Extract and compute indices and measures to describe parameters of meta-analysis models.
Usage
# S3 method for rma
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
include_studies = TRUE,
verbose = TRUE,
...
)
Arguments
- model
Model object.
- ci
Confidence Interval (CI) level. Default to
0.95
(95%
).- bootstrap
Should estimates be based on bootstrapped model? If
TRUE
, then arguments of Bayesian regressions apply (see alsobootstrap_parameters()
).- iterations
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
- standardize
The method used for standardizing the parameters. Can be
NULL
(default; no standardization),"refit"
(for re-fitting the model on standardized data) or one of"basic"
,"posthoc"
,"smart"
,"pseudo"
. See 'Details' instandardize_parameters()
. Important:The
"refit"
method does not standardized categorical predictors (i.e. factors), which may be a different behaviour compared to other R packages (such as lm.beta) or other software packages (like SPSS). to mimic such behaviours, either usestandardize="basic"
or standardize the data withdatawizard::standardize(force=TRUE)
before fitting the model.For mixed models, when using methods other than
"refit"
, only the fixed effects will be returned.Robust estimation (i.e.,
vcov
set to a value other thanNULL
) of standardized parameters only works whenstandardize="refit"
.
- exponentiate
Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. 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
compare_parameters()
,exponentiate = "nongaussian"
will only exponentiate coefficients from non-Gaussian families.- include_studies
Logical, if
TRUE
(default), includes parameters for all studies. Else, only parameters for overall-effects are shown.- verbose
Toggle warnings and messages.
- ...
Arguments passed to or from other methods. For instance, when
bootstrap = TRUE
, arguments liketype
orparallel
are passed down tobootstrap_model()
, and arguments likeci_method
are passed down tobayestestR::describe_posterior()
.
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)
}
#>
#> Loading the 'metafor' package (version 3.4-0). For an
#> introduction to the package please type: help(metafor)
#>
#> Attaching package: ‘metafor’
#> The following object is masked from ‘package:mclust’:
#>
#> hc
#> Meta-analysis using 'metafor'
#>
#> Parameter | Coefficient | SE | 95% CI | z | p | Weight
#> -------------------------------------------------------------------------
#> Study 1 | -0.39 | 0.32 | [-1.01, 0.23] | -1.24 | 0.215 | 9.95
#> Study 2 | 0.68 | 0.32 | [ 0.05, 1.30] | 2.13 | 0.033 | 9.95
#> Study 3 | 0.28 | 0.13 | [ 0.03, 0.54] | 2.17 | 0.030 | 59.17
#> Study 4 | -1.40 | 0.36 | [-2.10, -0.69] | -3.88 | < .001 | 7.72
#> Overall | -0.18 | 0.44 | [-1.05, 0.68] | -0.42 | 0.676 |
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald z-distribution approximation.
if (FALSE) {
# 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")
model <- rma(yi, vi, mods = ~alloc, data = dat, digits = 3, slab = author)
model_parameters(model)
}
if (require("metaBMA", quietly = TRUE)) {
data(towels)
m <- meta_random(logOR, SE, study, data = towels)
model_parameters(m)
}
}