ml.quantilefind_f: SLMix packageml.quantilefind_f

In alexpkeil1/SLmix: Extensions of Bayesian Kernel Machine Regression and Weighted Quantile Sums for prediction and integration with SuperLearner package

Usage

ml.quantilefind_f(data, v_n, q)

Arguments

data
v_n
q

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (data, v_n, q) 
{
    ncols = dim(data)[2]
    br = matrix(0, q + 1, length(v_n))
    for (i in 1:length(v_n)) {
        dat_num = as.numeric(unlist(data[, v_n[i]]))
        br[, i] = quantile(dat_num, probs = seq(0, 1, by = 1/q), 
            na.rm = TRUE)
        br[1, i] = -1e+32
        br[q + 1, i] = 1e+32
    }
    return(br)
  }

alexpkeil1/SLmix documentation built on Aug. 15, 2020, 9:10 a.m.