listTopCorrelatedVariables: List the variables that are highly correlated with each other

In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

List the variables that are highly correlated with each other

Description

This function computes the Pearson, Spearman, or Kendall correlation for each specified variable in the data set and returns a list of the variables that are correlated to them. It also provides a short variable list without the highly correlated variables.

Usage

	listTopCorrelatedVariables(variableList,
	                           data,
	                           pvalue = 0.001,
	                           corthreshold = 0.9,
	                           method = c("pearson", "kendall", "spearman"))

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
data	A data frame where all variables are stored in different columns
pvalue	The maximum p-value, associated to method, allowed for a pair of variables to be defined as significantly correlated
corthreshold	The minimum correlation score, associated to method, allowed for a pair of variables to be defined as significantly correlated
method	Correlation method: Pearson product-moment ("pearson"), Spearman's rank ("spearman"), or Kendall rank ("kendall")

Value

correlated.variables	A data frame with two columns: cor.var.names: The variables that are correlated cor.var.value: The correlation value
short.list	A vector with a list of variables that are not correlated to each other. For every correlated pair, only the variable that first entered the correlation analysis was kept

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

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),]
	# Load a pre-stablished data frame with the names and descriptions of all variables
	data(cancerVarNames)
	# Get the variables that have a correlation coefficient larger 
	# than 0.65 at a p-value of 0.05
	cor <- listTopCorrelatedVariables(variableList = cancerVarNames,
	                                  data = dataCancer,
	                                  pvalue = 0.05,
	                                  corthreshold = 0.65,
	                                  method = "pearson")
	# Shut down the graphics device driver
	dev.off()
## End(Not run)

FRESA.CAD documentation built on Nov. 25, 2023, 1:07 a.m.