cv_validate: Calculate Cross-validated prediction performance metrics

In harrisonreeder/SemiCompRisksPen: Penalized Models for Semi-Competing Risks

Calculate Cross-validated prediction performance metrics

Description

This function calculates cross-validated prediction performance metrics

Usage

cv_validate(
  cv_predict_out_list,
  metrics = c("brier", "auc", "hum", "ccp", "pdi"),
  verbose = 0
)

Arguments

cv_predict_out_list	is an object output from cv_predict function
metrics	is a vector of strings naming what metrics to compute. Options are "brier", "auc", "hum", "ccp",and "pdi".
verbose	Numeric indicating the amount of intermediate information that should be printed during the cross-validation process. Larger numbers correspond to more printed information.

Value

if Xmat has only one row, and t_cutoff is a scalar, then returns a 4 element row matrix of probabilities. If Xmat has n rows, then returns an n by 4 matrix of probabilities. If Xmat has n rows and t_cutoff is a vector of length s, then returns an s by 4 by n array.

harrisonreeder/SemiCompRisksPen documentation built on Dec. 13, 2024, 5:30 a.m.