# wilkinsonp: Combine p-values using Wilkinson's method In metap: Meta-Analysis of Significance Values

 wilkinsonp R Documentation

## Combine p-values using Wilkinson's method

### Usage

wilkinsonp(p, r = 1, alpha = 0.05, log.p = FALSE)
maximump(p, alpha = 0.05, log.p = FALSE)
minimump(p, alpha = 0.05, log.p = FALSE)
## S3 method for class 'wilkinsonp'
print(x, ...)
## S3 method for class 'maximump'
print(x, ...)
## S3 method for class 'minimump'
print(x, ...)


### Arguments

 p \sigvec r Use the \mjseqnrth smallest \mjseqnp value alpha The significance level log.p \logp x An object of class ‘wilkinsonp’ or of class ‘maximump’ or of class ‘minimump’ ... Other arguments to be passed through

### Details

Wilkinson \insertCitewilkinson51metap originally proposed his method in the context of simultaneous statistical inference: the probability of obtaining \mjseqnr or more significant statistics by chance in a group of \mjseqnk. The values are obtained from the Beta distribution, see pbeta.

If alpha is greater than unity it is assumed to be a percentage. Either values greater than 0.5 (assumed to be confidence coefficient) or less than 0.5 are accepted.

\lele

two

maximump and minimump each provide a wrapper for wilkinsonp for the special case when \mjeqnr = \mathrmlength(p)r = length(p) or \mjseqnr=1 respectively and each has its own print method. The method of minimum \mjseqnp is also known as Tippett's method \insertCitetippett31metap. \insertNoCitebecker94metap\insertNoCitebirnbaum54metap

\plotmethod \nocancel

### Value

An object of class ‘wilkinsonp’ and ‘metap’ or of class ‘maximump’ and ‘metap’ or of class ‘minimump’ and ‘metap’, a list with entries

 p The \mjseqnp-value resulting from the meta–analysis pr The \mjseqnrth smallest \mjseqnp value used r The value of \mjseqnr critp The critical value at which the \mjseqnrth value would have been significant for the chosen alpha validp The input vector with illegal values removed

### Note

The value of critp is always on the raw scale even if log.p has been set to TRUE

Michael Dewey

### References

\insertAllCited

See also plotp

### Examples

data(dat.metap)
beckerp <- dat.metap$beckerp minimump(beckerp) # signif = FALSE, critp = 0.0102, minp = 0.016 teachexpect <- dat.metap$teachexpect
minimump(teachexpect) # crit 0.0207, note Becker says minp = 0.0011
wilkinsonp(c(0.223, 0.223), r = 2) # Birnbaum, just signif
validity <- dat.metap$validity$p
minimump(validity) # minp = 0.00001, critp = 1.99 * 10^{-4}
minimump(c(0.0001, 0.0001, 0.9999, 0.9999)) # is significant
all.equal(exp(minimump(validity, log.p = TRUE)$p), minimump(validity)$p)
all.equal(exp(maximump(validity, log.p = TRUE)$p), maximump(validity)$p)


metap documentation built on March 18, 2022, 7:31 p.m.