classDS: Distance to the Trimmed Mean Classification Method

In depthTools: Depth Tools Package

Description

Implementation of the classification technique based on assigning each observation to the group that minimizes the distance of the observation to the trimmed mean of the group.

Usage

classDS(xl,yl,xt,alpha=0.2)

Arguments

xl	an nxp data matrix containing the observations (samples) from the learning set by rows and the variables (genes) by columns
yl	a vector of length n containing the class each observations in xl belongs to
xt	an mxp data matrix containing the observations (samples) from the test set by rows and the variables (genes) by columns
alpha	the proportion of observations that are trimmed out when computing the mean. 0.2 by default.

Details

This classification method proceeds by first computing the alpha trimmed mean corresponding to each group from the learning set, then computing the distance from a new observation to each trimmed mean. The new sample will then be assigned to the group that minimizes such distance. At the moment, only the Euclidean distance is implemented.

Value

pred	the vector of length m containing the predicted class of observations in matrix xt

Author(s)

Sara Lopez-Pintado [e-mail] and

Aurora Torrente [e-mail]

References

Lopez-Pintado, S. et al. (2010). Robust depth-based tools for the analysis of gene expression data. Biostatistics, 11 (2), 254-264.

See Also

classTAD

Examples

  ## simulated data
  set.seed(10)
  xl <- matrix(rnorm(100),10,10); xl[1:5,]<-xl[1:5,]+1
  yl <- c(rep(0,5),rep(1,5))
  xt <- matrix(rnorm(100),10,10)
  classDS(xl,yl,xt)

  ## real data
  data(prostate)
  prost.x<-prostate[,1:100]
  prost.y<-prostate[,101]
  set.seed(1)
  learning <- sample(50,40,replace=FALSE) 
  yl <- prost.y[learning]   
  xl <- prost.x[learning,]
  training <- c(1:nrow(prost.x))[-learning]
  yt.real <- prost.y[training]
  xt <- prost.x[training,] 
  yt.estimated <- classDS(xl,yl,xt)
  yt.real==yt.estimated
  

Example output

 [1] 1 1 1 1 1 1 1 1 1 1
 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

depthTools documentation built on May 24, 2021, 9:06 a.m.