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Summarize brma Object

Source: R/summary.R
summary.brma.Rd

summary.brma creates summary tables for a brma object. For RoBMA objects, inclusion summaries are printed before parameter estimates.

Usage

# S3 method for class 'brma'
summary(
  object,
  probs = c(0.025, 0.5, 0.975),
  include_mcmc_diagnostics = TRUE,
  standardized_coefficients = FALSE,
  conditional = FALSE,
  logBF = FALSE,
  BF01 = FALSE,
  ...
)

# S3 method for class 'summary.brma'
print(x, ...)

# S3 method for class 'brma'
print(x, ...)

Arguments

object

a fitted brma object

probs

quantiles of the posterior samples to be displayed. Defaults to c(.025, .50, .975)

include_mcmc_diagnostics

whether to include MCMC diagnostics in the output. Defaults to TRUE.

standardized_coefficients

whether to show standardized meta-regression coefficients. Defaults to FALSE. When set to TRUE, standardized meta-regression coefficients are returned for the intercept and continuous predictors. These coefficients correspond to the standardized scale on which prior distributions are specified by default (i.e., standardize_continuous_predictors = TRUE).

conditional

whether to include conditional estimates for RoBMA product-space objects. Defaults to FALSE.

logBF

whether to show inclusion Bayes factors on the log scale. Defaults to FALSE.

BF01

whether to show inverse inclusion Bayes factors. Defaults to FALSE.

...

additional arguments

x

a summary.brma or fitted brma object.

Value

A list of class summary.brma with model name, optional RoBMA inclusion tables, common estimates, moderator estimates, scale estimates, publication-bias estimates, and optional conditional estimates. The printed form displays the non-empty tables.

See also

brma(), brma.glmm()

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")

  fit <- bPET(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  summary(fit)
}
} # }