Multivariate Random Forest Meta-Analysis — mars_rf

Convenience wrapper around mars_rf() for multivariate correlation or SMD inputs.

Usage

mars_rf_multivariate(
  data,
  studyID,
  effectID,
  sample_size = NULL,
  effectsize_type,
  variable_names = NULL,
  effectsize_name = NULL,
  varcov_type,
  intercept = FALSE,
  missing = "remove",
  multivariate_covs = NULL,
  num_trees = 500L,
  mtry = NULL,
  minsplit = 10L,
  minbucket = 5L,
  cp = 0.001,
  maxdepth = 30L,
  sample_fraction = 1,
  seed = NULL,
  importance = TRUE,
  ...
)

Arguments

data: Data used for analysis.
studyID: Character string representing the study ID.
effectID: Character string representing the effect size ID for multivariate inputs.
sample_size: Character string representing study sample size.
effectsize_type: Type of effect size being analyzed, such as "cor" or "smd".
variable_names: Vector of variables used for correlation synthesis in multivariate models.
effectsize_name: Character string naming the effect size column.
varcov_type: Type of within-study variance-covariance structure.
intercept: Whether the multivariate design matrix should include an intercept.
missing: Missing-data handling mode. Use "remove", "keep", or "em".
multivariate_covs: One-sided formula specifying moderator covariates for multivariate models.
num_trees: Number of trees in the forest.
mtry: Number of candidate predictors randomly selected for each tree. Defaults to floor(sqrt(p)).
minsplit, minbucket: Tree-growing control parameters.
cp: Minimum split-improvement threshold for growing a new node.
maxdepth: Maximum tree depth.
sample_fraction: Fraction of studies sampled with replacement for each tree.
seed: Optional random seed.
importance: Logical; if TRUE, aggregate split-based variable importance across trees.
...: Not currently used.

Examples

if (FALSE) { # \dontrun{
rf_fit <- mars_rf_multivariate(
  data = becker09,
  studyID = "ID",
  effectID = "numID",
  sample_size = "N",
  effectsize_type = "cor",
  varcov_type = "weighted",
  variable_names = c(
    "Cognitive_Performance",
    "Somatic_Performance",
    "Selfconfidence_Performance",
    "Somatic_Cognitive",
    "Selfconfidence_Cognitive",
    "Selfconfidence_Somatic"
  ),
  multivariate_covs = ~ Team,
  num_trees = 25,
  seed = 123
)
rf_importance(rf_fit)
} # }