Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score
Add interaction terms corresponding to ASPRE model
Cost estimating function in ASPRE simulation
Computes ASPRE score
Confidence interval for minimum total cost, when estimated using param...
Confidence interval for optimal holdout size, when estimated using par...
Covariance function for Gaussian process
Measure of error for emulation-based OHS emulation
Expected improvement
Coefficients for imperfect risk score
Generate matrix of random observations
Generate response
Gradient of minimum cost (power law)
Gradient of optimal holdout size (power law)
Logistic
Logit
Make predictions
Train model (wrapper)
Updating function for mean.
Finds best value of n to sample next
Estimate optimal holdout size under semi-parametric assumptions
Estimate optimal holdout size under parametric assumptions
Generate responses
Plot estimated cost function using emulation (semiparametric)
Plot estimated cost function
Power law function
General solver for power law curve
Standard error matrix for learning curve parameters (power law)
Fit power law curve
Updating function for variance.
Sensitivity at theshold quantile 10%
Simulate random dataset similar to ASPRE training data
Split data
Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (to appear in Annals of Applied Statistics) for details of methods.