R: Estimate CAT Scores and t-Scores

catscore {sda} | R Documentation

## Estimate CAT Scores and t-Scores

### Description

`catscore` computes CAT scores (correlation-adjusted t-scores) between the group centroids and the pooled mean.

### Usage

    catscore(Xtrain, L, lambda, lambda.var, lambda.freqs, diagonal=FALSE, verbose=TRUE)

### Arguments

`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=0` implies no shrinkage and `lambda=1` complete shrinkage.
`lambda.var` | Shrinkage intensity for the variances. If not specified it is estimated from the data. `lambda.var=0` implies no shrinkage and `lambda.var=1` complete shrinkage.
`lambda.freqs` | Shrinkage intensity for the frequencies. If not specified it is estimated from the data. `lambda.freqs=0` implies no shrinkage (i.e. empirical frequencies) and `lambda.freqs=1` complete shrinkage (i.e. uniform frequencies).
`diagonal` | for `diagonal=FALSE` (the default) CAT scores are computed; otherwise with `diagonal=TRUE` t-scores.
`verbose` | Print out some info while computing.

### Details

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`).

### Value

`catscore` returns a matrix containing the cat score (or t-score) between each group centroid and the pooled mean for each feature.

### Author(s)

Verena Zuber, Miika Ahdesm\"aki and Korbinian Strimmer (http://strimmerlab.org).

### References

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.

### See Also

`sda.ranking`, `carscore`,.

### Examples

    # 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,]

[Package sda version 1.3.7 Index]