Parameters from Meta-Analysis
Extract and compute indices and measures to describe parameters of meta-analysis models.
## S3 method for class 'rma' model_parameters( model, ci = 0.95, bootstrap = FALSE, iterations = 1000, standardize = NULL, exponentiate = FALSE, include_studies = TRUE, verbose = TRUE, ... )
model |
Model object. |
ci |
Confidence Interval (CI) level. Default to 0.95 (95%). |
bootstrap |
Should estimates be based on bootstrapped model? If
|
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
|
exponentiate |
Logical, indicating whether or not to exponentiate the
the coefficients (and related confidence intervals). This is typical for,
say, logistic regressions, or more generally speaking: for models with log
or logit link. Note: standard errors are also transformed (by
multiplying the standard errors with the exponentiated coefficients), to
mimic behaviour of other software packages, such as Stata. For
|
include_studies |
Logical, if |
verbose |
Toggle warnings and messages. |
... |
Arguments passed to or from other methods. For instance, when
|
A data frame of indices related to the model's parameters.
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)
}
## Not run:
# 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)
}
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