FRESA.CAD (version 2.0.2)

heatMaps: Plot a heat map of selected variables

Description

This function creates a heat map for a data set based on a univariate or frequency ranking

Usage

heatMaps(variableList,
	         varRank = NULL,
	         Outcome,
	         idnames = NULL,
	         data,
	         title = "Heat Map",
	         colCluster = FALSE,
	         prediction = NULL,
	         outcomeGain = 0)

Arguments

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

varRank

A data frame with the name of the variables in variableList, ranked according to a certain metric

Outcome

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

idnames

The name of the column in data that stores the IDs of the measurements (optional)

data

A data frame where all variables are stored in different columns

title

The title of the plot

colCluster

Logical. If TRUE, variables will be clustered

prediction

A vector with a prediction for each subject, which will be used to rank the heat map

outcomeGain

An optional value that can be used to force the heatmap to cluster subjects according to outcome

Value

• dataMatrix
  A matrix with all the terms in data described by variableList
• orderMatrix
  A matrix similar to dataMatrix, where rows are ordered according to the outcome
• heatMap
  A list with the values returned by the heatmap.2 function (gplots package)

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
	# - According to the zIDI
	rankedDataCancer <- univariateRankVariables(variableList = cancerVarNames,
	                                            formula = "Surv(pgtime, pgstat) ~ 1",
	                                            Outcome = "pgstat",
	                                            data = dataCancer,
	                                            categorizationType = "Raw",
	                                            type = "COX",
	                                            rankingTest = "zIDI",
	                                            description = "Description")
	# Get a Cox proportional hazards model using:
	# - 10 bootstrap loops
	# - Age as a covariate
	# - zIDI as the feature inclusion criterion
	cancerModel <- ReclassificationFRESA.Model(loops = 10,
	                                           covariates = "1 + age",
	                                           Outcome = "pgstat",
	                                           variableList = rankedDataCancer,
	                                           data = dataCancer,
	                                           type = "COX",
	                                           timeOutcome = "pgtime",
	                                           selectionType = "zIDI")
	# Scale the C prostate cancer data for a heatmap
	dataCancerScale <- as.data.frame(scale(dataCancer))
	# Generate a heat map using:
	# - The top ranked variables
	# - The scaled data
	hmTop <- heatMaps(variableList = rankedDataCancer,
	                  varRank = cancerModel$ranked.var,
	                  Outcome = "pgstat",
	                  data = dataCancerScale,
	                  outcomeGain = 10)
	# Shut down the graphics device driver
	dev.off()