
Deprecated and will be removed in the future. Please use
\code{\link{loo_compare}} instead.
}
\details{
See \code{\link{loo_compare}} for the recommended way
of comparing models with the \pkg{loo} package.
}
\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
compare_ic(waic1, waic2)
}
}
\seealso{
\code{\link{loo}},
\code{\link{loo_compare}}
\code{\link{add_criterion}}
}
brms/man/loo.brmsfit.Rd 0000644 0001762 0000144 00000010045 14213413565 014521 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loo.R
\name{loo.brmsfit}
\alias{loo.brmsfit}
\alias{loo}
\alias{LOO}
\alias{LOO.brmsfit}
\title{Efficient approximate leave-one-out cross-validation (LOO)}
\usage{
\method{loo}{brmsfit}(
x,
...,
compare = TRUE,
resp = NULL,
pointwise = FALSE,
moment_match = FALSE,
reloo = FALSE,
k_threshold = 0.7,
save_psis = FALSE,
moment_match_args = list(),
reloo_args = list(),
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{prepare_predictions}} 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{moment_match}{Logical; Indicate whether \code{\link{loo_moment_match}}
should be applied on problematic observations. Defaults to \code{FALSE}.
For most models, moment matching will only work if you have set
\code{save_pars = save_pars(all = TRUE)} when fitting the model with
\code{\link{brm}}. See \code{\link{loo_moment_match.brmsfit}} for more
details.}
\item{reloo}{Logical; Indicate whether \code{\link{reloo}}
should be applied on problematic observations. Defaults to \code{FALSE}.}
\item{k_threshold}{The threshold at which pareto \eqn{k}
estimates are treated as problematic. Defaults to \code{0.7}.
Only used if argument \code{reloo} is \code{TRUE}.
See \code{\link[loo:pareto-k-diagnostic]{pareto_k_ids}} for more details.}
\item{save_psis}{Should the \code{"psis"} object created internally be saved
in the returned object? For more details see \code{\link[loo:loo]{loo}}.}
\item{moment_match_args}{Optional \code{list} of additional arguments passed to
\code{\link{loo_moment_match}}.}
\item{reloo_args}{Optional \code{list} of additional arguments passed to
\code{\link{reloo}}.}
\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{
Perform approximate leave-one-out cross-validation based
on the posterior likelihood using the \pkg{loo} package.
For more details see \code{\link[loo:loo]{loo}}.
}
\details{
See \code{\link{loo_compare}} for details on model comparisons.
For \code{brmsfit} objects, \code{LOO} is an alias of \code{loo}.
Use method \code{\link{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)
(loo1 <- loo(fit1))
# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
(loo2 <- loo(fit2))
# compare both models
loo_compare(loo1, loo2)
}
}
\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/car.Rd 0000644 0001762 0000144 00000004562 14213413565 013037 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula-ac.R
\name{car}
\alias{car}
\title{Spatial conditional autoregressive (CAR) structures}
\usage{
car(M, gr = NA, type = "escar")
}
\arguments{
\item{M}{Adjacency matrix of locations. All non-zero entries are treated as
if the two locations are adjacent. If \code{gr} is specified, the row names
of \code{M} have to match the levels of the grouping factor.}
\item{gr}{An optional 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.}
}
\value{
An object of class \code{'car_term'}, which is a list
of arguments to be interpreted by the formula
parsing functions of \pkg{brms}.
}
\description{
Set up an spatial conditional autoregressive (CAR) term in \pkg{brms}. The
function does not evaluate its arguments -- it exists purely to help set up a
model with CAR terms.
