updateModel.Res: Update the NeRI-based model using new data or new threshold values

Description

This function will take the frequency-ranked set of variables and will generate a new model with terms that meet the net residual improvement (NeRI) threshold criteria.

Usage

updateModel.Res(Outcome, 
	                covariates = "1", 
	                pvalue = c(0.025, 0.05),
	                VarFrequencyTable, 
	                variableList, 
	                data, 
	                type = c("LM", "LOGIT", "COX"),
	                testType=c("Binomial", "Wilcox", "tStudent"), 
	                lastTopVariable = 0, 
	                timeOutcome = "Time",
	                interaction = 1,
	                maxTrainModelSize = -1,
					bootLoops=1)

Arguments

Outcome

The name of the column in data that stores the variable to be predicted by the model

covariates

A string of the type "1 + var1 + var2" that defines which variables will always be included in the models (as covariates)

pvalue

The maximum p-value, associated to the NeRI, allowed for a term in the model

VarFrequencyTable

An array with the ranked frequencies of the features, (e.g. the ranked.var value returned by the ForwardSelection.Model.Res function)

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

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")

lastTopVariable

The maximum number of variables to be tested

timeOutcome

The name of the column in data that stores the time to event (needed only for a Cox proportional hazards regression model fitting)

interaction

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

maxTrainModelSize

Maximum number of terms that can be included in the model

bootLoops

the number of loops for bootstrap estimation of test error

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
z.NeRI: 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
loops: The number of loops it took for the model to stabilize

Examples

Run this code

	## Not run: 
# 	# 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 <- ForwardSelection.Model.Res(loops = 10,
# 	                                    Outcome = "pgstat",
# 	                                    variableList = rankedDataCancer,
# 	                                    data = dataCancer,
# 	                                    type = "COX",
# 	                                    testType= "Wilcox",
# 	                                    timeOutcome = "pgtime")
# 	# Update the model, adding first order interactions
# 	
# 	uCancerModel <- updateModel.Res(Outcome = "pgstat",
# 	        VarFrequencyTable = cancerModel$ranked.var,
# 	        variableList = cancerVarNames,
# 	        data = dataCancer,
# 	        type = "COX",
# 	        testType = "Wilcox",
# 	        timeOutcome = "pgtime",
# 	        interaction = 2)
# 	# Shut down the graphics device driver
# 	dev.off()## End(Not run)

Run the code above in your browser using DataLab