parallelPearson: Combine parallel p-values with Pearson's method

In LTLA/metapod: Meta-Analyses on P-Values of Differential Analyses

Combine parallel p-values with Pearson's method

Description

Combine p-values from parallel tests with Pearson's method. Each group of p-values is defined from the corresponding entries across all vectors.

Usage

parallelPearson(p.values, log.p = FALSE)

Arguments

p.values	A list of numeric vectors of the same length, containing the p-values to be combined.
log.p	Logical scalar indicating whether the p-values in p.values are log-transformed.

Details

Here, the joint null hypothesis for each group is that all of the individual null hypotheses are true. Pearson's method combines information from all individual nulls to determine if the joint null should be rejected. Compared to Stouffer's and Pearson's methods, Pearson's method is more sensitive to the largest individual p-value. This method is only applicable to independent tests and no weights are considered.

The representative test for each group is defined as the test with the largest p-value, as this has the greatest effect on the combined p-value. All tests for each group are considered to be influential as increasing any of them (e.g., 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 Pearson 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 combined p-value.

Author(s)

Aaron Lun

References

Pearson K (1934). On a new method of deternining “goodness of fit.” Biometrika 26, 425-442.

See Also

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

parallelFisher and parallelStouffer, for different approaches to testing a joint null of independent hypotheses.

Examples

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

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

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

LTLA/metapod documentation built on Jan. 19, 2024, 11:49 p.m.