}
\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{https://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 + car(W),
data = dat, data2 = list(W = W),
family = binomial())
summary(fit)
}
}
\seealso{
\code{\link{autocor-terms}}
}
brms/man/BetaBinomial.Rd 0000644 0001762 0000144 00000002454 14275436221 014620 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{BetaBinomial}
\alias{BetaBinomial}
\alias{dbeta_binomial}
\alias{pbeta_binomial}
\alias{rbeta_binomial}
\title{The Beta-binomial Distribution}
\usage{
dbeta_binomial(x, size, mu, phi, log = FALSE)
pbeta_binomial(q, size, mu, phi, lower.tail = TRUE, log.p = FALSE)
rbeta_binomial(n, size, mu, phi)
}
\arguments{
\item{x, q}{Vector of quantiles.}
\item{size}{Vector of number of trials (zero or more).}
\item{mu}{Vector of means.}
\item{phi}{Vector of precisions.}
\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 draws to sample from the distribution.}
}
\description{
Cumulative density & mass functions, and random number generation for the
Beta-binomial distribution using the following re-parameterisation of the
\href{https://mc-stan.org/docs/2_29/functions-reference/beta-binomial-distribution.html}{Stan
Beta-binomial definition}:
\itemize{
\item{\code{mu = alpha * beta}} mean probability of trial success.
\item{\code{phi = (1 - mu) * beta}} precision or over-dispersion, component.
}
}
brms/man/StudentT.Rd 0000644 0001762 0000144 00000002511 14403575116 014036 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distributions.R
\name{StudentT}
\alias{StudentT}
\alias{dstudent_t}
\alias{pstudent_t}
\alias{qstudent_t}
\alias{rstudent_t}
\title{The Student-t Distribution}
\usage{
dstudent_t(x, df, mu = 0, sigma = 1, log = FALSE)
pstudent_t(q, df, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE)
qstudent_t(p, df, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE)
rstudent_t(n, df, mu = 0, sigma = 1)
}
\arguments{
\item{x}{Vector of quantiles.}
\item{df}{Vector of degrees of freedom.}
\item{mu}{Vector of location values.}
\item{sigma}{Vector of scale values.}
\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{p}{Vector of probabilities.}
\item{n}{Number of draws to sample 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 00000002505 14213413565 016231 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 00000000766 14160105076 015076 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.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/as.data.frame.brmsfit.Rd 0000644 0001762 0000144 00000003334 14160105076 016333 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior.R
\name{as.data.frame.brmsfit}
\alias{as.data.frame.brmsfit}
\alias{as.matrix.brmsfit}
\alias{as.array.brmsfit}
\title{Extract Posterior Draws}
\usage{
\method{as.data.frame}{brmsfit}(
x,
row.names = NULL,
optional = TRUE,
pars = NA,
variable = NULL,
draw = NULL,
subset = NULL,
...
)
\method{as.matrix}{brmsfit}(x, pars = NA, variable = NULL, draw = NULL, subset = NULL, ...)
\method{as.array}{brmsfit}(x, pars = NA, variable = NULL, draw = NULL, subset = NULL, ...)
}
\arguments{
\item{x}{A \code{brmsfit} object or another \R object for which
the methods are defined.}
\item{row.names, optional}{Unused and only added for consistency with
the \code{\link[base:as.data.frame]{as.data.frame}} generic.}
\item{pars}{Deprecated alias of \code{variable}. For reasons of backwards
compatibility, \code{pars} is interpreted as a vector of regular
expressions by default unless \code{fixed = TRUE} is specified.}
\item{variable}{A character vector providing the variables to extract.
By default, all variables are extracted.}
\item{draw}{The draw indices to be select. Subsetting draw indices will lead
to an automatic merging of chains.}
\item{subset}{Deprecated alias of \code{draw}.}
\item{...}{Further arguments to be passed to the corresponding
\code{\link[brms:draws-brms]{as_draws_*}} methods as well as to
\code{\link[posterior:subset_draws]{subset_draws}}.}
}
\value{
A data.frame, matrix, or array containing the posterior draws.
}
\description{
Extract posterior draws in conventional formats
as data.frames, matrices, or arrays.
