preselect_vars.Rd
Returns a vector of variable names from an mf_preselect object, based on a cutoff criterion provided.
preselect_vars(x, cutoff = NULL, criterion = NULL)
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 |
Character vector.
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.
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) }