IDeA {FRESA.CAD}

Decorrelation of data frames

Description

All continous features that with significant correlation will be decorrelated

Usage

  ILAA(data=NULL,
                thr=0.80,
                method=c("pearson","spearman"),
                Outcome=NULL,
                drivingFeatures=NULL,
                maxLoops=100,
                verbose=FALSE,
                bootstrap=0
                )
                
  IDeA(data=NULL,thr=0.80,
                       method=c("fast","pearson","spearman","kendall"),
                       Outcome=NULL,
                       refdata=NULL,
                       drivingFeatures=NULL,
                       useDeCorr=TRUE,
                       relaxed=TRUE,
                       corRank=TRUE,
                       maxLoops=100,
                       unipvalue=0.05,
                       verbose=FALSE,
                       ...)

  
  predictDecorrelate(decorrelatedobject,testData)

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

drivingFeatures

A vector of features to be used as basis vectors.

unipvalue

Maximum p-value for correlation significance

useDeCorr

if TRUE, the transformation matrix (UPLTM) 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.

relaxed

is set to TRUE it will use relaxed convergence

corRank

is set to TRUE it will correlation matrix to break ties.

...

parameters passed to the featureAdjustment function.

decorrelatedobject

The returned dataframe of the IDeA function

testData

The new dataframe to be decorrelated

bootstrap

If greater than 1 the number of boostrapping loops

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 unit-preserving spatial transformation matrix (UPLTM). ILAA is a wrapper of the more comprensive IDeA method. It estimates linear transforms and allows for boosted transform estimations

Value

decorrelatedDataframe

The decorrelated data frame with the follwing attributes

attr:UPLTM

Attribute of decorrelatedDataframe: The Decorrelation matrix with the beta coefficients

attr:fscore

Attribute of decorrelatedDataframe: The score of each feature.

attr:drivingFeatures

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

attr:unipvalue

Attribute of decorrelatedDataframe: The p-value used to check for fit significance

attr:R.critical

Attribute of decorrelatedDataframe: The pearson correlation critical value

attr:IDeAEvolution

Attribute of decorrelatedDataframe: The R measure history and the sparcity

attr:VarRatio

Attribute of decorrelatedDataframe: The variance ratio between the output latent variable and the observed

Author(s)

Jose G. Tamez-Pena

See Also

featureAdjustment

Examples

  ## Not run: 
  # 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 <- IDeA(iris,thr=0.25))
  
  ## The transformation matrix is stored at "UPLTM" attribute
  UPLTM <- attr(irisDecor,"UPLTM")
  print(UPLTM)

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

  ## 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="Outcome-Blind IDeA IRIS")


  featuresDecor <- colnames(irisDecorOutcome[,sapply(irisDecorOutcome,is,"numeric")])
  plot(irisDecorOutcome[,featuresDecor],col=classcolor,main="Outcome-Driven IDeA IRIS")
  
## End(Not run)