R: IDI/NRI-based backwards variable elimination with...

bootstrapVarElimination_Bin {FRESA.CAD}

R Documentation

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.