metacor | R Documentation |
Calculation of common effect and random effects estimates for meta-analyses with correlations; inverse variance weighting is used for pooling.
metacor(
cor,
n,
studlab,
data = NULL,
subset = NULL,
exclude = NULL,
cluster = NULL,
rho = 0,
sm = gs("smcor"),
level = gs("level"),
common = gs("common"),
random = gs("random") | !is.null(tau.preset),
overall = common | random,
overall.hetstat = if (is.null(gs("overall.hetstat"))) common | random else
gs("overall.hetstat"),
prediction = gs("prediction") | !missing(method.predict),
method.tau = gs("method.tau"),
method.tau.ci = gs("method.tau.ci"),
level.hetstat = gs("level.hetstat"),
tau.preset = NULL,
TE.tau = NULL,
tau.common = gs("tau.common"),
method.I2 = gs("method.I2"),
level.ma = gs("level.ma"),
method.random.ci = gs("method.random.ci"),
adhoc.hakn.ci = gs("adhoc.hakn.ci"),
level.predict = gs("level.predict"),
method.predict = gs("method.predict"),
adhoc.hakn.pi = gs("adhoc.hakn.pi"),
seed.predict = NULL,
null.effect = 0,
method.bias = gs("method.bias"),
backtransf = gs("backtransf"),
text.common = gs("text.common"),
text.random = gs("text.random"),
text.predict = gs("text.predict"),
text.w.common = gs("text.w.common"),
text.w.random = gs("text.w.random"),
title = gs("title"),
complab = gs("complab"),
outclab = "",
label.left = gs("label.left"),
label.right = gs("label.right"),
col.label.left = gs("col.label.left"),
col.label.right = gs("col.label.right"),
subgroup,
subgroup.name = NULL,
print.subgroup.name = gs("print.subgroup.name"),
sep.subgroup = gs("sep.subgroup"),
test.subgroup = gs("test.subgroup"),
prediction.subgroup = gs("prediction.subgroup"),
seed.predict.subgroup = NULL,
byvar,
adhoc.hakn,
keepdata = gs("keepdata"),
warn.deprecated = gs("warn.deprecated"),
control = NULL,
...
)
cor |
Correlation. |
n |
Number of observations. |
studlab |
An optional vector with study labels. |
data |
An optional data frame containing the study information, i.e., cor and n. |
subset |
An optional vector specifying a subset of studies to be used. |
exclude |
An optional vector specifying studies to exclude from meta-analysis, however, to include in printouts and forest plots. |
cluster |
An optional vector specifying which estimates come from the same cluster resulting in the use of a three-level meta-analysis model. |
rho |
Assumed correlation of estimates within a cluster. |
sm |
A character string indicating which summary measure
( |
level |
The level used to calculate confidence intervals for individual studies. |
common |
A logical indicating whether a common effect meta-analysis should be conducted. |
random |
A logical indicating whether a random effects meta-analysis should be conducted. |
overall |
A logical indicating whether overall summaries should be reported. This argument is useful in a meta-analysis with subgroups if overall results should not be reported. |
overall.hetstat |
A logical value indicating whether to print heterogeneity measures for overall treatment comparisons. This argument is useful in a meta-analysis with subgroups if heterogeneity statistics should only be printed on subgroup level. |
prediction |
A logical indicating whether a prediction interval should be printed. |
method.tau |
A character string indicating which method is
used to estimate the between-study variance |
method.tau.ci |
A character string indicating which method is
used to estimate the confidence interval of |
level.hetstat |
The level used to calculate confidence intervals for heterogeneity statistics. |
tau.preset |
Prespecified value for the square root of the
between-study variance |
TE.tau |
Overall treatment effect used to estimate the between-study variance tau-squared. |
tau.common |
A logical indicating whether tau-squared should be the same across subgroups. |
method.I2 |
A character string indicating which method is
used to estimate the heterogeneity statistic I |
level.ma |
The level used to calculate confidence intervals for meta-analysis estimates. |
method.random.ci |
A character string indicating which method
is used to calculate confidence interval and test statistic for
random effects estimate (see |
adhoc.hakn.ci |
A character string indicating whether an
ad hoc variance correction should be applied in the case
of an arbitrarily small Hartung-Knapp variance estimate (see
|
level.predict |
The level used to calculate prediction interval for a new study. |
method.predict |
A character string indicating which method is
used to calculate a prediction interval (see
|
adhoc.hakn.pi |
A character string indicating whether an
ad hoc variance correction should be applied for
prediction interval (see |
seed.predict |
A numeric value used as seed to calculate
bootstrap prediction interval (see |
null.effect |
A numeric value specifying the effect under the null hypothesis. |
method.bias |
A character string indicating which test is to
be used. Either |
backtransf |
A logical indicating whether results for Fisher's
z transformed correlations ( |
text.common |
A character string used in printouts and forest plot to label the pooled common effect estimate. |
text.random |
A character string used in printouts and forest plot to label the pooled random effects estimate. |
text.predict |
A character string used in printouts and forest plot to label the prediction interval. |
