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.