Usage
ebamControl(p0 = NA, p0.estimation = c("splines", "interval", "adhoc"), lambda = NULL, ncs.value = "max", use.weights = FALSE)
find.a0Control(p0.estimation = c("splines", "adhoc", "interval"), lambda = NULL, ncs.value = "max", use.weights = FALSE, n.chunk = 5, n.interval = 139, df.ratio = NULL)
Arguments
p0
a numeric value specifying the prior probability $p0$ that a gene is not
differentially expressed. If NA
, p0
will be estimated automatically.
p0.estimation
either "splines"
(default), "interval"
, or "adhoc"
.
If "splines"
, the spline based method of Storey and Tibshirani (2003) is used to estimate
$p0$. If "adhoc"
("interval"
), the adhoc (interval based) method
proposed by Efron et al.\ (2001) is used to estimate $p0$.
lambda
a numeric vector or value specifying the $lambda$ values used in
the estimation of $p0$. If NULL
, lambda
is set to seq(0, 0.95, 0.05)
if p0.estimation = "splines"
, and to 0.5
if p0.estimation = "interval"
.
Ignored if p0.estimation = "adhoc"
. For details, see pi0.est
. ncs.value
a character string. Only used if p0.estimation = "splines"
and
lambda
is a vector. Either "max"
or "paper"
. For details, see
pi0.est
. use.weights
should weights be used in the spline based estimation of $p0$? If
TRUE
, 1 - lambda
is used as weights. For details, see pi0.est
. n.chunk
an integer specifying in how many subsets the B
permutations
should be split when computing the permuted test scores.
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.