write.method.template: Method to estimate the coefficients for the super learner

Description

These functions contain the information on the loss function and the model to combine algorithms

Usage

write.method.template(file = "", ...)
## a few built in options:
method.NNLS()
method.NNLS2()
method.NNloglik()
method.CC_LS()
method.CC_nloglik()
method.AUC(nlopt_method=NULL, optim_method="L-BFGS-B", bounds=c(0, Inf), normalize=TRUE)

Arguments

file

A connection, or a character string naming a file to print to. Passed to cat.

optim_method

Passed to the optim call method. See optim for details.

nlopt_method

Either optim_method or nlopt_method must be provided, the other must be NULL

bounds

Bounds for parameter estimates

normalize

Logical. Should the parameters be normalized to sum up to 1

…

Additional arguments passed to cat.

Value

A list containing 3 elements:

require

A character vector listing any required packages. Use NULL if no additional packages are required

computeCoef

A function. The arguments are: Z, Y, libraryNames, obsWeights, control, verbose. The value is a list with two items: cvRisk and coef. This function computes the coefficients of the super learner. As the super learner minimizes the cross-validated risk, the loss function information is contained in this function as well as the model to combine the algorithms in SL.library.

computePred

A function. The arguments are: predY, coef, control. The value is a numeric vector with the super learner predicted values.

Details

A SuperLearner method must be a list (or a function to create a list) with exactly 3 elements. The 3 elements must be named require, computeCoef and computePred.

Examples

Run this code

# NOT RUN {
write.method.template(file = '')
# }