cross validation

Description

cross validation function for lars algorithm

Usage

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  MPA.cvlars(X, y, nbFolds = 10,
    index = seq(0, 1, by = 0.01),
    maxSteps = 3 * min(dim(X)),
    eps = .Machine$double.eps^0.5)

Arguments

X

the matrix (of size n*p) of the covariates.

y

a vector of length n with the response.

nbFolds

the number of folds for the cross-validation.

index

Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01).

maxSteps

Maximal number of steps for lars algorithm.

eps

Tolerance of the algorithm.

Value

A list containing

cv

Mean prediction error for each value of index.

cvError

Standard error of cv.

minCv

Minimal cv criterion.

fraction

Value of lambda for which the cv criterion is minimal.

index

Values at which prediction error should be computed. This is the fraction of the saturated |beta|. The default value is seq(0,1,by=0.01).

maxSteps

Maximum number of steps of the lars algorithm.

Author(s)

Quentin Grimonprez

Examples

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dataset=MPA.simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
result=MPA.cvlars(dataset$data,dataset$response,5)