SL.bkmr: SLMix package

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

Usage

SL.bkmr(Y, X, newX, family, obsWeights, id, ...)

Arguments

Y
X
newX
family
obsWeights
id
...

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, ...) 
{
    if (is.matrix(X)) {
        X = as.data.frame(X)
    }
    if (is.matrix(newX)) {
        newX = as.data.frame(newX)
    }
    if (family$family == "gaussian") {
        fit = kmbayes(y = Y, Z = X, iter = 1000, family = "gaussian", 
            verbose = FALSE)
    }
    if (family$family == "binomial") {
        fit = kmbayes(y = Y, Z = X, iter = 1000, family = "binomial", 
            verbose = FALSE)
    }
    pMat <- SamplePred(fit, Znew = newX, Xnew = cbind(0), type = "response")
    pred <- as.numeric(apply(pMat, 2, mean))
    class(fit) <- c("SL.bkmr")
    out <- list(pred = pred, fit = fit)
    return(out)
  }

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