wilkinsonp: Combine p-values using Wilkinson's method

In metap: Meta-Analysis of Significance Values

Combine p-values using Wilkinson's method

Description

Combine \mjseqnp-values using Wilkinson's method\loadmathjax

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

Author(s)

Michael Dewey

References

\insertAllCited

See Also

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 Oct. 9, 2023, 5:10 p.m.