bootstrapValidation.Res: Bootstrap validation of regression models In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

Description

This function bootstraps the model n times to estimate for each variable the empirical bootstrapped distribution of model coefficients, and net residual improvement (NeRI). At each bootstrap the non-observed data is predicted by the trained model, and statistics of the test prediction are stores and reported.

Usage

 1 2 3 4 5 6 7 8 bootstrapValidation_Res(fraction = 1, loops = 200, model.formula, Outcome, data, type = c("LM", "LOGIT", "COX"), plots = FALSE, bestmodel.formula=NULL)

Arguments

 fraction The fraction of data (sampled with replacement) to be used as train loops The number of bootstrap loops model.formula An object of class formula with the formula to be used Outcome The name of the column in data that stores the variable to be predicted by the model data A data frame where all variables are stored in different columns type Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX") plots Logical. If TRUE, density distribution plots are displayed bestmodel.formula An object of class formula with the best formula to be compared

Details

The bootstrap validation will estimate the confidence interval of the model coefficients and the NeRI. It will also compute the train and blind test root-mean-square error (RMSE), as well as the distribution of the NeRI p-values.

Value

 data The data frame used to bootstrap and validate the model outcome A vector with the predictions made by the model boot.model An object of class lm, glm, or coxph containing a model whose coefficients are the median of the coefficients of the bootstrapped models NeRIs A matrix with the NeRI for each model term, estimated using the bootstrap test sets tStudent.pvalues A matrix with the t-test p-value of the NeRI for each model term, estimated using the bootstrap train sets wilcox.pvalues A matrix with the Wilcoxon rank-sum test p-value of the NeRI for each model term, estimated using the bootstrap train sets bin.pvalues A matrix with the binomial test p-value of the NeRI for each model term, estimated using the bootstrap train sets F.pvalues A matrix with the F-test p-value of the NeRI for each model term, estimated using the bootstrap train sets test.tStudent.pvalues A matrix with the t-test p-value of the NeRI for each model term, estimated using the bootstrap test sets test.wilcox.pvalues A matrix with the Wilcoxon rank-sum test p-value of the NeRI for each model term, estimated using the bootstrap test sets test.bin.pvalues A matrix with the binomial test p-value of the NeRI for each model term, estimated using the bootstrap test sets test.F.pvalues A matrix with the F-test p-value of the NeRI for each model term, estimated using the bootstrap test sets testPrediction A vector that contains all the individual predictions used to validate the model in the bootstrap test sets testOutcome A vector that contains all the individual outcomes used to validate the model in the bootstrap test sets testResiduals A vector that contains all the residuals used to validate the model in the bootstrap test sets trainPrediction A vector that contains all the individual predictions used to validate the model in the bootstrap train sets trainOutcome A vector that contains all the individual outcomes used to validate the model in the bootstrap train sets trainResiduals A vector that contains all the residuals used to validate the model in the bootstrap train sets testRMSE The global RMSE, estimated using the bootstrap test sets trainRMSE The global RMSE, estimated using the bootstrap train sets trainSampleRMSE A vector with the RMSEs in the bootstrap train sets testSampledRMSE A vector with the RMSEs in the bootstrap test sets

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

See Also

bootstrapValidation_Bin, plot.bootstrapValidation_Res

FRESA.CAD documentation built on Jan. 13, 2021, 3:39 p.m.