wilkinsonp | R Documentation |
Combine \mjseqnp-values using Wilkinson's method\loadmathjax
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, ...)
p |
\sigvec |
r |
Use the \mjseqnrth smallest \mjseqnp value |
alpha |
The significance level |
log.p |
\logp |
x |
An object of class ‘ |
... |
Other arguments to be passed through |
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.
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
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 |
validp |
The input vector with illegal values removed |
The value of critp
is always on the raw scale even
if log.p
has been set to TRUE
Michael Dewey
See also plotp
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.