print.meta: Print and summary method for objects of class meta

Description

Print and summary method for objects of class meta.

Usage

## S3 method for class 'meta':
print(x, sortvar, level=x$level, level.comb=x$level.comb,
      details=FALSE, ma=TRUE, digits=max(4, .Options$digits - 3), ...)
## S3 method for class 'meta':
summary(object, byvar, bylab, bystud=FALSE,
        level=object$level, level.comb=object$level.comb, warn=TRUE, ...)
## S3 method for class 'summary.meta':
print(x, digits = max(3, .Options$digits - 3),
      print.byvar = TRUE, ...)

Arguments

An object of class meta or summary.meta.

object

An object of class meta.

sortvar

An optional vector used to sort the individual studies (must be of same length as x$TE).

level

The level used to calculate confidence intervals for individual studies.

level.comb

The level used to calculate confidence intervals for pooled estimates.

details

A logical indicating whether further details of individual studies should be printed.

A logical indicating whether the summary results of the meta-analysis should be printed.

byvar

An optional vector containing grouping information (must be of same length as x$TE).

bylab

A character string with a label for the grouping variable.

bystud

A logical indicating whether results of individual studies should be printed by grouping variable.

digits

Minimal number of significant digits, see print.default.

print.byvar

A logical indicating whether the name of the grouping variable should be printed in front of the group labels.

warn

A logical indicating whether the use of summary.meta in connection with metacum or metainf should result in a warning.

...

other arguments

Value

A list is returned by the function summary.meta with the following elements:
studyResults for individual studies (a list with elements TE, seTE, lower, upper, z, p, level).
fixedResults for fixed effect model (a list with elements TE, seTE, lower, upper, z, p, level).
randomResults for random effects model (a list with elements TE, seTE, lower, upper, z, p, level).
kNumber of studies combined in meta-analysis.
QHeterogeneity statistic Q.
tauSquare-root of between-study variance (moment estimator of DerSimonian-Laird).
HHeterogeneity statistic H (a list with elements TE, lower, upper).
I2Heterogeneity statistic I2 (a list with elements TE, lower, upper), see Higgins & Thompson (2002).
k.allTotal number of trials.
Q.CMHCochrane-Mantel-Haenszel heterogeneity statistic.
smA character string indicating underlying summary measure.
methodA character string with the pooling method.
callFunction call.
ci.labLabel for confidence interval.
withinResults within groups (a list with elements TE, seTE, lower, upper, z, p, level) - if byvar is not missing.
k.wNumber of studies combined within groups - if byvar is not missing.
Q.wHeterogeneity statistic Q within groups - if byvar is not missing.
bylabLabel for grouping variable - if byvar is not missing.
by.levsLevels of grouping variable - if byvar is not missing.

References

Cooper H & Hedges LV (1994), The Handbook of Research Synthesis. Newbury Park, CA: Russell Sage Foundation.

Higgins JPT & Thompson SG (2002), Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21, 1539--1558.

Examples

Run this code

data(Fleiss93cont)
meta1 <- metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c, data=Fleiss93cont, sm="SMD")
summary(meta1)
summary(meta1, byvar=c(1,2,1,1,2), bylab="group")

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