FRESA.CAD (version 2.2.0)

modelFitting: Fit a model to the data

Description

This function fits a linear, logistic, or Cox proportional hazards regression model to given data

Usage

modelFitting(model.formula,
	             data,
	             type = c("LOGIT", "LM", "COX"),
	             fast=FALSE,
	              ...)

Arguments

model.formula

An object of class formula with the formula to be used

data

A data frame where all variables are stored in different columns

type

Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")

fast

if true it will perform a fast fitting.

...

Additional parameters for fitting a default glm object

Value

A fitted model of the type defined in type

Examples

	## 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),]
# 	# Create a formula of a Cox proportional hazards model using all variables
# 	allVars <- formula("Surv(pgtime, pgstat)  ~ 1 +
# 	                                            age +
# 	                                            g2 +
# 	                                            grade +
# 	                                            gleason4 +
# 	                                            gleason5 +
# 	                                            gleason6 +
# 	                                            gleason7 +
# 	                                            gleason8 +
# 	                                            gleason910 +
# 	                                            eet +
# 	                                            diploid +
# 	                                            tetraploid +
# 	                                            notAneuploid")
# 	# Fit the model to the dataCancer
# 	allVarsFit <- modelFitting(model.formula = allVars,
# 	                           data = dataCancer,
# 	                           type = "COX")
# 	# Shut down the graphics device driver
# 	dev.off()## End(Not run)