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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulate_parameters.R
\name{simulate_parameters}
\alias{simulate_parameters}
\alias{simulate_parameters.default}
\title{Simulate Model Parameters}
\usage{
simulate_parameters(model, ...)
\method{simulate_parameters}{default}(
model,
iterations = 1000,
centrality = "median",
ci = 0.95,
ci_method = "quantile",
test = "p-value",
...
)
}
\arguments{
\item{model}{Statistical model (no Bayesian models).}
\item{...}{Arguments passed to \code{\link[insight:get_varcov]{insight::get_varcov()}}, e.g. to allow simulated
draws to be based on heteroscedasticity consistent variance covariance matrices.}
\item{iterations}{The number of draws to simulate/bootstrap.}
\item{centrality}{The point-estimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: \code{"median"}, \code{"mean"}, \code{"MAP"}
(see \code{\link[bayestestR:map_estimate]{map_estimate()}}), \code{"trimmed"} (which is just \code{mean(x, trim = threshold)}),
\code{"mode"} or \code{"all"}.}
\item{ci}{Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to \code{0.95} (\verb{95\%}).}
\item{ci_method}{The type of index used for Credible Interval. Can be \code{"ETI"}
(default, see \code{\link[bayestestR:eti]{eti()}}), \code{"HDI"} (see \code{\link[bayestestR:hdi]{hdi()}}), \code{"BCI"} (see \code{\link[bayestestR:bci]{bci()}}),
\code{"SPI"} (see \code{\link[bayestestR:spi]{spi()}}), or \code{"SI"} (see \code{\link[bayestestR:si]{si()}}).}
\item{test}{The indices of effect existence to compute. Character (vector) or
list with one or more of these options: \code{"p_direction"} (or \code{"pd"}),
\code{"rope"}, \code{"p_map"}, \code{"p_significance"} (or \code{"ps"}), \code{"p_rope"},
\code{"equivalence_test"} (or \code{"equitest"}), \code{"bayesfactor"} (or \code{"bf"}) or
\code{"all"} to compute all tests. For each "test", the corresponding
\pkg{bayestestR} function is called (e.g. \code{\link[bayestestR:rope]{rope()}} or \code{\link[bayestestR:p_direction]{p_direction()}})
and its results included in the summary output.}
}
\value{
A data frame with simulated parameters.
}
\description{
Compute simulated draws of parameters and their related indices
such as Confidence Intervals (CI) and p-values. Simulating parameter draws
can be seen as a (computationally faster) alternative to bootstrapping.
}
\details{
\subsection{Technical Details}{
\code{simulate_parameters()} is a computationally faster alternative
to \code{bootstrap_parameters()}. Simulated draws for coefficients are based
on a multivariate normal distribution (\code{MASS::mvrnorm()}) with mean
\code{mu = coef(model)} and variance \code{Sigma = vcov(model)}.
}
\subsection{Models with Zero-Inflation Component}{
For models from packages \strong{glmmTMB}, \strong{pscl}, \strong{GLMMadaptive} and
\strong{countreg}, the \code{component} argument can be used to specify
which parameters should be simulated. For all other models, parameters
from the conditional component (fixed effects) are simulated. This may
include smooth terms, but not random effects.
}
}
\note{
There is also a \href{https://easystats.github.io/see/articles/parameters.html}{\code{plot()}-method} implemented in the \href{https://easystats.github.io/see/}{\strong{see}-package}.
}
\examples{
model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
simulate_parameters(model)
\donttest{
if (require("glmmTMB", quietly = TRUE)) {
model <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
simulate_parameters(model, centrality = "mean")
simulate_parameters(model, ci = c(.8, .95), component = "zero_inflated")
}
}
}
\references{
Gelman A, Hill J. Data analysis using regression and
multilevel/hierarchical models. Cambridge; New York: Cambridge University
Press 2007: 140-143
}
\seealso{
\code{\link[=bootstrap_model]{bootstrap_model()}}, \code{\link[=bootstrap_parameters]{bootstrap_parameters()}}, \code{\link[=simulate_model]{simulate_model()}}
}
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