R: NeRI-based backwards variable elimination

backVarElimination_Res {FRESA.CAD}

R Documentation

NeRI-based backwards variable elimination

Description

This function removes model terms that do not significantly improve the "net residual" (NeRI)

Usage

	backVarElimination_Res(object,
	                       pvalue = 0.05,
	                       Outcome = "Class",
	                       data,
	                       startOffset = 0, 
	                       type = c("LOGIT", "LM", "COX"),
	                       testType = c("Binomial", "Wilcox", "tStudent", "Ftest"),
	                       setIntersect = 1
						   )

Arguments

`object`	An object of class `lm`, `glm`, or `coxph` containing the model to be analyzed
`pvalue`	The maximum p-value, associated to the NeRI, 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")
`testType`	Type of non-parametric test to be evaluated by the `improvedResiduals` function: Binomial test ("Binomial"), Wilcoxon rank-sum test ("Wilcox"), Student's t-test ("tStudent"), or F-test ("Ftest")
`setIntersect`	The intersect of the model (To force a zero intersect, set this value to 0)

Details

For each model term x_i, the residuals are 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 residuals improvement is selected. The hypothesis: the term improves residuals is tested by checking the pvalue of improvement. If p(residuals better than reduced residuals)>pvalue, then the term is removed. In other words, only model terms that significantly aid in improving residuals are kept. The procedure is repeated until no term fulfils the removal criterion. The p-values of improvement can be computed via a sign-test (Binomial) a paired Wilcoxon test, paired t-test or f-test. The first three tests compare the absolute values of the residuals, while the f-test test if the variance of the residuals is improved significantly.

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 NeRI statistics of the reduced model, as given by the `getVar.Res` function
`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 with before the FSR procedure.
`beforeFSC.formula`	the string formula of the the FSR procedure

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya