arguments {palasso}

Arguments for "palasso"

Description

This page lists the arguments for the (internal) "palasso" function(s).

Arguments

y

response: vector of length n

X

covariates: list of matrices, each with n rows (samples) and p columns (variables)

max

maximum number of non-zero coefficients: positive numeric, or NULL (no sparsity constraint)

...

further arguments for cv.glmnet or glmnet

x

covariates: matrix with n rows (samples) and k * p columns (variables)

args

options for paired lasso: list of arguments (output from .dims and .args)

nfolds

number of folds: positive integer (>= 10 recommended)

foldid

fold identifiers: vector of length n, with entries from 1 to nfolds

cor

correlation coefficients: list of k vectors of length p (one vector for each covariate set with one entry for each covariate)

lambda

lambda sequence: vector of decreasing positive values

family

model family: character "gaussian", "binomial", "poisson", or "cox"

type.measure

... loss function: character "deviance", "mse", "mae", "class", or "auc"

fit

matrix with one row for each sample ("gaussian", "binomial" and "poisson"), or one row for each fold (only "cox"), and one column for each lambda (output from .fit)

cvm

mean cross-validated loss: vector of same length as lambda (output from .loss)