text.w.common |
A character string used to label weights of common effect model. |
text.w.random |
A character string used to label weights of random effects model. |
title |
Title of meta-analysis / systematic review. |
complab |
Comparison label. |
outclab |
Outcome label. |
label.left |
Graph label on left side of null effect in forest plot. |
label.right |
Graph label on right side of null effect in forest plot. |
col.label.left |
The colour of the graph label on the left side of the null effect. |
col.label.right |
The colour of the graph label on the right side of the null effect. |
subgroup |
An optional vector to conduct a meta-analysis with subgroups. |
subgroup.name |
A character string with a name for the subgroup variable. |
print.subgroup.name |
A logical indicating whether the name of the subgroup variable should be printed in front of the group labels. |
sep.subgroup |
A character string defining the separator between name of subgroup variable and subgroup label. |
test.subgroup |
A logical value indicating whether to print results of test for subgroup differences. |
prediction.subgroup |
A logical indicating whether prediction intervals should be printed for subgroups. |
seed.predict.subgroup |
A numeric vector providing seeds to calculate bootstrap prediction intervals within subgroups. Must be of same length as the number of subgroups. |
byvar |
Deprecated argument (replaced by 'subgroup'). |
adhoc.hakn |
Deprecated argument (replaced by 'adhoc.hakn.ci'). |
keepdata |
A logical indicating whether original data (set) should be kept in meta object. |
warn.deprecated |
A logical indicating whether warnings should be printed if deprecated arguments are used. |
control |
An optional list to control the iterative process to
estimate the between-study variance |
... |
Additional arguments (to catch deprecated arguments). |
Common effect and random effects meta-analysis of correlations
based either on Fisher's z transformation of correlations (sm
= "ZCOR"
) or direct combination of (untransformed) correlations
(sm = "COR"
) (see Cooper et al., 2009, p264-5 and
p273-4). Only few statisticians would advocate the use of
untransformed correlations unless sample sizes are very large (see
Cooper et al., 2009, p265). The artificial example given below
shows that the smallest study gets the largest weight if
correlations are combined directly because the correlation is
closest to 1.
A three-level random effects meta-analysis model (Van den Noortgate
et al., 2013) is utilised if argument cluster
is used and at
least one cluster provides more than one estimate. Internally,
rma.mv
is called to conduct the analysis and
weights.rma.mv
with argument type =
"rowsum"
is used to calculate random effects weights.
Default settings are utilised for several arguments (assignments
using gs
function). These defaults can be changed for
the current R session using the settings.meta
function.
Furthermore, R function update.meta
can be used to
rerun a meta-analysis with different settings.
Argument subgroup
can be used to conduct subgroup analysis for
a categorical covariate. The metareg
function can be
used instead for more than one categorical covariate or continuous
covariates.
Arguments subset
and exclude
can be used to exclude
studies from the meta-analysis. Studies are removed completely from
the meta-analysis using argument subset
, while excluded
studies are shown in printouts and forest plots using argument
exclude
(see Examples in metagen
).
Meta-analysis results are the same for both arguments.
Internally, both common effect and random effects models are
calculated regardless of values choosen for arguments
common
and random
. Accordingly, the estimate
for the random effects model can be extracted from component
TE.random
of an object of class "meta"
even if
argument random = FALSE
. However, all functions in R
package meta will adequately consider the values for
common
and random
. E.g. functions
print.meta
and forest.meta
will not
print results for the random effects model if random =
FALSE
.
A prediction interval will only be shown if prediction =
TRUE
.
An object of class c("metacor", "meta")
with corresponding
generic functions (see meta-object
).
The function metagen
is called internally to
calculate individual and overall treatment estimates and standard
errors.
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de
Cooper H, Hedges LV, Valentine JC (2009): The Handbook of Research Synthesis and Meta-Analysis, 2nd Edition. New York: Russell Sage Foundation
Van den Noortgate W, López-López JA, Marín-Martínez F, Sánchez-Meca J (2013): Three-level meta-analysis of dependent effect sizes. Behavior Research Methods, 45, 576–94
meta-package
, update.meta
,
metacont
, metagen
,
print.meta
m1 <- metacor(c(0.85, 0.7, 0.95), c(20, 40, 10))
# Print correlations (back transformed from Fisher's z
# transformation)
#
m1
# Print Fisher's z transformed correlations
#
print(m1, backtransf = FALSE)
# Forest plot with back transformed correlations
#
forest(m1)
# Forest plot with Fisher's z transformed correlations
#
forest(m1, backtransf = FALSE)
m2 <- update(m1, sm = "cor")
m2
## Not run:
# Identical forest plots (as back transformation is the identity
# transformation)
forest(m2)
forest(m2, backtransf = FALSE)
## End(Not run)
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