bootstrapVarElimination.Bin: IDI/NRI-based backwards variable elimination with...

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

IDI/NRI-based backwards variable elimination with bootstrapping

Description

This function removes model terms that do not improve the bootstrapped integrated discrimination improvement (IDI) or net reclassification improvement (NRI) significantly.

Usage

	bootstrapVarElimination_Bin(object,
	                        pvalue = 0.05,
	                        Outcome = "Class",
	                        data,
	                        startOffset = 0, 
	                        type = c("LOGIT", "LM", "COX"),
	                        selectionType = c("zIDI", "zNRI"),
	                        loops = 64,
	                        print=TRUE,
	                        plots=TRUE
	                        )

Arguments

object	An object of class lm, glm, or coxph containing the model to be analyzed
pvalue	The maximum p-value, associated to either IDI or NRI, allowed for a term in the model
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
startOffset	Only terms whose position in the model is larger than the startOffset are candidates to be removed
type	Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")
selectionType	The type of index to be evaluated by the improveProb function (Hmisc package): z-score of IDI or of NRI
loops	The number of bootstrap loops
print	Logical. If TRUE, information will be displayed
plots	Logical. If TRUE, plots are displayed

Details

For each model term x_i, the IDI or NRI is computed for the Full model and the reduced model( where the term x_i removed). The term whose removal results in the smallest drop in bootstrapped improvement is selected. The hypothesis: the term adds classification improvement is tested by checking the p value of average improvement. If p(IDI or NRI)>pvalue, then the term is removed. In other words, only model terms that significantly aid in subject classification are kept. The procedure is repeated until no term fulfils the removal criterion.

Value

back.model	An object of the same class as object containing the reduced model
loops	The number of loops it took for the model to stabilize
reclas.info	A list with the NRI and IDI statistics of the reduced model, as given by the getVar.Bin function
bootCV	An object of class bootstrapValidation_Bin containing the results of the bootstrap validation in the reduced model
back.formula	An object of class formula with the formula used to fit the reduced model
lastRemoved	The name of the last term that was removed (-1 if all terms were removed)
at.opt.model	The model will have the fitted model that had close to maximum bootstrapped test accuracy
beforeFSC.formula	The formula of the model before False Selection Correction
at.Accuracy.formula	the string formula of the model that had the best or close to tbe best test accuracy

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

References

Pencina, M. J., D'Agostino, R. B., & Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine 27(2), 157-172.

See Also

bootstrapVarElimination_Res, backVarElimination_Bin, backVarElimination_Res

FRESA.CAD documentation built on Nov. 25, 2023, 1:07 a.m.