vim.ebam {logicFS} | R Documentation |
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.
vim.ebam(object, data = NULL, cl = NULL, nameEBAM = NULL, ...)
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. |
nameEBAM |
a character string. If specified, then the output of the EBAM analysis is stored under this name in the global environment. |
... |
further arguments of ebam and cat.ebam . For details, see the help files
of these functions from the package siggenes . |
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.perm
),
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
.
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. |
Holger Schwender, holger.schwender@udo.edu
Schwender, H. and Ickstadt, K. (2008). Empirical Bayes Analysis of Single Nucleotide Polymorphisms. BMC Bioinformatics, 9:144.
logic.bagging
, logicFS
,
vim.logicFS
, vim.norm
, vim.chisq