This function implements a simple Gaussian maximum likelihood
discriminant rule, for diagonal class covariance matrices.
Usage
stat.diag.da(ls, cll, ts, pool=1)
Arguments
ls
learning set data matrix, with rows corresponding to
cases (i.e., mRNA samples) and columns to predictor variables
(i.e., genes).
cll
class labels for learning set, must be consecutive integers.
ts
test set data matrix, with rows corresponding to cases
and columns to predictor variables.
pool
logical flag. If pool=1, the covariance matrices
are assumed to be constant across classes and the discriminant rule
is linear in the data. If pool=0, the covariance matrices may
vary across classes and the discriminant rule is q
Value
List containing the following components
predvector of class predictions for the test set.
References
S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of
Discrimination Methods for the Classification of Tumors Using Gene
Expression Data. June 2000. (Statistics, UC Berkeley, Tech Report #576).