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

Description Usage Arguments Details Examples

In general, the function passed to sl_control$cv_risk should expect a list of outcomes and predictions in validation folds. The function should return a list with names cv_measure, ci_low, ci_high, and p_value. The output of this function is returned irrespective of the names of the list; however, the names are necessary for print methods to work properly.

1	cv_risk_sl_auc(input, sl_control)

`input`	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 `learner`).
`sl_control`	List of super learner control options.

In this case, the confidence intervals are computed using the cvAUC::cvAUC.ci function from the cvAUC package. The p-value is for the one-sided hypothesis test that cross-validated AUC equals 0.5 against the alternative that it is greater than 0.5.

# simulate data with proper format
input <- list(list(valid_folds=1, Y = rbinom(50,1,0.5), pred = rbinom(50,1,0.5)),
              list(valid_folds=2, Y = rbinom(50,1,0.5), pred = rbinom(50,1,0.5))) 
     
# alpha value                        
sl_control= list(alpha= 0.05)    

# get risk 
cv_risk <- cv_risk_sl_r2(input, sl_control)