R: NeRI-based feature selection procedure for linear, logistic,...

ForwardSelection.Model.Res {FRESA.CAD}

R Documentation

NeRI-based feature selection procedure for linear, logistic, or Cox proportional hazards regression models

Description

This function performs a bootstrap sampling to rank the most frequent variables that statistically aid the models by minimizing the residuals. After the frequency rank, the function uses a forward selection procedure to create a final model, whose terms all have a significant contribution to the net residual improvement (NeRI).

Usage

	ForwardSelection.Model.Res(size = 100, 
	                     fraction = 1, 
	                     pvalue = 0.05, 
	                     loops = 100, 
	                     covariates = "1", 
	                     Outcome, 
	                     variableList, 
	                     data, 
	                     maxTrainModelSize = 20, 
	                     type = c("LM", "LOGIT", "COX"), 
	                     testType=c("Binomial", "Wilcox", "tStudent", "Ftest"),
	                     timeOutcome = "Time",
	                     cores = 6,
	                     randsize = 0,
	                     featureSize=0)

Arguments

`size`	The number of candidate variables to be tested (the first `size` variables from `variableList`)
`fraction`	The fraction of data (sampled with replacement) to be used as train
`pvalue`	The maximum p-value, associated to the NeRI, allowed for a term in the model (controls the false selection rate)
`loops`	The number of bootstrap loops
`covariates`	A string of the type "1 + var1 + var2" that defines which variables will always be included in the models (as covariates)
`Outcome`	The name of the column in `data` that stores the variable to be predicted by the model
`variableList`	A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables
`data`	A data frame where all variables are stored in different columns
`maxTrainModelSize`	Maximum number of terms that can be included in the model
`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")
`timeOutcome`	The name of the column in `data` that stores the time to event (needed only for a Cox proportional hazards regression model fitting)
`cores`	Cores to be used for parallel processing
`randsize`	the model size of a random outcome. If randsize is less than zero. It will estimate the size
`featureSize`	The original number of features to be explored in the data frame.

Value

`final.model`	An object of class `lm`, `glm`, or `coxph` containing the final model
`var.names`	A vector with the names of the features that were included in the final model
`formula`	An object of class `formula` with the formula used to fit the final model
`ranked.var`	An array with the ranked frequencies of the features
`formula.list`	A list containing objects of class `formula` with the formulas used to fit the models found at each cycle
`variableList`	A list of variables used in the forward selection

Author(s)