arguments: Arguments for "palasso"

In palasso: Sparse Regression with Paired Covariates

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)

palasso documentation built on Sept. 30, 2024, 9:30 a.m.