SL.wqs: SLMix package

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

Usage

SL.wqs(Y, X, newX, family, obsWeights, id, B = 20, n.quantiles = 4, ...)

Arguments

Y
X
newX
family
obsWeights
id
B
n.quantiles
...

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 (Y, X, newX, family, obsWeights, id, B = 20, n.quantiles = 4, 
    ...) 
{
    if (is.matrix(X)) {
        X = as.data.frame(X)
    }
    if (is.matrix(newX)) {
        newX = as.data.frame(newX)
    }
    if (family$family == "gaussian") {
        wqsfit = my.wqs.est(y.train = Y, x.train = X, z.train = NULL, 
            n.quantiles = n.quantiles, B = B, b1.pos = TRUE)
    }
    if (family$family == "binomial") {
        wqsfit = my.wqs.est(y.train = Y, x.train = X, z.train = NULL, 
            n.quantiles = n.quantiles, B = B, b1.pos = TRUE)
    }
    pred <- predict.my.wqs(wqsfit, newdata = newX)
    class(wqsfit) <- c("SL.wqs")
    out <- list(pred = pred, fit = wqsfit)
    return(out)
  }

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