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#' predict probabilities
#'
#' predict probabilities
#'
#' @param object object that is used to predict the probabilities
#' @param newdata dataset for which the probabilities must be predicted
#' @param \dots provided for extensibility (S3)
#' @param verbosity The higher this value, the more levels of progress and debug
#' information is displayed (note: in R for Windows, turn off buffered output)
#' @return vector of probabilities of the same length as \code{nrow(newdata)}
#' @author Nick Sabbe \email{nick.sabbe@@ugent.be}
#' @keywords predict probability
#' @export
predictProb<-function(object, newdata, ..., verbosity=0) UseMethod("predictProb")
#' @method predictProb lognetProbabilityReusable
#' @rdname predictProb
#' @inheritParams predictProb
#' @return vector of probabilities of the same length as \code{nrow(newdata)}
#' @author Nick Sabbe \email{nick.sabbe@@ugent.be}
#' @keywords predict probability
#' @examples data(iris)
#' iris.nd2<-numdfr(iris)
#' y2<-rbinom(nrow(iris), 1, 0.5)
#' iris.nic2<-normalImputationConversion(
#' scalingParams=typicalScaleAndCenter(),
#' transformParams=typicalTransformations())
#' iris.cp2<-imputeDs2FitDsProps(iris.nic2,iris.nd2,verbosity=1)
#'
#' iris.cvtd2<-imputeDs2FitDs(iris.cp2,ds=iris.nd2,verbosity=3)
#'
#' lnet<-glmnet(iris.cvtd2, y2, family="binomial")
#' lpw<-lognetProbabilityReusable(lnet, imputeDs2FitDsProperties=iris.cp2, iris.nd2, usecol=5, verbosity=1)
#' predictProb(lpw, iris.nd2[seq(20),], verbosity=10)
#' @export
predictProb.lognetProbabilityReusable<-function(object, newdata, ..., verbosity=0)
{
catwif(verbosity > 10, "The betas are:")
printif(verbosity > 10, object$betas)
mat<-imputeDs2FitDs(object$imputeDs2FitDsProperties,newdata,verbosity=verbosity-1)
mat<-mat[,names(object$betas),drop=FALSE]
catwif(verbosity > 5, "Structure of converted and column-reduced matrix:")
strif(verbosity > 5, mat)
tmpres<-as.vector(mat %*% object$betas)
catwif(verbosity > 5, "Result after matrix multiplication:")
printif(verbosity > 5, tmpres)
linval<-as.vector(tmpres) + object$a0 - object$penalty
# rvnopen<-expit(linval)
# catwif(verbosity > 5, "Linear values without penalty:")
# printif(verbosity > 5, linval)
linval<- linval - object$penalty
catwif(verbosity > 5, "Linear values:")
printif(verbosity > 5, linval)
rv<-expit(linval)
# catwif(verbosity > 5, "Unpenalized probability:")
# printif(verbosity > 5, rvnopen)
catwif(verbosity > 5, "Penalized probability:")
printif(verbosity > 5, rv)
return(rv)
}
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