parameters (version 0.10.1)

model_parameters.rma: Parameters from Meta-Analysis

Description

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 also bootstrap_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 "refit", "posthoc", "smart", "basic", "pseudo" or NULL (default) for no standardization. See 'Details' in standardize_parameters. Note that robust estimation (i.e. robust=TRUE) of standardized parameters only works when standardize="refit".

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.

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 like ci_method are passed down to describe_posterior.

Value

A data frame of indices related to the model's parameters.

Examples

# NOT RUN {
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")) {
  model <- rma(yi = effectsize, sei = stderr, method = "REML", data = mydat)
  model_parameters(model)
}
# }
# NOT RUN {
# with subgroups
if (require("metafor")) {
  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")) {
  data(towels)
  m <- meta_random(logOR, SE, study, data = towels)
  model_parameters(m)
}
# }
# NOT RUN {
# }