| higgins {heterometa} | R Documentation |
Compute heterogeneity statistics after Higgins
Description
Computes various statistics recommended by Higgins et al for quantifying heterogeneity in meta-analysis
Usage
higgins(Q = NULL, k = NULL, pval = NULL, slab = NULL, conflevel = 0.95)
## S3 method for class 'higgins'
print(x, type = "I2", na.print = "", ...)
Arguments
Q |
Numeric: a vector of heterogeneity \(\chi^2\) from the meta–analyses |
k |
Numeric: a vector of number of studies in each meta-analysis |
pval |
Numeric: a vector of \(p\) values |
slab |
Character: a vector of labels for the meta-analyses |
conflevel |
Numeric: a vector of confidence levels |
x |
An object of class |
type |
One of "H", "I2", "both" |
na.print |
What to print instead of NA |
... |
Argument(s) to be passed through |
Details
Either Q or pval should be provided.
Limited error checks for illegal parameters are performed.
A warning is given if any conflevel is \(<0.5\).
A print method is provided.
Value
A list of type higgins containing
H |
A data frame with columns Q, k, H, ll, ul, where ll and ul are the confidence limits |
I2 |
A data frame with columns Q, k, I2, ll, ul |
call |
The call |
Author(s)
Michael Dewey
References
Higgins JPT, Thompson SG (2002). “Quantifying heterogeneity in a meta–analysis.” Statistics in Medicine, 21, 1539–1558. doi:10.1002/sim.1186.
Examples
higgins(14.4, 24) # 1 (1, 1.34) 0 (0, 45)
higgins(14.1, 11) # 1.19 (1, 1.64) 20 (0, 65) probably a typo for 29
higgins(81.5, 19) # 2.13 (1.71, 2.64) 78 (66, 86)
higgins(41.5, 7) # 2.63 (1.90, 3.65) 86 (72, 92)
higgins(130.3, 3) # 8.07 (6.08, 10.72) 98 (97, 99)
data(dat.higgins02)
with(dat.higgins02, higgins(Q, trials, slab = rownames(dat.higgins02)))