anova.ma_psychmeta {psychmeta}

Wald-type tests for moderators in psychmeta meta-analyses

Description

This function computes Wald-type pairwise comparisons for each level of categorical moderators for an ma_psychmeta object, as well as an ombnibus one-way ANOVA test (equal variance not assumed).

Currently, samples across moderator levels are assumed to be independent.

Usage

## S3 method for class 'ma_psychmeta'
anova(
  object,
  ...,
  analyses = "all",
  moderators = NULL,
  L = NULL,
  ma_obj2 = NULL,
  ma_method = c("bb", "ic", "ad"),
  correction_type = c("ts", "vgx", "vgy"),
  conf_level = NULL
)

Arguments

object

A psychmeta meta-analysis object.

...

Additional arguments.

analyses

Which analyses to test moderators for? Can be either "all" to test moderators for all meta-analyses in the object (default) or a list containing one or more of the arguments construct, construct_pair, pair_id, k_min, and N_min. See filter_ma() for details. Note that analysis_id should not be used. If k_min is not supplied, it is set to 2.

moderators

A character vector of moderators to test. If NULL, all categorical moderators are tested.

L

A named list with with elements specifying set of linear contrasts for each variable in moderators. (Not yet implemented.)

ma_obj2

A second psychmeta meta-analysis object to compare to object (Not yet implemented.)

ma_method

Meta-analytic methods to be included. Valid options are: "bb", "ic", and "ad"

correction_type

Types of meta-analytic corrections to be included. Valid options are: "ts", "vgx", and "vgy"

conf_level

Confidence level to define the width of confidence intervals (defaults to value set when object was fit)

Value

An object of class anova.ma_psychmeta. A tibble with a row for each construct pair in object and a column for each moderator tested. Cells lists of contrasts tested.

Note

Currently, only simple (single) categorical moderators (one-way ANOVA) are supported.

Examples

ma_obj <- ma_r(rxyi, n, construct_x = x_name, construct_y = y_name,
moderators = moderator, data = data_r_meas_multi)

anova(ma_obj)