cv_lasso: Fit a model using a design matrix with cross validation

In iilasso: Independently Interpretable Lasso

Description

Fit a model using a design matrix with cross validation

Usage

cv_lasso(X, y, nfolds = 10, lambda.min.ratio = 1e-04, nlambda = 100,
  lambda = NULL, foldid = NULL, unit = "sample", seed, cl, ...)

Arguments

X	matrix of explanatory variables
y	vector of objective variable
nfolds	the number of folds (ignored if foldid is specified)
lambda.min.ratio	ratio of max lambda and min lambda (ignored if lambda is specified)
nlambda	the number of lambda (ignored if lambda is specified)
lambda	lambda sequence
foldid	vector indicating id of fold for each sample
unit	unit for cross validation error: "sample" (default) or "fold"
seed	random seed of cross validation
cl	(not yet implemented)
...	parameters of lasso function

Value

lasso model

fit	lasso model with hole data
lambda.min	lambda with minimum cross validation error
lambda.min.index	index of lambda.min
lambda.1se	largest lambda such that error is within 1 standard error of the minimum
lambda.1se.index	index of lambda.1se
delta	delta defined above
foldid	fold id
cve	cross validation error
cvse	cross validation standard error
cvup	cross validation error + standard error
cvlo	cross validation error - standard error
pe	prediction error (for family="binomial")

Examples

X <- matrix(c(1,2,3,5,4,7,6,8,9,10), nrow=5, ncol=2)
b <- matrix(c(-1,1), nrow=2, ncol=1)
e <- matrix(c(0,-0.1,0.1,-0.1,0.1), nrow=5, ncol=1)
y <- as.numeric(X %*% b + e)
cv_fit <- cv_lasso(X, y, nfolds=5)
fit <- cv_fit$fit
pr <- predict_lasso(fit, X, cv_fit$lambda.min)
plot_cv_lasso(cv_fit)

iilasso documentation built on May 2, 2019, 10:14 a.m.