}
\seealso{
\code{\link[brms:draws-brms]{as_draws}},
\code{\link[posterior:subset_draws]{subset_draws}}
}
brms/man/posterior_average.brmsfit.Rd 0000644 0001762 0000144 00000006264 14213413565 017460 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 draws of parameters averaged across models}
\usage{
\method{posterior_average}{brmsfit}(
x,
...,
variable = NULL,
pars = NULL,
weights = "stacking",
ndraws = NULL,
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{prepare_predictions}} for further
supported arguments.}
\item{variable}{Names of variables (parameters) for which to average across
models. Only those variables can be averaged that appear in every model.
Defaults to all overlapping variables.}
\item{pars}{Deprecated alias of \code{variable}.}
\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).
For some methods, \code{weights} may also be a numeric vector of
pre-specified weights.}
\item{ndraws}{Total number of posterior draws to use.}
\item{nsamples}{Deprecated alias of \code{ndraws}.}
\item{missing}{An optional numeric value or a named list of numeric values
to use if a model does not contain a variable for which posterior draws
should be averaged. Defaults to \code{NULL}, in which case only those
variables 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 draws.
}
\description{
Extract posterior draws 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 00000000705 14160105076 014712 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 00000007261 14417771074 015741 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 Draws of Residuals/Predictive Errors}
\usage{
\method{residuals}{brmsfit}(
object,
newdata = NULL,
re_formula = NULL,
method = "posterior_predict",
type = c("ordinary", "pearson"),
resp = NULL,
ndraws = NULL,
draw_ids = 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 used to obtain predictions. Can be set to
\code{"posterior_predict"} (the default), \code{"posterior_epred"},
or \code{"posterior_linpred"}. For more details, see the respective
function documentations.}
\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{ndraws}{Positive integer indicating how many posterior draws should
be used. If \code{NULL} (the default) all draws are used. Ignored if
\code{draw_ids} is not \code{NULL}.}
\item{draw_ids}{An integer vector specifying the posterior draws to be used.
If \code{NULL} (the default), all draws 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{prepare_predictions}}
that control several aspects of data validation and prediction.}
}
\value{
An \code{array} of predictive error/residual draws. 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 draws.
}
\description{
This method is an alias of \code{\link{predictive_error.brmsfit}}
with additional arguments for obtaining summaries of the computed draws.
}
\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(Yrep)}, where \eqn{SD(Yrep)} is an estimate of the standard deviation of
\eqn{Yrep}.
}
\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/InvGaussian.Rd 0000644 0001762 0000144 00000002064 14275436221 014516 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 draws to sample 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 00000007103 14213413565 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 draws averaged across models}
\usage{
\method{pp_average}{brmsfit}(
x,
...,
weights = "stacking",
method = "posterior_predict",
ndraws = NULL,
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{prepare_predictions}} 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).
For some methods, \code{weights} may also 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{"posterior_epred"},
\code{"posterior_linpred"} or \code{"predictive_error"}.}
\item{ndraws}{Total number of posterior draws to use.}
\item{nsamples}{Deprecated alias of \code{ndraws}.}
\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 draws 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 14160105076 014106 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 00000003266 14213413565 015315 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 00000003526 14361545260 013213 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 00000005533 14213413565 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,
by = NULL,
cor = TRUE,
id = NA,
cov = NULL,
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{by}{An optional factor matrix, specifying sub-populations of the
groups. It should have as many columns as grouping terms specified in
\code{...}. For each level of the \code{by} variable, a separate
variance-covariance matrix will be fitted. Levels of the grouping factor
must be nested in levels of the \code{by} variable matrix.}
\item{cor}{Logical. If \code{TRUE} (the default), group-level terms will be
modelled as correlated.}
\item{id}{Optional character string. All group-level terms across the model
with the same \code{id} will be modeled as correlated (if \code{cor} is
\code{TRUE}). See \code{\link{brmsformula}} for more details.}
\item{cov}{An optional matrix which is proportional to the withon-group
covariance matrix of the group-level effects. 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. By default, levels
of the same grouping factor are modeled as independent of each other.}
\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/posterior_smooths.brmsfit.Rd 0000644 0001762 0000144 00000004007 14213413565 017533 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/posterior_smooths.R
\name{posterior_smooths.brmsfit}
\alias{posterior_smooths.brmsfit}
\alias{posterior_smooths}
\title{Posterior Predictions of Smooth Terms}
\usage{
\method{posterior_smooths}{brmsfit}(
object,
smooth,
newdata = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
...
