% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functions.R \name{cornet} \alias{cornet} \title{Logistic regression with a continuous response} \usage{ cornet(y, cutoff, X, alpha = 1, npi = 101, pi = NULL, nsigma = 99, sigma = NULL, nfolds = 10, foldid = NULL, type.measure = "deviance", ...) } \arguments{ \item{y}{continuous response\strong{:} vector of length \eqn{n}} \item{cutoff}{cutoff point for dichotomising response into classes\strong{:} value between \code{min(y)} and \code{max(y)}} \item{X}{covariates\strong{:} numeric matrix with \eqn{n} rows (samples) and \eqn{p} columns (variables)} \item{alpha}{elastic net mixing parameter\strong{:} numeric between \eqn{0} (ridge) and \eqn{1} (lasso)} \item{npi}{number of \code{pi} values} \item{pi}{pi sequence\strong{:} vector of values in the unit interval; or \code{NULL} (default sequence)} \item{nsigma}{number of \code{sigma} values} \item{sigma}{sigma sequence\strong{:} vector of increasing positive values; or \code{NULL} (default sequence)} \item{nfolds}{number of folds} \item{foldid}{fold identifiers\strong{:} vector with entries between \eqn{1} and \code{nfolds}; or \code{NULL} (balance)} \item{type.measure}{loss function for binary classification (linear regression uses the deviance)} \item{...}{further arguments passed to \code{\link[glmnet]{glmnet}}} } \description{ Implements logistic regression with a continuous response. } \details{ - INCLUDE note on deviance (not comparable between lin and log models) - alpha: elastic net parameter\strong{:} numeric between \eqn{0} (ridge) and \eqn{1} (lasso) - do not use "family" } \examples{ n <- 100; p <- 200 y <- rnorm(n) X <- matrix(rnorm(n*p),nrow=n,ncol=p) net <- cornet(y=y,cutoff=0,X=X) ### Add ... to all glmnet::glmnet calls !!! ### }