optim_risk_sl_auc: Cross-validated area under receiver operating characteristic...
In benkeser/cvma: Cross-validation-based maximal association

Description Usage Arguments Details Value Examples

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.

1	optim_risk_sl_auc(sl_weight, input, sl_control)

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

In this case, the function computes cross-validated area under the receiver operating characteristics curve (AUC) using cvAUC. scaled to be between 0 and 1. The option trim must be a value greater than zero in order that the loss is bounded. The bounds on the outcome are set via l and u.

Numeric value of cross-validated AUC.

#Simulate data with proper format:
#Y is one component of the multivariate outcome, pred is the predictions made by 2 learners
input <- list(Y = rbinom(50, 1, 0.5), pred = cbind(runif(50,0,1), runif(50,0,1)))

#Example weights:
sl_weight <- c(0.5, 0.5)

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

#Risk: 
risk <- optim_risk_sl_auc(sl_weight, input, sl_control)