parallelHolmMin {metapod} | R Documentation

## Combine parallel p-values with the minimum Holm approach

### Description

Combine p-values from parallel tests with the minimum Holm approach. Each group of p-values is defined from the corresponding entries across all vectors.

### Usage

    parallelHolmMin(
      p.values,
      weights = NULL,
      log.p = FALSE,
      min.n = 1,
      min.prop = 0.5
    )

### Arguments

`p.values` | A list of numeric vectors of the same length, containing the p-values to be combined.
`weights` | A numeric vector of positive weights, with one value per vector in `...`. Each weight is applied to all entries of itscorresponding vector, i.e., all p-values in that vector receive the same weight. Alternatively, a list of numeric vectors of weights with the same structure as `p.values`. Each p-value is then assigned the weight in the corresponding entry of `weights`. Alternatively `NULL`, in which case all p-values are assigned equal weight.
`log.p` | Logical scalar indicating whether the p-values in `p.values` are log-transformed.
`min.n` | Integer scalar specifying the minimum number of individual nulls to reject when testing the joint null.
`min.prop` | Numeric scalar in [0, 1], specifying the minimum proportion of individual nulls to reject when testing the joint null.

### Details

Here, the joint null hypothesis for each group is that fewer than N individual null hypotheses are false. The joint null is only rejected if N or more individual nulls are rejected; hence the “minimum” in the function name.

N is defined as the larger of `min.n` and the product of `min.prop` with the number of tests in the group (rounded up). This allows users to scale rejection of the joint null with the size of the group, while avoiding a too-low N when the group is small. Note that N is always capped at the total size of the group.

To compute the combined p-value, we apply the Holm-Bonferroni correction to all p-values in the set and take the N-th smallest value. This effectively recapitulates the step-down procedure where we reject individual nulls until we reach the N-th test. This method works correctly in the presence of dependencies between p-values.

If non-equal weights are provided, they are used to effectively downscale the p-values. This aims to redistribute the error rate across the individual tests, i.e., tests with lower weights are given fewer opportunities to drive acceptance of the joint null.

The representative test for each group is defined as that with the N-th smallest p-value, as this is directly used as the combined p-value. The influential tests for each group are defined as those with p-values no greater than the representative test's p-value. This is based on the fact that increasing them (e.g., by setting them to unity) would result in a larger combined p-value.

### Value

A list containing:

- `p.value`, a numeric vector of length equal to the length of each vector in `p.values`. This contains the combined p-value for each group, log-transformed if `log.p=TRUE`.

- `representative`, an integer scalar specifying the representative test in each group. Specifically, this refers to the index of the vector of `p.values` containing the representative test.

- `influential`, a list of logical vectors mirroring the structure of `p.values`. Entries are `TRUE` for any p-value that is deemed “influential” to the final per-group p-value.

### Author(s)

Aaron Lun

### References

Holm S (1979). A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65-70.

### See Also

`groupedHolmMin`, for a version that combines p-values based on a grouping factor.

`parallelWilkinson`, for a more relaxed version of this test when hypotheses are independent.

### Examples

    p1 <- rbeta(100, 0.8, 1)
    p2 <- runif(100)
    p3 <- rbeta(100, 0.5, 1)

    # Standard application:
    out <- parallelHolmMin(list(p1, p2, p3))
    str(out)

    # With vector-level weights:
    out <- parallelHolmMin(list(p1, p2, p3), weights=c(10, 20, 30))
    str(out)

    # With log p-values.
    out <- parallelHolmMin(list(log(p1), log(p2), log(p3)), log.p=TRUE)
    str(out)

[Package metapod version 1.2.0 Index]