gr.cv: A function for cross validation
In geneRecommender: A gene recommender algorithm to identify genes coexpressed with a query set of genes

Description Usage Arguments Details Value Author(s) References See Also Examples

gr.cv performs leave-one-out cross validation with gr.main for each element of the query.

1	gr.cv(normalized.dataset, query, fun = median)

`normalized.dataset`	A matrix or ExpressionSet containing the normalized gene expression data. The rows correspond to genes, the columns correspond to experiments, and the entries correspond to the gene expression levels. The rows must be labeled. The values contained in `normalized.dataset` must be either finite or NA.
`query`	A vector containing the query set of genes. These should correspond to the row names of `normalized.dataset`. The query must contain at least 2 elements
`fun`	A function used in choosing the number of experiments to include in the calculation. See the help file for `gr.main` for details.

In addition to measuring performance, the results of the cross validation can be used to determine if some element(s) in the query might not belong. If one of the elements in the output vector was very large, one would suspect that the associated gene was regulated differently than the other genes in the query.

A vector containing the rank of each element in the query produced by applying gr.main to the query with that element removed.

Gregory J. Hather ghather@stat.berkeley.edu
with contributions from from Art B. Owen art@stat.stanford.edu
and Terence P. Speed terry@stat.berkeley.edu.

Art B. Owen, Josh Stuart, Kathy Mach, Anne M. Villeneuve, and Stuart Kim. A Gene Recommender Algorithm to Identify Coexpressed Genes in C. elegans. Genome Research 13:1828-1837, 2003.

gr.main, gr.normalize

#This example uses the geneData dataset from the Biobase package
data(geneData)
my.query <- c("31730_at", "31331_at", "31712_at", "31441_at")
normalized.data <- gr.normalize(geneData)
gr.cv(normalized.data, my.query)