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SuperLearner (version 2.0-19)

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

Description

summary method for the CV.SuperLearner function

Usage

"summary"(object, obsWeights = NULL, ...)


"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 ...

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

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.

See Also

CV.SuperLearner