Extract and compute indices and measures to describe parameters of meta-analysis models.

# 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. Important: Categorical predictors (i.e. factors) are never standardized by default, which may be a different behaviour compared to other R packages or other software packages (like SPSS). If standardizing categorical predictors is desired, either use standardize="basic" to mimic behaviour of SPSS or packages such as lm.beta, or standardize the data with effectsize::standardize(force=TRUE) before fitting the model. 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 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 like type or parallel are passed down to bootstrap_model(), and arguments like ci_method are passed down to describe_posterior.

Value

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

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 3.0-2). 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  |       
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)
}
}