vim.ebam: EBAM Based Importance

In holgerschw/logicFS: Identification of SNP Interactions

Description

Determines the importance of interactions found by logic.bagging or logicFS by an Empirical Bayes Analysis of Microarrays (EBAM). Only available for the classification and the logistic regression approach of logic regression.

Usage

vim.ebam(object, data = NULL, cl = NULL, storeEBAM = FALSE, ...)

Arguments

object	either an object of class logicFS or the output of an application of logic.bagging with importance = TRUE.
data	a data frame or matrix consisting of 0's and 1's in which each column corresponds to one of the explanatory variables used in the original analysis with logic.bagging or logicFS, and each row corresponds to an observation. Must be specified if object is an object of class logicFS, or cl is specified. If object is an object of class logicBagg and neither data nor cl is specified, data and cl stored in object is used to compute the ChiSquare statistics. It is, however, highly recommended to use new data to test the interactions contained in object, as they have been found using the data stored in object, and it is very likely that most of them will show up as interesting if they are tested on the same data set.
cl	a numeric vector of 0's and 1's specifying the class labels of the observations in data. Must be specified either if object is an object of class logicFS, or if data is specified.
storeEBAM	logical specifying whether the output of the EBAM analysis should be stored in the output of vim.ebam.
...	further arguments of ebam and cat.ebam. For details, see the help files of these functions from the package siggenes.

Details

For each interaction found by logic.bagging or logicFS, the posterior probability that this interaction is significant is computed using the Empirical Bayes Analysis of Microarrays (EBAM). These posterior probabilities are used as the EBAM based importances of the interactions.

The test statistic underlying this EBAM analysis is Pearson's ChiSquare statistic. Currently, the value of this statistic is computed without continuity correction.

Contrary to vim.logicFS (and vim.norm and vim.signperm), vim.ebam does neither take the logic regression models into acount nor uses the out-of-bag observations for computing the importances of the identified interactions. It "just" tests each of the found interactions on the whole data set by calculating Pearson's ChiSquare statistic for each of these interactions and performing an EBAM analysis. It is, therefore, highly recommended to use an independent data set for specifying the importances of these interactions with vim.ebam.

Value

An object of class logicFS containing

primes	the prime implicants,
vim	the posterior probabilities of the interactions,
prop	NULL,
type	NULL,
param	further parameters (if object is the output of logicFS or vim.logicFS with addInfo = TRUE),
mat.imp	NULL,
measure	"EBAM Based",
threshold	the value of delta used in the EBAM analysis (see help files for ebam); by default: 0.9,
mu	NULL,
ebam	an object of class EBAM (only available if storeEBAM = TRUE).

Author(s)

Holger Schwender, [e-mail]

References

Schwender, H. and Ickstadt, K. (2008). Empirical Bayes Analysis of Single Nucleotide Polymorphisms. BMC Bioinformatics, 9:144.

See Also

logic.bagging, logicFS, vim.logicFS, vim.norm, vim.chisq

holgerschw/logicFS documentation built on April 15, 2020, 10:42 p.m.