postProbNull {OVESEG} | R Documentation |
This function computes posterior probabilities of each component null hypothesis given observed probes. Such probe-wise probabilities will be used as weights for aggregating permutations.
postProbNull(y, group, weights = NULL, alpha = "moderated", detail.return = TRUE)
y |
a numeric matrix containing log-expression or logCPM (log2-counts per million) values. Data frame or SummarizedExperiment object will be internally coerced into a matrix. Rows correspond to probes and columns to samples. Missing values are not permitted. |
group |
categorical vector or factor giving group membership of columns of y. At least two categories need to be presented. |
weights |
optional numeric matrix containing prior weights for each spot. |
alpha |
parameter specifying within-group variance estimator to be used.
'moderated': empirical Bayes moderated variance estimator as used in
|
detail.return |
a logical indicating whether more details (e.g. lfdr) will be returned. |
Posterior probabilities of each component null hypothesis given observed probes are estimated from ANOVA test on certain groups and local fdr. There are totally (K-1) null hypotheses, where K is the number of groups.
a list containing the following components:
W |
a matrix of posterior probabilities for each component null hypothesis given an observed probe. Rows correspond to probes and columns to one hypothesis. |
label |
a vector of group labels. |
groupOrder |
a matrix with each row being group indexes ordered based
on decreasing expression levels.
Group indexes are positions in |
F.p.value |
a matrix with each column giving p-values corresponding to F-statistics on certain groups. |
lfdr |
a matrix with each column being local false discovery rates estimated based on one column of weighted F.p.value matrix. |
fit |
a |
F.p.value
, lfdr
and fit
are returned only when
detail.return
is TRUE.
data(RocheBT) ppnull <- postProbNull(RocheBT$y, RocheBT$group, alpha='moderated') ppnull <- postProbNull(RocheBT$y, RocheBT$group, alpha=0.1)