bootstrapValidation.Res: Bootstrap validation of regression models

In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

Bootstrap validation of regression models

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

	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 Nov. 25, 2023, 1:07 a.m.