BSWiMS.model {FRESA.CAD}

BSWiMS model selection

Description

This function returns a set of models that best predict the outcome. Based on a Bootstrap Stage Wise Model Selection algorithm.

Usage

	BSWiMS.model(formula,
	            data,
	            type = c("Auto","LM","LOGIT","COX"),
	            testType = c("Auto","zIDI",
	                         "zNRI",
	                         "Binomial",
	                         "Wilcox",
	                         "tStudent",
	                         "Ftest"),
	            pvalue=0.05,
	            variableList=NULL,
	            size=0,
	            loops=20,
	            elimination.bootstrap.steps = 200,
	            fraction=1.0,
	            maxTrainModelSize=20,
	            maxCycles=20,
	            print=FALSE,
	            plots=FALSE,
	            featureSize=0,
	            NumberofRepeats=1,
	            bagPredictType=c("Bag","wNN","Ens")
	            )

Arguments

formula

An object of class formula with the formula to be fitted

data

A data frame where all variables are stored in different columns

type

The fit type. Auto will determine the fitting based on the formula

testType

For an Binary-based optimization, the type of index to be evaluated by the improveProb function (Hmisc package): z-value of Binary or of NRI. For a NeRI-based optimization, the 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")

pvalue

The maximum p-value, associated to the testType, allowed for a term in the model (it will control the false selection rate)

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

size

The number of candidate variables to be tested (the first size variables from variableList)

loops

The number of bootstrap loops for the forward selection procedure

elimination.bootstrap.steps

The number of bootstrap loops for the backwards elimination procedure

fraction

The fraction of data (sampled with replacement) to be used as train

maxTrainModelSize

Maximum number of terms that can be included in the each forward selection model

maxCycles

The maximum number of model generation cycles

print

Logical. If TRUE, information will be displayed

plots

Logical. If TRUE, plots are displayed

featureSize

The original number of features to be explored in the data frame.

NumberofRepeats

How many times the BSWiMS search will be repeated

bagPredictType

Type of prediction of the bagged formulas

Details

This is a core function of FRESA.CAD. The function will generate a set of B:SWiMS models from the data based on the provided baseline formula. The function will loop extracting a models whose all terms are statistical significant. After each loop it will remove the significant terms, and it will repeat the model generation until no mode significant models are found or the maximum number of cycles is reached.

Value

BSWiMS.model

the output of the bootstrap backwards elimination step

forward.model

The output of the forward selection step

update.model

The output of the forward selection step

univariate

The univariate ranking of variables if no list of features was provided

bagging

The model after bagging the set of models

formula.list

The formulas extracted at each cycle

forward.selection.list

All formulas generated by the forward selection procedure

oridinalModels

A list of scores, the data and a formulas vector required for ordinal scores predictions

Author(s)

Jose G. Tamez-Pena

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.

Examples

	## Not run: 

		# Start the graphics device driver to save all plots in a pdf format
		pdf(file = "BSWiMS.model.Example.pdf",width = 8, height = 6)

		# Get the stage C prostate cancer data from the rpart package
		data(stagec,package = "rpart")
		options(na.action = 'na.pass')
		stagec_mat <- cbind(pgstat = stagec$pgstat,
             pgtime = stagec$pgtime,
             as.data.frame(model.matrix(Surv(pgtime,pgstat) ~ .*.,stagec))[-1])
		fnames <- colnames(stagec_mat)
		fnames <- str_replace_all(fnames,":","__")
		colnames(stagec_mat) <- fnames

		dataCancerImputed <- nearestNeighborImpute(stagec_mat)

		# Get a Cox proportional hazards model using:
		# - The default parameters
		md <- BSWiMS.model(formula = Surv(pgtime, pgstat) ~ 1,
						  data = dataCancerImputed)

		#Plot the bootstrap validation
		pt <- plot(md$BSWiMS.model$bootCV)

		#Get the coefficients summary
		sm <- summary(md)
		print(sm$coefficients)

		#Plot the bagged model 
		pl <- plotModels.ROC(cbind(dataCancerImputed$pgstat,
							  predict(md,dataCancerImputed)),
							 main = "Bagging Predictions")


		# Get a Cox proportional hazards model using:
		# - The default parameters but repeated 10 times
		md <- BSWiMS.model(formula = Surv(pgtime, pgstat) ~ 1,
						   data = dataCancerImputed,
						   NumberofRepeats = 10)

		#Get the coefficients summary
		sm <- summary(md)
		print(sm$coefficients)

		#Check all the formulas
		print(md$formula.list)

		#Plot the bagged model 
		pl <- plotModels.ROC(cbind(dataCancerImputed$pgstat,
								   predict(md,dataCancerImputed)),
							 main = "Bagging Predictions")


		# Get a  regression of the survival time

		timeSubjects <- dataCancerImputed
		timeSubjects$pgtime <- log(timeSubjects$pgtime)

		md <- BSWiMS.model(formula = pgtime ~ 1,
						  data = timeSubjects,
						  )
		pt <- plot(md$BSWiMS.model$bootCV)
		sm <- summary(md)
		print(sm$coefficients)

		# Get a logistic regression model using
		# - The default parameters and removing time as possible predictor
		data(stagec,package = "rpart")
		stagec$pgtime <- NULL
		stagec_mat <- cbind(pgstat = stagec$pgstat,
                     as.data.frame(model.matrix(pgstat ~ .*.,stagec))[-1])
		fnames <- colnames(stagec_mat)
		fnames <- str_replace_all(fnames,":","__")
		colnames(stagec_mat) <- fnames
		dataCancerImputed <- nearestNeighborImpute(stagec_mat)


		md <- BSWiMS.model(formula = pgstat ~ 1,
						  data = dataCancerImputed)

		pt <- plot(md$BSWiMS.model$bootCV)
		sm <- summary(md)
		print(sm$coefficients)


		# Get a ordinal regression of grade model using GBSG2 data
		# - The default parameters and removing the 
		# time and status as possible predictor

		data("GBSG2", package = "TH.data")

		# Prepare the model frame for prediction
		GBSG2$time <- NULL;
		GBSG2$cens <- NULL;
		GBSG2_mat <- cbind(tgrade = as.numeric(GBSG2$tgrade),
                       as.data.frame(model.matrix(tgrade~.*.,GBSG2))[-1])

		fnames <- colnames(GBSG2_mat)
		fnames <- str_replace_all(fnames,":","__")
		colnames(GBSG2_mat) <- fnames

		md <- BSWiMS.model(formula = tgrade ~ 1,
						   data = GBSG2_mat)

		sm <- summary(md$oridinalModels$theBaggedModels[[1]]$bagged.model)
		print(sm$coefficients)
		sm <- summary(md$oridinalModels$theBaggedModels[[2]]$bagged.model)
		print(sm$coefficients)

		print(table(GBSG2_mat$tgrade,predict(md,GBSG2_mat)))

		# Shut down the graphics device driver
		dev.off()

	
## End(Not run)