stat.diag.da {sma}R Documentation

Diagonal Discriminant Analysis

Description

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 quadratic in the data.

Value

List containing the following components

pred vector of class predictions for the test set.

Author(s)

Sandrine Dudoit, sandrine@stat.berkeley.edu
Jane Fridlyand, janef@stat.berkeley.edu

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).


[Package sma version 0.5.15 Index]