Takes a MetaForest object, and applies different algorithms for variable selection.

preselect(x, replications = 100L, algorithm = "replicate", ...)

## Arguments

x Model to perform variable selection for. Accepts MetaForest objects. Integer. Number of replications to run for variable preselection. Default: 100. Character. Preselection method to apply. Currently, 'replicate', 'recursive', and 'bootstrap' are available. Other arguments to be passed to and from functions.

## Value

An object of class 'mf_preselect'

## Details

Currently, available methods under algorithm are:

replicate

This simply replicates the analysis, which means the forest has access to the full data set, but the trees are grown on different bootstrap samples across replications (thereby varying monte carlo error).

bootstrap

This replicates the analysis on bootstrapped samples, which means each replication has access to a different sub-sample of the full data set. When selecting this algorithm, cases are either bootstrap-sampled by study, or a new study column is generated, and a clustered MetaForest is grown (because some of the rows in the data will be duplicated) , and this would lead to an under-estimation of the OOB error.

recursive

Starting with all moderators, the variable with the most negative variable importance is dropped from the model, and the analysis re-run. This is repeated until only variables with a positive variable importance are left, or no variables are left. The proportion of final models containing each variable reflects its importance.

## Examples

if (FALSE) {
data <- get(data(dat.bourassa1996))
data <- escalc(measure = "OR", ai = lh.le, bi = lh.re, ci = rh.le, di= rh.re,
data = data, add = 1/2, to = "all")
data$mage[is.na(data$mage)] <- median(data$mage, na.rm = TRUE) data[c(5:8)] <- lapply(data[c(5:8)], factor) data$yi <- as.numeric(data\$yi)
mf.model <- MetaForest(formula = yi~ selection + investigator + hand_assess + eye_assess +
mage +sex,
data, study = "sample",
whichweights = "unif", num.trees = 300)
preselect(mf.model,
replications = 10,
algorithm = "bootstrap")
}