parallelPearson {metapod}R Documentation

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:

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)


[Package metapod version 1.0.0 Index]