```
filtered_p(filter, test, theta, data, method = "none")
filtered_R(alpha, filter, test, theta, data, method = "none")
```

alpha

A cutoff to which p-values, possibly adjusted for multiple testing,
will be compared.

filter

A vector of stage-one filter statistics, or a function which is able
to compute this vector from

`data`

, if `data`

is supplied.test

A vector of unadjusted p-values, or a function which is able
to compute this vector from the filtered portion of limma t-statistic is an
example.)

`data`

, if
`data`

is supplied. The option to supply a function is useful
when the value of the test statistic depends on which hypotheses are
filtered out at stage one. (The theta

A vector with one or more filtering fractions to consider. Actual
cutoffs are then computed internally by applying

`quantile`

to the filter statistics contained in (or
produced by) the `filter`

argument.data

If

`filter`

and/or `test`

are functions rather than
vectors of statistics, they will be applied to `data`

. The
functions will be passed the whole `data`

object, and must work
over rows, etc. themselves as appropriate.method

The unadjusted p-values contained in (or produced by) stats package. See the

`test`

will be adjusted for multiple testing after filtering, using the
`p.adjust`

function in the `method`

argument there for options.- For
`filtered_p`

, a matrix of p-values, possible adjusted for multiple testing, with one row per null hypothesis and one column per filtering fraction given in`theta`

. For a given column, entries which have been filtered out are`NA`

.For

`filtered_R`

, a count of the entries in the`filtered_p`

result which are less than`alpha`

.

`rejection_plot`

for visualization of
`filtered_p`

results.`# See the vignette: Diagnostic plots for independent filtering`

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