Single-split test

Compares models for a continuous response with a cut-off value.

.test(y, cutoff, X, alpha = 1, type.measure = "deviance")

Arguments

y

continuous outcome: vector of length \(n\)

cutoff

cut-off point for dichotomising outcome into classes: meaningful value between min(y) and max(y)

X

features: numeric matrix with \(n\) rows (samples) and \(p\) columns (variables)

alpha

elastic net mixing parameter: numeric between \(0\) (ridge) and \(1\) (lasso)

type.measure

loss function for binary classification: character "deviance", "mse", "mae", or "class" (see cv.glmnet)

Details

Splits samples into \(80\) percent for training and \(20\) percent for testing, calculates squared deviance residuals of logistic and combined regression, conducts the paired one-sided Wilcoxon signed rank test, and returns the \(p\)-value. For the multi-split test, use the median \(p\)-value from \(50\) single-split tests (van de Wiel 2009).

Examples

n <- 100; p <- 200
y <- rnorm(n)
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
cornet:::.test(y=y,cutoff=0,X=X)
#> Multiple minima.

#> $log
#> [1] 0.9983606
#> 
#> $lin
#> [1] 0.9921098
#>