Method to estimate the coefficients for the super learner
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)
file |
A connection, or a character string naming a file to print to. Passed to |
optim_method |
Passed to the |
nlopt_method |
Either |
bounds |
Bounds for parameter estimates |
normalize |
Logical. Should the parameters be normalized to sum up to 1 |
... |
Additional arguments passed to |
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
.
A list containing 3 elements:
require |
A character vector listing any required packages. Use |
computeCoef |
A function. The arguments are: |
computePred |
A function. The arguments are: |
Eric C Polley epolley@uchicago.edu
write.method.template(file = '')
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