classDS {depthTools} | R Documentation |
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.
classDS(xl,yl,xt,alpha=0.2)
xl |
an |
yl |
a vector of length |
xt |
an |
alpha |
the proportion of observations that are trimmed out when computing the mean. 0.2 by default. |
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.
pred |
the vector of length |
Sara Lopez-Pintado sl2929@columbia.edu and
Aurora Torrente etorrent@est-econ.uc3m.es
Lopez-Pintado, S. et al. (2010). Robust depth-based tools for the analysis of gene expression data. Biostatistics, 11 (2), 254-264.
classTAD
## 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