summary.CV.SuperLearner: Summary Function for Cross-Validated Super Learner

View source: R/summary.CV.SuperLearner.R

summary.CV.SuperLearnerR Documentation

Summary Function for Cross-Validated Super Learner

Description

summary method for the CV.SuperLearner function

Usage


## S3 method for class 'CV.SuperLearner'
summary(object, obsWeights = NULL, ...)

## S3 method for class 'summary.CV.SuperLearner'
print(x, digits, ...)

Arguments

object

An object of class "CV.SuperLearner", the result of a call to CV.SuperLearner.

x

An object of class "summary.CV.SuperLearner", the result of a call to summary.CV.SuperLearner.

obsWeights

Optional vector for observation weights.

digits

The number of significant digits to use when printing.

...

additional arguments ...

Details

Summary method for CV.SuperLearner. Calculates the V-fold cross-validated estimate of either the mean squared error or the -2*log(L) depending on the loss function used.

Value

summary.CV.SuperLearner returns a list with components

call

The function call from CV.SuperLearner

method

Describes the loss function used. Currently either least squares of negative log Likelihood.

V

Number of folds

Risk.SL

Risk estimate for the super learner

Risk.dSL

Risk estimate for the discrete super learner (the cross-validation selector)

Risk.library

A matrix with the risk estimates for each algorithm in the library

Table

A table with the mean risk estimate and standard deviation across the folds for the super learner and all algorithms in the library

Author(s)

Eric C Polley eric.polley@nih.gov

See Also

CV.SuperLearner


SuperLearner documentation built on May 29, 2024, 5:25 a.m.