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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods_metafor.R
\name{model_parameters.rma}
\alias{model_parameters.rma}
\title{Parameters from Meta-Analysis}
\usage{
\method{model_parameters}{rma}(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
include_studies = TRUE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
}
\arguments{
\item{model}{Model object.}
\item{ci}{Confidence Interval (CI) level. Default to \code{0.95} (\verb{95\%}).}
\item{bootstrap}{Should estimates be based on bootstrapped model? If \code{TRUE},
then arguments of \link[=model_parameters.brmsfit]{Bayesian regressions} apply
(see also \code{\link[=bootstrap_parameters]{bootstrap_parameters()}}).}
\item{iterations}{The number of bootstrap replicates. This only apply in the
case of bootstrapped frequentist models.}
\item{standardize}{The method used for standardizing the parameters. Can be
\code{NULL} (default; no standardization), \code{"refit"} (for re-fitting the model
on standardized data) or one of \code{"basic"}, \code{"posthoc"}, \code{"smart"},
\code{"pseudo"}. See 'Details' in \code{\link[=standardize_parameters]{standardize_parameters()}}.
\strong{Importantly}:
\itemize{
\item The \code{"refit"} method does \emph{not} standardize categorical predictors (i.e.
factors), which may be a different behaviour compared to other R packages
(such as \strong{lm.beta}) or other software packages (like SPSS). to mimic
such behaviours, either use \code{standardize="basic"} or standardize the data
with \code{datawizard::standardize(force=TRUE)} \emph{before} fitting the model.
\item For mixed models, when using methods other than \code{"refit"}, only the fixed
effects will be standardized.
\item Robust estimation (i.e., \code{vcov} set to a value other than \code{NULL}) of
standardized parameters only works when \code{standardize="refit"}.
}}
\item{exponentiate}{Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use \code{exponentiate = TRUE} for models
with log-transformed response values. For models with a log-transformed
response variable, when \code{exponentiate = TRUE}, a one-unit increase in the
predictor is associated with multiplying the outcome by that predictor's
coefficient. \strong{Note:} Delta-method standard errors are also computed (by
multiplying the standard errors by the transformed coefficients). This is
to mimic behaviour of other software packages, such as Stata, but these
standard errors poorly estimate uncertainty for the transformed
coefficient. The transformed confidence interval more clearly captures this
uncertainty. For \code{compare_parameters()}, \code{exponentiate = "nongaussian"}
will only exponentiate coefficients from non-Gaussian families.}
\item{include_studies}{Logical, if \code{TRUE} (default), includes parameters for
all studies. Else, only parameters for overall-effects are shown.}
\item{keep}{Character containing a regular expression pattern that
describes the parameters that should be included (for \code{keep}) or excluded
(for \code{drop}) in the returned data frame. \code{keep} may also be a
named list of regular expressions. All non-matching parameters will be
removed from the output. If \code{keep} is a character vector, every parameter
name in the \emph{"Parameter"} column that matches the regular expression in
\code{keep} will be selected from the returned data frame (and vice versa,
all parameter names matching \code{drop} will be excluded). Furthermore, if
\code{keep} has more than one element, these will be merged with an \code{OR}
operator into a regular expression pattern like this: \code{"(one|two|three)"}.
If \code{keep} is a named list of regular expression patterns, the names of the
list-element should equal the column name where selection should be
applied. This is useful for model objects where \code{model_parameters()}
returns multiple columns with parameter components, like in
\code{\link[=model_parameters.lavaan]{model_parameters.lavaan()}}. Note that the regular expression pattern
should match the parameter names as they are stored in the returned data
frame, which can be different from how they are printed. Inspect the
\verb{$Parameter} column of the parameters table to get the exact parameter
names.}
\item{drop}{See \code{keep}.}
\item{verbose}{Toggle warnings and messages.}
\item{...}{Arguments passed to or from other methods. For instance, when
\code{bootstrap = TRUE}, arguments like \code{type} or \code{parallel} are passed down to
\code{bootstrap_model()}.
Further non-documented arguments are:
\itemize{
\item \code{digits}, \code{p_digits}, \code{ci_digits} and \code{footer_digits} to set the number of
digits for the output. \code{groups} can be used to group coefficients. These
arguments will be passed to the print-method, or can directly be used in
\code{print()}, see documentation in \code{\link[=print.parameters_model]{print.parameters_model()}}.
\item If \code{s_value = TRUE}, the p-value will be replaced by the S-value in the
output (cf. \emph{Rafi and Greenland 2020}).
\item \code{pd} adds an additional column with the \emph{probability of direction} (see
\code{\link[bayestestR:p_direction]{bayestestR::p_direction()}} for details). Furthermore, see 'Examples' for
this function.
\item For developers, whose interest mainly is to get a "tidy" data frame of
model summaries, it is recommended to set \code{pretty_names = FALSE} to speed
up computation of the summary table.
}}
}
\value{
A data frame of indices related to the model's parameters.
}
\description{
Extract and compute indices and measures to describe parameters of meta-analysis models.
}
\examples{
library(parameters)
mydat <<- data.frame(
effectsize = c(-0.393, 0.675, 0.282, -1.398),
stderr = c(0.317, 0.317, 0.13, 0.36)
)
if (require("metafor", quietly = TRUE)) {
model <- rma(yi = effectsize, sei = stderr, method = "REML", data = mydat)
model_parameters(model)
}
\donttest{
# with subgroups
if (require("metafor", quietly = TRUE)) {
data(dat.bcg)
dat <- escalc(
measure = "RR",
ai = tpos,
bi = tneg,
ci = cpos,
di = cneg,
data = dat.bcg
)
dat$alloc <- ifelse(dat$alloc == "random", "random", "other")
d <<- dat
model <- rma(yi, vi, mods = ~alloc, data = d, digits = 3, slab = author)
model_parameters(model)
}
if (require("metaBMA", quietly = TRUE)) {
data(towels)
m <- suppressWarnings(meta_random(logOR, SE, study, data = towels))
model_parameters(m)
}
}
}
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