| wilkinsonp {metap} | R Documentation |
Combine p-values using Wilkinson's method
wilkinsonp(p, r = 1, alpha = 0.05) maximump(p, alpha = 0.05) minimump(p, alpha = 0.05) ## S3 method for class 'wilkinsonp' print(x, ...) ## S3 method for class 'maximump' print(x, ...) ## S3 method for class 'minimump' print(x, ...)
p |
A vector of p-values |
r |
Use the rth smallest p value |
alpha |
The significance level |
x |
An object of class ‘ |
... |
Other arguments to be passed through |
Wilkinson originally proposed his method in the context of
simultaneous statistical inference: the probability
of obtaining r or more significant statistics by
chance in a group of k.
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.
The values of p should be such that 0<=p<=1 and a warning is issued if that is not true. An error results if possibly as a result of deletions fewer than two studies remain.
maximump and
minimump each provide a wrapper for wilkinsonp
for the special case when r = length(p)
or r=1 respectively and each has its own
print method.
The method of minimum p is also known as Tippett'smethod.
The plot method for class ‘metap’
calls schweder on the valid
p-values.
Inspection of the p-values is recommended as extreme values
in opposite directions do not cancel out.
See last example.
This may not be what you want.
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 p-value resulting from the meta–analysis |
pr |
The rth smallest p value used |
r |
The value of r |
critp |
The critical value at which the rth value
would have been significant for the chosen |
validp |
The input vector with illegal values removed |
Michael Dewey
Becker, B J. Combining significance levels. In Cooper, H and Hedges, L V, editors A handbook of research synthesis, chapter 15, pages 215–230. Russell Sage, New York, 1994.
Birnbaum, A. Combining independent tests of significance. Journal of the American Statistical Association, 49:559–574, 1954.
Wilkinson, B. A statistical consideration in psychological research. Psychological Bulletin, 48:156–158, 1951.
See also schweder
data(beckerp)
minimump(beckerp) # signif = FALSE, critp = 0.0102, minp = 0.016
data(teachexpect)
minimump(teachexpect) # crit 0.0207, note Becker says minp = 0.0011
wilkinsonp(c(0.223, 0.223), r = 2) # Birnbaum, just signif
data(validity)
minimump(validity) # minp = 0.00001, critp = 1.99 * 10^{-4}
minimump(c(0.0001, 0.0001, 0.9999, 0.9999)) # is significant