nested.compare_0_5_1: Compare cross validation fit performances from a...

In glmnetr: Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models

Compare cross validation fit performances from a nested.glmnetr output.

Description

Compare cross-validation model fits in terms of average performances from the nested cross validation fits.

Usage

nested.compare_0_5_1(object, digits = 4, type = "devrat", pow = 1)

Arguments

object	A nested.glmnetr output object.
digits	digits for printing of z-scores, p-values, etc. with default of 4
type	determines what type of nested cross validation performance measures are compared. Possible values are "devrat" to compare the deviance ratios, i.e. the fractional reduction in deviance relative to the null model deviance, "agree" to compare agreement, "lincal" to compare the linear calibration slope coefficients, "intcal" to compare the linear calibration intercept coefficients, from the nested cross validation.
pow	the power to which the average of correlations is to be raised. Only applies to the "gaussian" model. Default is 2 to yield R-square but can be on to show correlations. pow is ignored for the family of "cox" and "binomial".

Value

A printout to the R console.

See Also

nested.cis , summary.nested.glmnetr , nested.glmnetr

Examples

sim.data=glmnetr.simdata(nrows=1000, ncols=100, beta=NULL)
xs=sim.data$xs 
y_=sim.data$yt
event=sim.data$event
# for this example we use a small number for folds_n to shorten run time 
fit3 = nested.glmnetr(xs, NULL, y_, event, family="cox", folds_n=3) 
nested.compare(fit3)

glmnetr documentation built on April 3, 2025, 6:45 p.m.