View source: R/nest_confects.R
nest_confects | R Documentation |
Find sets of discoveries for a range of effect sizes, controlling the False Discovery Rate (FDR) for each set.
nest_confects(n, pfunc, fdr = 0.05, step = 0.001, full = FALSE)
n |
Number of items being tested. |
pfunc |
A |
fdr |
False Discovery Rate to control for. |
step |
Granularity of effect sizes to test. |
full |
If TRUE, also include FDR-adjusted p-value that effect size is non-zero. Note that this is against the spirit of the topconfects approach. |
This is a general purpose function, which can be applied to any method of calculting p-values (supplied as a function argument) for the null hypothesis that the effect size is smaller than a given amount.
A "Topconfects" object, containing a table of results and various associated information.
The most important part of this object is the $table element, a data frame with the following columns:
rank
- Ranking by confect
and for equal
confect
by p-value at that effect size.
index
- Number of
the test, between 1 and n.
confect
- CONfident efFECT size.
The usage is as follows: To find a set of tests which have effect size
greater than x with the specified FDR, take the rows with abs(confect)
>= x
. Once the set is selected, the confect values provide confidence bounds
on the effect size with False Coverage-statement Rate (FCR) at the same level
as the FDR.
One may essentially take the top however many rows of the data frame and
these will be the best set of results of that size to dependably have an
effect size that is as large as possible. However if some genes have the same
abs(confect)
, all or none should be selected.
Some rows in the output may be given the same confect
, even if
step
is made small. This is an expected behaviour of the algorithm.
(This is similar to FDR adjustment of p-values sometimes resulting in a run
of the same adjusted p-value, even if all the input p-values are distinct.)
Some wrappers around this function may add a sign to the confect
column, if it makes sense to do so. They will also generally add an
effect
column, containing an estimate of the effect size that aims to
be unbiassed rather than a conservative lower bound.
# Find largest positive z-scores in a collection,
# and place confidence bounds on them that maintain FDR 0.05.
z <- c(1,2,3,4,5)
pfunc <- function(i, effect_size) {
pnorm(z[i], mean=effect_size, lower.tail=FALSE)
}
nest_confects(length(z), pfunc, fdr=0.05)
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