logitp {metap}

Combine p values using the logit method

Description

Combine \(p\) values using logit method

Usage

logitp(p, log.p = FALSE)
## S3 method for class 'logitp'
print(x, ...)

Arguments

p

A vector of significance values

log.p

Logical, if TRUE result is returned as log(p)

x

An object of class ‘logitp’

...

Other arguments to be passed through

Details

Defined as \[t = - \frac{\sum_{i=1}^k \log\frac{p_i}{1 - p_i}}{C}\] where \[C = \sqrt\frac{k \pi^2 (5 k + 2)}{3(5 k + 4)}\] and \(k\) is the number of studies.

The values of \(p_i\) should be such that \(0 < p_i < 1\) and a warning is given if that is not true. A warning is given if, possibly as a result of removing illegal values, fewer than two values remain and the return values are set to NA.

The plot method for class ‘metap’ calls plotp on the valid p-values.

Value

An object of class ‘logitp’ and ‘metap’, a list with entries

t

Value of Student's \(t\)

df

Associated degrees of freedom

p

Associated \(p\)-value

validp

The input vector with illegal values removed

Author(s)

Michael Dewey

References

Becker BJ (1994). “Combining significance levels.” In Cooper H, Hedges LV (eds.), A handbook of research synthesis, 215–230. Russell Sage, New York.

See Also

See also plotp

Examples

data(dat.metap)
teachexpect <- dat.metap$teachexpect
logitp(teachexpect) # t = 2.763, df = 99, p = 0.0034, from Becker
beckerp <- dat.metap$beckerp
logitp(beckerp) # t = 1.62, df = 29, NS, from Becker
validity <- dat.metap$validity$p
logitp(validity) # t = 9.521, df = 104, p = 3.89 * 10^{-16}
all.equal(exp(logitp(validity, log.p = TRUE)$p), logitp(validity)$p)