Parametric, non-parametric, robust, and Bayesian random-effects meta-analysis.
meta_analysis(
data,
type = "parametric",
random = "mixture",
k = 2L,
conf.level = 0.95,
...
)The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):
statistic: the numeric value of a statistic
df: the numeric value of a parameter being modeled (often degrees
of freedom for the test)
df.error and df: relevant only if the statistic in question has
two degrees of freedom (e.g. anova)
p.value: the two-sided p-value associated with the observed statistic
method: the name of the inferential statistical test
estimate: estimated value of the effect size
conf.low: lower bound for the effect size estimate
conf.high: upper bound for the effect size estimate
conf.level: width of the confidence interval
conf.method: method used to compute confidence interval
conf.distribution: statistical distribution for the effect
effectsize: the name of the effect size
n.obs: number of observations
expression: pre-formatted expression containing statistical details
For examples, see data frame output vignette.
A data frame. It must contain columns named estimate (effect
sizes or outcomes) and std.error (corresponding standard errors). These
two columns will be used:
as yi and sei arguments in metafor::rma() (for parametric test)
or metaplus::metaplus() (for robust test)
as y and SE arguments in metaBMA::meta_random() (for Bayesian
test).
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
The type of random effects distribution. One of "normal", "t-dist", "mixture", for standard normal, \(t\)-distribution or mixture of normals respectively.
Number of digits after decimal point (should be an integer)
(Default: k = 2L).
Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals
will be computed.
Additional arguments passed to the respective meta-analysis function.
The table below provides summary about:
statistical test carried out for inferential statistics
type of effect size estimate and a measure of uncertainty for this estimate
functions used internally to compute these details
Hypothesis testing and Effect size estimation
| Type | Test | CI available? | Function used |
| Parametric | Pearson's correlation coefficient | Yes | correlation::correlation() |
| Non-parametric | Spearman's rank correlation coefficient | Yes | correlation::correlation() |
| Robust | Winsorized Pearson's correlation coefficient | Yes | correlation::correlation() |
| Bayesian | Bayesian Pearson's correlation coefficient | Yes | correlation::correlation() |