`R/nest_confects.R`

`nest_confects.Rd`

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. |

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.

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.

# 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)#> $table #> rank index confect #> 1 5 2.673 #> 2 4 1.946 #> 3 3 1.119 #> 4 2 0.249 #> 5 1 NA #> 4 of 5 non-zero effect size at FDR 0.05