optim_risk_sl_se: Cross-validated mean squared-error for computing super...

In benkeser/cvma: Cross-validation-based maximal association

Description

In general, the function passed to sl_control$optim_risk should expect a named list of outcomes (Y) and predictions (pred) in validation folds and should return a criteria by which super learner weights may be optimized. The weights are input to the function via sl_weight and are optimized in the sl_control$weight_fn. See Examples section below for an example of the format of the input list used for sl_control$optim_risk functions.

Usage

optim_risk_sl_se(sl_weight, input, sl_control)

Arguments

sl_weight	A numeric vector of super learner weights corresponding to each learner. Typically, this is what is maximized over in sl_control$weight_fn.
input	A list where each entry corresponds to a validation fold. Each entry is a list with entries: Y (univariate outcome for this validation fold), pred (matrix of predictions from learner and columns correspond to different leaners).
sl_control	Super learner control options.

Details

In this case, the function computes cross-validated mean squared-error.

Value

Numeric value of cross-validated mean squared-error

Examples

# simulate data with proper format
# Y is one component of the multivariate outcome
# pred is the predictions made by learners 
input <- list(Y = rnorm(50), pred = cbind(rnorm(50), rnorm(50)))

# made up weights
sl_weight <- c(0.5, 0.5)

# linear ensemble
sl_control <- list(ensemble_fn = "ensemble_linear")

# get risk 
risk <- optim_risk_sl_se(sl_weight, input, sl_control)

benkeser/cvma documentation built on May 5, 2019, 1:37 p.m.