R: Calculate performance measure CI's and p's

nested.cis {glmnetr}

R Documentation

Calculate performance measure CI's and p's

Description

Calculate overall estimates and confidence intervals for performance measures based upon stored cross validation performance measures in a nested.glmnetr() output object.

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

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)