stat.diag.da {sma} | R Documentation |
This function implements a simple Gaussian maximum likelihood discriminant rule, for diagonal class covariance matrices.
stat.diag.da(ls, cll, ts, pool=1)
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 quadratic in the
data. |
List containing the following components
pred |
vector of class predictions for the test set. |
Sandrine Dudoit, sandrine@stat.berkeley.edu
Jane Fridlyand, janef@stat.berkeley.edu
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).