Estimate CAT Scores and t-Scores
catscore computes CAT scores
(correlation-adjusted t-scores)
between the group centroids and the pooled mean.
catscore(Xtrain, L, lambda, lambda.var, lambda.freqs, diagonal=FALSE, verbose=TRUE)
Xtrain |
A matrix containing the training data set. Note that the rows correspond to observations and the columns to variables. |
L |
A factor with the class labels of the training samples. |
lambda |
Shrinkage intensity for the correlation matrix. If not specified it is
estimated from the data. |
lambda.var |
Shrinkage intensity for the variances. If not specified it is
estimated from the data. |
lambda.freqs |
Shrinkage intensity for the frequencies. If not specified it is
estimated from the data. |
diagonal |
for |
verbose |
Print out some info while computing. |
CAT scores generalize conventional t-scores to account for correlation among predictors (Zuber and Strimmer 2009). If there is no correlation then CAR scores reduce to t-scores. The squared CAR scores provide a decomposition of Hotelling's T^2 statistic.
CAT scores for two classes are described in Zuber and Strimmer (2009), for the multi-class case see Ahdesm\"aki and Strimmer (2010).
The scale factors for t-scores and CAT-scores are computed from the estimated frequencies
(for empirical scale factors set lambda.freqs=0).
catscore returns a matrix containing the cat score (or t-score) between
each group centroid and the pooled mean for each feature.
Verena Zuber, Miika Ahdesm\"aki and Korbinian Strimmer (http://strimmerlab.org).
Ahdesm\"aki, A., and K. Strimmer. 2010. Feature selection in omics prediction problems using cat scores and false non-discovery rate control. Ann. Appl. Stat. 4: 503-519. Preprint available from http://arxiv.org/abs/0903.2003.
Zuber, V., and K. Strimmer. 2009. Gene ranking and biomarker discovery under correlation. Bioinformatics 25: 2700-2707. Preprint available from http://arxiv.org/abs/0902.0751.
# load sda library
library("sda")
#################
# training data #
#################
# prostate cancer set
data(singh2002)
# training data
Xtrain = singh2002$x
Ytrain = singh2002$y
dim(Xtrain)
####################################################
# shrinkage t-score (DDA setting - no correlation) #
####################################################
tstat = catscore(Xtrain, Ytrain, diagonal=TRUE)
dim(tstat)
tstat[1:10,]
########################################################
# shrinkage CAT score (LDA setting - with correlation) #
########################################################
cat = catscore(Xtrain, Ytrain, diagonal=FALSE)
dim(cat)
cat[1:10,]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.