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]