CVscore: Cross-validated score

In multiridge: Fast Cross-Validation for Multi-Penalty Ridge Regression

Cross-validated score

Description

Cross-validated score for given penalty parameters.

Usage

CVscore(penalties, XXblocks, Y, X1 = NULL, pairing = NULL, folds, intercept =
ifelse(is(Y, "Surv"),FALSE, TRUE), frac1 = NULL, score = "loglik", model =
NULL, eps = 1e-07, maxItr = 100, trace = FALSE,   printCV = TRUE, save = FALSE,
parallel = FALSE)

Arguments

penalties	Numeric vector.
XXblocks	List of nxn matrices. Usually output of createXXblocks.
Y	Response vector: numeric, binary, factor or survival.
X1	Matrix. Dimension n x p_0, p_0 < n, representing unpenalized covariates
pairing	Numerical vector of length 3 or NULL when pairs are absent. Represents the indices (in XXblocks) of the two data blocks involved in pairing, plus the index of the paired block.
folds	List of integer vector. Usually output of CVfolds.
intercept	Boolean. Should an intercept be included?
frac1	Scalar. Prior fraction of cases. Only relevant for model=" logistic".
score	Character. See Details.
model	Character. Any of c("linear", "logistic", "cox"). Is inferred from Y when NULL.
eps	Scalar. Numerical bound for IWLS convergence.
maxItr	Integer. Maximum number of iterations used in IWLS.
trace	Boolean. Should the output of the IWLS algorithm be traced?
printCV	Boolean. Should the CV-score be printed on screen?
save	Boolean. If TRUE appends the penalties and resulting CVscore to global variable allscores
parallel	Boolean. Should computation be done in parallel? If TRUE, requires to run setupParallel first.

Details

See Scoring for details on score.

Value

Numeric, cross-validated prediction score for given penalties

See Also

doubleCV for double cross-validation, used for performance evaluation

Examples

data(dataXXmirmeth)
resp <- dataXXmirmeth[[1]]
XXmirmeth <- dataXXmirmeth[[2]]

# Find initial lambdas: fast CV per data block separately.
cvperblock2 <- fastCV2(XXblocks=XXmirmeth,Y=resp,kfold=10,fixedfolds = TRUE)
lambdas <- cvperblock2$lambdas

# Create training-test splits
leftout <- CVfolds(Y=resp,kfold=10,nrepeat=3,fixedfolds = TRUE)
CVscore(penalties=lambdas, XXblocks=XXmirmeth,Y=resp,folds=leftout,score="loglik")

multiridge documentation built on June 13, 2022, 5:07 p.m.