data matrix. Note that the columns correspond to variables (“genes”)
and the rows to samples.
L
factor with class labels for the two groups. If only a single label is given then a one-sample CAT score against 0 is computed.
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).
var.equal
assume equal (default) or unequal variances in each group.
paired
compute paired CAT score (default is to use unpaired CAT score).
verbose
print out some (more or less useful) information during computation.
Details
The CAT (“correlation-adjusted t”) score is the product of the square root of the
inverse correlation matrix with a vector of t scores. The CAT score thus describes the
contribution of each individual feature in separating the two groups,
after removing the effect of all other features.
In Zuber and Strimmer (2009)
it is shown that the CAT score is
a natural criterion to rank features in the presence of correlation.
If there is no correlation, the CAT score reduces to the usual t score
(hence in this case the estimate from shrinkcat.stat equals that from shrinkt.stat).
The function catscore implements multi-class CAT scores.
Value
shrinkcat.stat returns a vector containing a shrinkage estimate of the
“CAT score” for each variable/gene.
The corresponding shrinkcat.fun functions return a function that
computes the cat score when applied to a data matrix
(this is very useful for simulations).
The scale factor in the ”shrinkage CAT” statistic is computed from the estimated frequencies
(to use the standard empirical scale factor set lambda.freqs=0).