# 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.