)
posterior_smooths(object, ...)
}
\arguments{
\item{object}{An object of class \code{brmsfit}.}
\item{smooth}{Name of a single smooth term for which predictions should
be computed.}
\item{newdata}{An optional \code{data.frame} for which to evaluate
predictions. If \code{NULL} (default), the original data of the model is
used. Only those variables appearing in the chosen \code{smooth} term are
required.}
\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, expected predictions of this parameters are returned.}
\item{nlpar}{Optional name of a predicted non-linear parameter.
If specified, expected predictions of this parameters are returned.}
\item{ndraws}{Positive integer indicating how many posterior draws should
be used. If \code{NULL} (the default) all draws are used. Ignored if
\code{draw_ids} is not \code{NULL}.}
\item{draw_ids}{An integer vector specifying the posterior draws to be used.
If \code{NULL} (the default), all draws are used.}
\item{...}{Currently ignored.}
}
\value{
An S x N matrix, where S is the number of
posterior draws and N is the number of observations.
}
\description{
Compute posterior predictions of smooth \code{s} and \code{t2} terms of
models fitted with \pkg{brms}.
}
\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)
summary(fit)
newdata <- data.frame(x2 = seq(0, 1, 10))
str(posterior_smooths(fit, smooth = "s(x2)", newdata = newdata))
}
}
brms/man/set_prior.Rd 0000644 0001762 0000144 00000046001 14430733376 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{https://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.
Below, we provide details for the individual parameter classes that you can
set priors on. 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}}.
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.
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("
[
](https://mc-stan.org/)
# brms
[](https://github.com/paul-buerkner/brms/actions)
[](https://app.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
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,] 5.90325 2.486249 2 11
#> [2,] 4.59025 2.180262 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,] 5.918847 0.6762827 4.666180 7.308699
#> [2,] 4.554778 0.5144053 3.630642 5.659664
```
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 -671.6 35.8
#> p_loo 94.6 13.6
#> looic 1343.3 71.6
#> ------
#> Monte Carlo SE of elpd_loo is NA.
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. n_eff
#> (-Inf, 0.5] (good) 209 88.6% 546
#> (0.5, 0.7] (ok) 18 7.6% 125
#> (0.7, 1] (bad) 7 3.0% 23
#> (1, Inf) (very bad) 2 0.8% 12
#> See help('pareto-k-diagnostic') for details.
#>
#> Output of model 'fit2':
#>
#> Computed from 4000 by 236 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -596.2 14.1
#> p_loo 108.5 7.3
#> looic 1192.3 28.3
#> ------
#> Monte Carlo SE of elpd_loo is NA.
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. n_eff
#> (-Inf, 0.5] (good) 84 35.6% 755
#> (0.5, 0.7] (ok) 96 40.7% 171
#> (0.7, 1] (bad) 50 21.2% 25
#> (1, Inf) (very bad) 6 2.5% 10
#> See help('pareto-k-diagnostic') for details.
#>
#> Model comparisons:
#> elpd_diff se_diff
#> fit2 0.0 0.0
#> fit1 -75.5 26.3
```
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
- Bürkner P. C. (2021). Bayesian Item Response Modeling in R with brms
and Stan. *Journal of Statistical Software*, 100(5), 1-54.
doi.org/10.18637/jss.v100.i05
As brms is a high-level interface to Stan, please additionally cite Stan
(see also