nested.cis: Calculate performance measure "nominal" CI's and p's

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

Calculate performance measure "nominal" CI's and p's

Description

Calculate overall estimates and "nominal" confidence intervals for performance measures based upon stored cross validation performance measures in a nested.glmnetr() output object. The simple standard errors derived here from cross-validation are questionable and the actual coverage probabilities of these CIs and the p's, may be differ meaningfully. See the Vignette references.

Usage

nested.cis(object, type = "devrat", pow = 1, digits = 4, returnd = 0)

Arguments

object	A nested.glmnetr output object.
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". When pow = 2, calculations are made using correlations and the final estimates and confidence intervals are raised to the power of 2. A negative sign before an R-square estimate or confidence limit indicates the estimate or confidence limit was negative before being raised to the power of 2.
digits	digits for printing of z-scores, p-values, etc. with default of 4
returnd	1 to return the deviance ratios in a list, 0 to not return. The deviances are stored in the nested.glmnetr() output object but not the deviance ratios. This function provides a simple mechanism to obtain the cross validated deviance ratios.

Value

A printout to the R console

See Also

nested.compare , 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.cis(fit3)

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