meta_analysis {statsExpressions} | R Documentation |
Parametric, non-parametric, robust, and Bayesian random-effects meta-analysis.
meta_analysis( data, type = "parametric", random = "mixture", k = 2L, conf.level = 0.95, ... )
data |
A data frame. It must contain columns named
|
type |
A character specifying the type of statistical approach:
You can specify just the initial letter. |
random |
The type of random effects distribution. One of "normal", "t-dist", "mixture", for standard normal, t-distribution or mixture of normals respectively. |
k |
Number of digits after decimal point (should be an integer)
(Default: |
conf.level |
Scalar between |
... |
Additional arguments passed to the respective meta-analysis function. |
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.
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() |
Important: The function assumes that you have already downloaded the
needed package ({metafor}
, {metaplus}
, or {metaBMA}
) for meta-analysis.
If they are not available, you will be asked to install them.
# setup set.seed(123) library(statsExpressions) # a data frame with estimates and standard errors # (`mag` dataset from `{metaplus}`) df <- tibble::tribble( ~study, ~estimate, ~std.error, "Abraham", -0.83, 1.247, "Bertschat", -1.056, 0.414, "Ceremuzynski", -1.278, 0.808, "Feldstedt", -0.043, 1.429, "Golf", 0.223, 0.489, "ISIS-4", -2.407, 1.072, "LIMIT-2", -1.28, 1.193, "Morton", -1.191, 1.661, "Pereira", -0.695, 0.536, "Rasmussen", -2.208, 1.109, "Schechter", -2.038, 0.78, "Schechter 1", -0.85, 0.618, "Schechter 2", -0.793, 0.625, "Singh", -0.299, 0.146, "Smith", -1.57, 0.574, "Thogersen", 0.057, 0.031 ) # parametric meta_analysis(df) # robust meta_analysis(df, type = "random", random = "normal") # Bayesian meta_analysis(df, type = "bayes")