FRESA.CAD (version 2.0.2)

NeRIBasedFRESA.Model: 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

NeRIBasedFRESA.Model(size = 100, 
	                     fraction = 1, 
	                     pvalue = 0.05, 
	                     loops = 100, 
	                     covariates = "1", 
	                     Outcome, 
	                     variableList, 
	                     data, 
	                     maxTrainModelSize = 10, 
	                     type = c("LM", "LOGIT", "COX"), 
	                     testType=c("Binomial", "Wilcox", "tStudent", "Ftest"),
	                     timeOutcome = "Time",
	                     loop.threshold = 20,
	                     interaction = 1,
	                     cores = 4)

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)

loop.threshold

After loop.threshold cycles, only variables that have already been selected in previous cycles will be candidates to be selected in posterior cycles

interaction

Set to either 1 for first order models, or to 2 for second order models

cores

Cores to be used for parallel processing

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
• z.NeRIs
  A vector in which each element represents the z-score of the NeRI, associated to the testType, for each feature found in the final model
• formula.list
  A list containing objects of class formula with the formulas used to fit the models found at each cycle

See Also

ReclassificationFRESA.Model

Examples

# Start the graphics device driver to save all plots in a pdf format
	pdf(file = "Example.pdf")
	# Get the stage C prostate cancer data from the rpart package
	library(rpart)
	data(stagec)
	# Split the stages into several columns
	dataCancer <- cbind(stagec[,c(1:3,5:6)],
	                    gleason4 = 1*(stagec[,7] == 4),
	                    gleason5 = 1*(stagec[,7] == 5),
	                    gleason6 = 1*(stagec[,7] == 6),
	                    gleason7 = 1*(stagec[,7] == 7),
	                    gleason8 = 1*(stagec[,7] == 8),
	                    gleason910 = 1*(stagec[,7] >= 9),
	                    eet = 1*(stagec[,4] == 2),
	                    diploid = 1*(stagec[,8] == "diploid"),
	                    tetraploid = 1*(stagec[,8] == "tetraploid"),
	                    notAneuploid = 1-1*(stagec[,8] == "aneuploid"))
	# Remove the incomplete cases
	dataCancer <- dataCancer[complete.cases(dataCancer),]
	# Load a pre-stablished data frame with the names and descriptions of all variables
	data(cancerVarNames)
	# Rank the variables:
	# - Analyzing the raw data
	# - Using a Cox proportional hazards fitting
	# - According to the NeRI
	rankedDataCancer <- univariateRankVariables(variableList = cancerVarNames,
	                                            formula = "Surv(pgtime, pgstat) ~ 1",
	                                            Outcome = "pgstat",
	                                            data = dataCancer,
	                                            categorizationType = "Raw",
	                                            type = "COX",
	                                            rankingTest = "NeRI",
	                                            description = "Description")
	# Get a Cox proportional hazards model using:
	# - 10 bootstrap loops
	# - The ranked variables
	# - The Wilcoxon rank-sum test as the feature inclusion criterion
	cancerModel <- NeRIBasedFRESA.Model(loops = 10,
	                                    Outcome = "pgstat",
	                                    variableList = rankedDataCancer,
	                                    data = dataCancer,
	                                    type = "COX",
	                                    testType= "Wilcox",
	                                    timeOutcome = "pgtime")
	# Shut down the graphics device driver
	dev.off()