Implements the EM algorithm for gene expression mixture model
1 2 3 4 
data 
a matrix 
family 
an object of class “ebarraysFamily” or a character string which can
be coerced to one. Currently, only the characters "GG" and "LNN", and
"LNNMV" are valid. For LNNMV, a 
hypotheses 
an object of class “ebarraysPatterns” representing the hypotheses
of interest. Such patterns can be generated by the function

... 
other arguments. These include:

There are many optional arguments. So a call might look more like this:
emfit(data, family, hypotheses, cluster, type=2, criterion="BIC", cluster.init = NULL, num.iter = 20, verbose = getOption("verbose"), optim.control = list(), ...)
an object of class “ebarraysEMfit”, that can be summarized by
show()
and used to generate posterior probabilities using
postprob
Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and Christina Kendziorski
Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R. (2001). On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8:3752.
Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:38993914.
Newton, M.A. and Kendziorski, C.M. Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.
Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5: 155176.
Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 10891098.
ebPatterns
, ebarraysFamilyclass
1 2 3 4 5 6  data(sample.ExpressionSet) ## from Biobase
eset < exprs(sample.ExpressionSet)
patterns < ebPatterns(c("1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
"1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2"))
gg.fit < emfit(data = eset, family = "GG", hypotheses = patterns, verbose = TRUE)
show(gg.fit)

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