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)

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