R: Distance to the Trimmed Mean Classification Method

classDS {depthTools}

R Documentation

Distance to the Trimmed Mean Classification Method

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 sl2929@columbia.edu and

Aurora Torrente etorrent@est-econ.uc3m.es

References

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

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

[Package depthTools version 0.5 Index]