1d:T7fd,# Distance to the Trimmed Mean Classification Method 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. ```r 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. ## Returns - **pred**: the vector of length `m` containing the predicted class of observations in matrix `xt` ## 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. ## 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. ## See Also classTAD ## Examples ```r ## 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.estimate

classDS function