Extract variable names from mf_preselect object — preselect

Returns a vector of variable names from an mf_preselect object, based on a cutoff criterion provided.

preselect_vars(x, cutoff = NULL, criterion = NULL)

Arguments

x	Object of class mf_preselect.
cutoff	Numeric. Must be a value between 0 and 1. By default, uses .95 for bootstrapped preselection, and .1 for recursive preselection.
criterion	Character. Which criterion to use. See `Details` for more information. By default, uses 'ci' (confidence interval) for bootstrapped preselection, and 'p' (proportion) for recursive preselection.

Value

Character vector.

Details

For criterion = 'p', the function evaluates the proportion of replications in which a variable achieved a positive (>0) variable importance. For criterion = 'ci', the function evaluates whether the lower bound of a confidence interval of a variable's importance across replications exceeds zero. The width of the confidence interval is determined by cutoff.

For recursive preselection, any variable not included in a final model is assigned zero 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)
preselected <- preselect(formula = yi~ selection + investigator + hand_assess + eye_assess +
                        mage +sex,
          data, study = "sample",
          whichweights = "unif", num.trees = 300,
          replications = 10,
          algorithm = "bootstrap")
preselect_vars(preselected)
}