iilasso (version 0.0.2)

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

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

Run this code
# NOT RUN {
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)
# }

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