After fitting a classifier, it is often desirable to output the most "interesting" genes for further validation. This function will output the top 'n' genes that discriminate between each class, along with an estimate of the stability of the observed rankings (see details for more information).

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`formula` |
A symbolic description of the model to be fit. Details given below. |

`method` |
One of "pls", "pcr", "ridge", corresponding to partial least squares, principal components regression and ridge regression. |

`data` |
An optional data.frame that contains the variables in the
model. If not found in |

`weights` |
An optional vector of sample weights. Defaults to 1. |

`theta` |
An optional matrix of class scores, typically with less than J - 1 columns. |

`dimension` |
The dimension of the solution, no greater than J - 1, where J is the number of classes. Defaults to J - 1. |

`eps` |
A threshold for excluding small discriminant
variables. Defaults to |

`genelist` |
A vector of gene names, one per gene. |

`list.length` |
The number of 'top' genes to output. |

`B` |
The number of bootstrap samples to use for estimating stability. Defaults to 100. More than this may take an inordinate amount of time. |

`...` |
Additional parameters to pass to |

The formula interface is identical to all other formula calls in R, namely Y ~ X, where Y is a numeric vector of class assignments and X is a matrix or data.frame containing the gene expression values. Note that unlike most microarray analyses, in this instance the columns of X are genes and rows are samples, so most calls will require something similar to Y ~ t(X).

The dimension of the solution is typically J - 1, where J is the
number of classes. The model fit uses `contr.treatment`

contrasts, which means that all of the coefficients in the model
are comparing the given class to a baseline class. Therefore, the
genes listed are those that discriminate between a given class and the
baseline. For instance, if there are three classes (characterized by a
numeric vector of 1s, 2s, and 3s), then there will be two sets of 'top
genes'. The first set will be those genes that discriminate between
class 2 and class 1, whereas the second set will be the genes that
discriminate between class 3 and class 1. The 'Y' vector will
therefore need to be constructed to give the comparisons of interest.

A list containing a `data.frame`

for each comparison. The first
column of each `data.frame`

contains the gene names, and the
second column contains the frequency that the gene was observed in the
bootstrapped samples.

James W. MacDonald and Debashis Ghosh. Partial least squares
and principal components regression based on code written by
Mike Denham and contributed to StatLib. Model fit based on code from
the `mda`

package written by Trevor Hastie and Robert Tibshirani
and ported to R by Kurt Hornik, Brian D. Ripley, and Friedrich Leisch.

http://www.sph.umich.edu/~ghoshd/COMPBIO/POPTSCORE

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