wilc.ebam(data, cl, approx50 = TRUE, ties.method = c("min", "random", "max"), use.offset = TRUE, df.glm = 5, use.row = FALSE, rand = NA)
data
must correspond to a variable (e.g., a gene), and each column to a sample (i.e.\ an observation).ncol(data)
containing the class
labels of the samples. In the two class paired case, cl
can also
be a matrix with ncol(data)
rows and 2 columns. For details
on how cl
should be specified, see ebam
.TRUE
, the null distribution will be approximated by
the standard normal distribution. Otherwise, the exact null distribution is
computed. This argument will automatically be set to FALSE
if there
are less than 50 samples in each of the groups."min"
(default), "random"
, or "max"
. If
"random"
, the ranks of ties are randomly assigned. If "min"
or "max"
,
the ranks of ties are set to the minimum or maximum rank, respectively. For details, see
the help of rank
. If use.row = TRUE
, then ties.method = "max"
is used. For the handling of Zeros, see Details.TRUE
, the log-transformed
values of the null density is used as offset.TRUE
, rowWilcoxon
is used to compute the Wilcoxon
rank statistics.NA
, the random number generator
will be set into a reproducible state.ebam
.
approx50
to FALSE
.
Schwender, H., Krause, A. and Ickstadt, K. (2003). Comparison of the Empirical Bayes and the Significance Analysis of Microarrays. Technical Report, SFB 475, University of Dortmund, Germany.
ebam
, wilc.stat