cv_risk_y_r2: Cross-validated nonparametric R-squared for evaluating...
In benkeser/cvma: Cross-validation-based maximal association

Description Usage Arguments Details Value Examples

In general, the function passed to y_weight_control$cv_risk should expect a list of outcomes and predictions in validation folds, in addition to a list called y_weight that contains the outcome weights (computed in the training sample) corresponding to this validation fold and any other information needed by y_weight_control$cv_risk (e.g., anything needed to compute confidence intervals – in this case the marginal mean of the composite outcome in the training sample). 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_y_r2(input, y_weight_control)

`input`	A list where each entry corresponds to a validation fold. Each entry is a list with entries: Y (matrix of outcomes for this validation fold), pred (matrix of super learner predictions for each outcomes with columns corresponding to different outcomes).
`y_weight_control`	Composite outcome weight control options.

In this case, the confidence intervals are computed on the scale of log(MSE/Var) and back-transformed to the R-squared scale. Here, MSE is the cross-validated mean squared-error of the composite super learner predicting the composite outcome and Var is the cross-validated marginal mean of the composite outcome. The p-value is for the one-sided hypothesis test that cross-validated R-squared equals zero against the alternative that it is greater than zero.

List with named components cv_measure (cross-validated R-squared), ci_low (lower 100(1 - y_weight_control$alpha)% CI), ci_high (upper 100(1 - y_weight_control$alpha)% CI), p_value

# simulate data with proper format
input <- list(list(Y = cbind(rbinom(50,1,0.5), rbinom(50,1,0.5)), 
                   pred = cbind(runif(50,0,1), runif(50,0,1)),
                   y_weight = list(weight = c(0.5, 0.5), ybar=0.5)),
              list(Y = cbind(rbinom(50,1,0.5), rbinom(50,1,0.5)),
                   pred = cbind(runif(50,0,1), runif(50,0,1)),
                   y_weight = list(weight = c(0.25, 0.75), ybar=0.5)))
                   
# linear combination of outcomes
y_weight_control <- list(ensemble_fn = "ensemble_linear")

# get risk   
cv_risk <- cv_risk_y_r2(input, y_weight_control)