Usage
z.ebam(data, cl, a0 = NULL, quan.a0 = NULL, B = 100, var.equal = FALSE, B.more = 0.1, B.max = 30000, n.subset = 10, fast = FALSE, n.interval = 139, df.ratio = NULL, rand = NA) z.find(data, cl, B = 100, var.equal = FALSE, B.more = 0.1, B.max = 30000)
Arguments
data
a matrix, data frame or ExpressionSet object. Each row of data
(or exprs(data)) must correspond to a variable (e.g., a gene),
and each column to a sample (i.e.\ observation).
cl
a numeric vector of length ncol(data) containing the class
labels of the samples. For details on how cl should be specified,
see ebam. a0
a numeric value specifying the fudge factor.
quan.a0
a numeric value between 0 and 1 specifying the quantile of the
standard deviations of the genes that is used as fudge factor.
B
an integer indicating how many permutations should be used in the
estimation of the null distribution.
var.equal
should the ordinary t-statistic assuming equal group variances
be computed? If FALSE (default), Welch's t-statistic will be computed.
B.more
a numeric value. If the number of all possible permutations is smaller
than or equal to (1+B.more)*B, full permutation will be done.
Otherwise, B permutations are used. This avoids that B permutations
will be used -- and not all permutations -- if the number of all possible
permutations is just a little larger than B.
B.max
a numeric value. If the number of all possible permutations is smaller
than or equal to B.max, B randomly selected permutations will be used
in the computation of the null distribution. Otherwise, B random draws
of the group labels are used. In the latter way of permuting, it is possible that
some of the permutations are used more than once.
n.subset
an integer specifying in how many subsets the B permutations
should be split when computing the permuted test scores. Note that the meaning
of n.subset differs between the SAM and the EBAM functions.
fast
if FALSE the exact number of permuted test scores that are
more extreme than a particular observed test score is computed for each of
the genes. If TRUE, a crude estimate of this number is used.
n.interval
the number of intervals used in the logistic regression with
repeated observations for estimating the ratio $f0/f$.
df.ratio
integer specifying the degrees of freedom of the natural cubic
spline used in the logistic regression with repeated observations.
rand
integer. If specified, i.e. not NA, the random number generator
will be set into a reproducible state.