cvma: Cross-validation-based maximal association

Description Usage Arguments Details Value Examples

In general, the function passed to sl_control$weight_fn should expect a list of named lists of outcomes (Y), predictions (pred) in validation folds. Typically, this function is used to maximize sl_control$optim_risk_fn over weights. The function should return a named list. One of the names in the list should be weight, which is the optimized weights. Other entries in the return list are passed on to sl_control$cv_risk_fn (e.g., things needed to compute cross-validated measure of association, though none are present for this particular function).

1	weight_sl_01(input, sl_control)

`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 `leaner`s).
`sl_control`	Super learner control options.

In this case, the function selects the single outcome with the lowest value returned by sl_control$optim_risk_fn

Numeric vector giving 0/1 weights for super learner.

#Simulate data and properly format:
input <- list(list(Y = rbinom(50,1,0.5), pred = cbind(rnorm(50), rnorm(50))))

#Linear ensemble
sl_control <- list(ensemble_fn = "ensemble_linear", optim_risk_fn = "optim_risk_sl_auc")

#Get weights to minimize optim_risk:
sl_weight <- weight_sl_01(input, sl_control)