write.method.template: Method to estimate the coefficients for the super learner
In SuperLearner: Super Learner Prediction

Description Usage Arguments Details Value Author(s) See Also Examples

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 `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`.

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 `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.

Eric C Polley epolley@uchicago.edu

SuperLearner

1	write.method.template(file = '')

Loading required package: nnls
Super Learner
Version: 2.0-24
Package created on 2018-08-10

method.template <- function() {
  out <- list(
    # require allows you to pass a character vector with required packages
    # use NULL if no required packages
    require = NULL,

    # computeCoef is a function that returns a list with two elements:
    # 1) coef: the weights (coefficients) for each algorithm
    # 2) cvRisk: the V-fold CV risk for each algorithm
    computeCoef = function(Z, Y, libraryNames, obsWeights, control, verbose, ...) {
      cvRisk <- numeric()
      coef <- numeric()
      out <- list(cvRisk = cvRisk, coef = coef)
      return(out)
    },

    # computePred is a function that takes the weights and the predicted values
    # from each algorithm in the library and combines them based on the model to
    # output the super learner predicted values
    computePred = function(predY, coef, control, ...) {
      out <- crossprod(t(predY), coef)
      return(out)
    }
    )
    invisible(out)
  }