These functions contain the information on the loss function and the model to combine algorithms
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)
A list containing 3 elements:
A character vector listing any required packages. Use NULL
if no additional packages are required
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
.
A function. The arguments are: predY
, coef
, control
. The value is a numeric vector with the super learner predicted values.
A connection, or a character string naming a file to print to. Passed to cat
.
Passed to the optim
call method. See optim
for details.
Either optim_method
or nlopt_method
must be provided, the other must be NULL
Bounds for parameter estimates
Logical. Should the parameters be normalized to sum up to 1
Additional arguments passed to cat
.
Eric C Polley Polley.Eric@mayo.edu
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
.
SuperLearner
write.method.template(file = '')
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