FRESA.CAD (version 3.4.3)

GDSTMDecorrelation: Decorrelation of data frames

Description

All continous features that with significant correlation will be decorrelated

Usage

GDSTMDecorrelation(data=NULL,thr=0.80,
	                     refdata=NULL,Outcome=NULL,
	                     baseFeatures=NULL,unipvalue=0.05,
	                     useDeCorr=TRUE,maxLoops=100,
	                     verbose=FALSE,
	                     method=c("fast","pearson","spearman","kendall"),
						 skipRelaxed=TRUE,
	                     ...)
	
	predictDecorrelate(decorrelatedobject,testData)

Value

decorrelatedDataframe

The decorrelated data frame with the follwing attributes

attr:topFeatures)

Attribute of adjustedFrame: The list of features that were decorrelated

attr:TotalAdjustments

Attribute of adjustedFrame: The count of how many iteration were required

attr:GDSTM

Attribute of adjustedFrame: The Decorrelation matrix with the beta coefficients

attr:varincluded

Attribute of adjustedFrame: The list of input variables used in GDSTM

attr:baseFeatures

Attribute of adjustedFrame: The list of features used as base features for supervised basis

attr:useDeCorr

Attribute of adjustedFrame: If TRUE the estimated GDSTM is used for decorrelation

attr:correlatedToBase

Attribute of adjustedFrame: List of correlated features to the base features

attr:AbaseFeatures

Attribute of adjustedFrame: List of unsupervised basis features

attr:fscore

Attribute of adjustedFrame: The score of each feature.

Arguments

data

The dataframe whose features will de decorrelated

thr

The maximum allowed correlation.

refdata

Option: A data frame that may be used to decorrelate the target dataframe

Outcome

The target outcome for supervised basis

baseFeatures

A vector of features to be used as basis vectors.

unipvalue

Maximum p-value for correlation significance

useDeCorr

if TRUE, the transformation matrix (GDSTM) will be computed

maxLoops

the maxumum number of iteration loops

verbose

if TRUE, it will display internal evolution of algorithm.

method

if not set to "fast" the method will be pased to the cor() function.

skipRelaxed

is set to FALSE it will use relaxed convergence

...

parameters passed to the featureAdjustment function.

decorrelatedobject

The returned dataframe of the GDSTMDecorrelation function

testData

the new dataframe to be decorrelated

Author

Jose G. Tamez-Pena

Details

The dataframe will be analyzed and significantly correlated features whose correlation is larger than the user supplied threshold will be decorrelated. Basis feature selection may be based on Outcome association or by an unsupervised method. The default options will run the decorrelation using fast matrix operations using Rfast; hence, Pearson correlation will be used to estimate the GDSTM.

See Also

featureAdjustment

Examples

	# load FRESA.CAD library
#	library("FRESA.CAD")

# iris data set
	data('iris')

	colors <- c("red","green","blue")
	names(colors) <- names(table(iris$Species))
	classcolor <- colors[iris$Species]

	#Decorrelating with usupervised basis and correlation goal set to 0.25
	system.time(irisDecor <- GDSTMDecorrelation(iris,thr=0.25))
	
	## The transformation matrix is stored at "GDSTM" attribute
	GDSTM <- attr(irisDecor,"GDSTM")
	print(GDSTM)

	#Decorrelating with supervised basis and correlation goal set to 0.25
	system.time(irisDecorOutcome <- GDSTMDecorrelation(iris,Outcome="Species",thr=0.25))
	## The transformation matrix is stored at "GDSTM" attribute
	GDSTM <- attr(irisDecorOutcome,"GDSTM")
	print(GDSTM)

	## Compute PCA 
	features <- colnames(iris[,sapply(iris,is,"numeric")])
	irisPCA <- prcomp(iris[,features]);
	## The PCA transformation
	print(irisPCA$rotation)

	## Plot the transformed sets
	plot(iris[,features],col=classcolor,main="Raw IRIS")

	plot(as.data.frame(irisPCA$x),col=classcolor,main="PCA IRIS")

	featuresDecor <- colnames(irisDecor[,sapply(irisDecor,is,"numeric")])
	plot(irisDecor[,featuresDecor],col=classcolor,main="Unsupervised FCA IRIS")


	featuresDecor <- colnames(irisDecorOutcome[,sapply(irisDecorOutcome,is,"numeric")])
	plot(irisDecorOutcome[,featuresDecor],col=classcolor,main="Supervised FCA IRIS")