get.array.cv.fold.data: Splits array testing data into crossvalidation data sets.

In gregorkb/aenetgt: Adaptive Elastic Net for Group Testing Data

Splits array testing data into crossvalidation data sets.

Description

Splits array testing data into crossvalidation data sets.

Usage

get.array.cv.fold.data(X, Y, Z, K)

Arguments

X	Design matrix with first column a column of 1s.
Y	Group testing output from one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
Z	Group testing output from one of the functions individual.assay.gen, masterpool.assay.gen, dorfman.assay.gen, or array.assay.gen.
K	The number of crossvalidation folds; K may be changed internally if the number of arrays is not divisible by K.

Value

List of length K of the crossvalidation training and testing data sets.

Examples

# generate individual covariate values and disease statuses
N <- 160
data <- model1(N)
X <- data$X
Y.true <- data$Yi
Se <- c(.92,.95) # set row/col and individual assay sensitivity
Sp <- c(.97,.98) # set row/col and individual assay specificity
cj <- 4 # set size of master pools
# subject individuals to array testing
assay.data <- array.assay.gen(Y.true,Se,Sp,cj)
Z <- assay.data$Z
Y <- assay.data$Y
array.cv.fold.data <- get.array.cv.fold.data(X,Y,Z,K=5)

gregorkb/aenetgt documentation built on Oct. 12, 2022, 11:51 a.m.