SuperLearner (version 2.0-9)

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

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