--- a
+++ b/man/hsstan.Rd
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan.R
+\name{hsstan}
+\alias{hsstan}
+\title{Hierarchical shrinkage models}
+\usage{
+hsstan(
+  x,
+  covs.model,
+  penalized = NULL,
+  family = gaussian,
+  iter = 2000,
+  warmup = floor(iter/2),
+  scale.u = 2,
+  regularized = TRUE,
+  nu = ifelse(regularized, 1, 3),
+  par.ratio = 0.05,
+  global.df = 1,
+  slab.scale = 2,
+  slab.df = 4,
+  qr = TRUE,
+  seed = 123,
+  adapt.delta = NULL,
+  keep.hs.pars = FALSE,
+  ...
+)
+}
+\arguments{
+\item{x}{Data frame containing outcome, covariates and penalized predictors.
+Continuous predictors and outcome variable should be standardized
+before fitting the models as priors assume them to have mean zero and
+unit variance.}
+
+\item{covs.model}{Formula containing the unpenalized covariates.}
+
+\item{penalized}{Names of the variables to be used as penalized predictors.
+Any variable that is already part of the \code{covs.model} formula will be
+penalized. If \code{NULL} or an empty vector, a model with only unpenalized
+covariates is fitted.}
+
+\item{family}{Type of model fitted: either \code{gaussian()} for linear regression
+(default) or \code{binomial()} for logistic regression.}
+
+\item{iter}{Total number of iterations in each chain, including warmup
+(2000 by default).}
+
+\item{warmup}{Number of warmup iterations per chain (by default, half the
+total number of iterations).}
+
+\item{scale.u}{Prior scale (standard deviation) for the unpenalized
+covariates.}
+
+\item{regularized}{If \code{TRUE} (default), the regularized horseshoe prior
+is used as opposed to the original horseshoe prior.}
+
+\item{nu}{Number of degrees of freedom of the half-Student-t prior on the
+local shrinkage parameters (by default, 1 if \code{regularized=TRUE}
+and 3 otherwise).}
+
+\item{par.ratio}{Expected ratio of non-zero to zero coefficients (ignored
+if \code{regularized=FALSE}). The scale of the global shrinkage parameter
+corresponds to \code{par.ratio} divided by the square root of the number of
+observations; for linear regression only, it's further multiplied by
+the residual standard deviation \code{sigma}.}
+
+\item{global.df}{Number of degrees of freedom for the global shrinkage
+parameter (ignored if \code{regularized=FALSE}). Larger values induce more
+shrinkage.}
+
+\item{slab.scale}{Scale of the regularization parameter (ignored if
+\code{regularized=FALSE}).}
+
+\item{slab.df}{Number of degrees of freedom of the regularization parameter
+(ignored if \code{regularized=FALSE}).}
+
+\item{qr}{Whether the thin QR decomposition should be used to decorrelate the
+predictors (\code{TRUE} by default). This is silently set to \code{FALSE} if
+there are more predictors than observations.}
+
+\item{seed}{Optional integer defining the seed for the pseudo-random number
+generator.}
+
+\item{adapt.delta}{Target average proposal acceptance probability for
+adaptation, a value between 0.8 and 1 (excluded). If unspecified,
+it's set to 0.99 for hierarchical shrinkage models and to 0.95 for
+base models.}
+
+\item{keep.hs.pars}{Whether the parameters for the horseshoe prior should be
+kept in the \code{stanfit} object returned (\code{FALSE} by default).}
+
+\item{...}{Further arguments passed to \code{\link[rstan:stanmodel-method-sampling]{rstan::sampling()}},
+such as \code{chains} (4 by default), \code{cores} (the value of
+\code{options("mc.cores")} by default), \code{refresh} (\code{iter / 10} by default).}
+}
+\value{
+An object of class \code{hsstan} containing the following fields:
+\item{stanfit}{an object of class \code{stanfit} containing the output
+produced by Stan, including posterior samples and diagnostic summaries.
+It can be manipulated using methods from the \strong{rstan} package.}
+\item{betas}{posterior means of the unpenalized and penalized regression
+parameters.}
+\item{call}{the matched call.}
+\item{data}{the dataset used in fitting the model.}
+\item{model.terms}{a list of names for the outcome variable, the unpenalized
+covariates and the penalized predictors.}
+\item{family}{the \code{family} object used.}
+\item{hsstan.settings}{the optional settings used in the model.}
+}
+\description{
+Run the No-U-Turn Sampler (NUTS) as implemented in Stan to fit a hierarchical
+shrinkage model.
+}
+\examples{
+\dontshow{oldopts <- options(mc.cores=2)}
+data(diabetes)
+
+# non-default settings for speed of the example
+df <- diabetes[1:50, ]
+hs.biom <- hsstan(df, Y ~ age + sex, penalized=colnames(df)[5:10],
+                  chains=2, iter=250)
+\dontshow{options(oldopts)}
+
+}
+\seealso{
+\code{\link[=kfold]{kfold()}} for cross-validating a fitted object.
+}