emfit(data, family, hypotheses, ...)
groupid
is required. See below.
Other families can be supplied by constructing them explicitly.
ebPatterns
type
=1, cluster
is a vector
specifying the fixed cluster membership for each gene; if
type
=2, cluster
specifies the number of clusters to
be fitted
type
=1, the cluster membership is fixed as input
cluster
; if type
=2, fit the data with a fixed number
of clusters
type
=2 and cluster
contains more than one integers. All numbers of clusters provided in
cluster
will be fitted and the one that minimizes
criterion
will be returned. Possible values now are
"BIC", "AIC" and "HQ"
type
=2. Specify the initial
clustering membership.
optim
for
finer control
show()
and used to generate posterior probabilities using
postprob
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 22:3899-3914.
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: 155-176.
Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.
ebPatterns
, ebarraysFamily-class
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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