Implements logistic regression with a continuous response.

cornet(y, cutoff, X, alpha = 1, npi = 101, pi = NULL, nsigma = 99,
  sigma = NULL, nfolds = 10, foldid = NULL,
  type.measure = "deviance", ...)

Arguments

y

continuous response: vector of length \(n\)

cutoff

cutoff point for dichotomising response into classes: value between min(y) and max(y)

X

covariates: numeric matrix with \(n\) rows (samples) and \(p\) columns (variables)

alpha

elastic net mixing parameter: numeric between \(0\) (ridge) and \(1\) (lasso)

npi

number of pi values

pi

pi sequence: vector of values in the unit interval; or NULL (default sequence)

nsigma

number of sigma values

sigma

sigma sequence: vector of increasing positive values; or NULL (default sequence)

nfolds

number of folds

foldid

fold identifiers: vector with entries between \(1\) and nfolds; or NULL (balance)

type.measure

loss function for binary classification (linear regression uses the deviance)

...

further arguments passed to glmnet

Details

- INCLUDE note on deviance (not comparable between lin and log models) - alpha: elastic net parameter: numeric between \(0\) (ridge) and \(1\) (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 !!! ###