Description Usage Arguments Details References See Also Examples
Wrapper with default prior for Bayesian metaanalysis. Since version 0.6.6, the default priors for Cohen's d have been changed from a normal distribution with scale=0.3 to a Cauchy distribution with scale=0.707. Moreover, scale adjustments were implemented when using Fisher's z or log oddsratios.
1  meta_default(y, SE, labels, data, field = "psychology", effect = "d", ...)

y 
effect size per study. Can be provided as (1) a numeric vector, (2)
the quoted or unquoted name of the variable in 
SE 
standard error of effect size for each study. Can be a numeric
vector or the quoted or unquoted name of the variable in 
labels 
optional: character values with study labels. Can be a
character vector or the quoted or unquoted name of the variable in

data 
data frame containing the variables for effect size 
field 
either 
effect 
the type of effect size used in the metaanalysis: either
Cohen's d ( 
... 
further arguments passed to 
The prior distribution depends on the scale of the effect size that is used in
the metaanalysis (Cohen's d, Fisher's z, or log odds ratio). To ensure that
the results are comparable when transforming between different effect sizes
(e.g., using the function transform_es
), it is necessary to
adjust the prior distributions. The present adjustments merely use a linear
rescaling of the priors to achieve approximately invariant results when
using different types of effect sizes.
The distribution of Fisher's z is approximately half as wide as the distribution of Cohen's d and hence the prior scale parameter is divided by two.
The distribution of the log odds ratio is approximately
pi / sqrt(3) = 1.81
times as wide as the distribution of Cohen's d.
Hence, the prior scale parameter is doubled by this factor.
For field = "psychology"
, this results in the following defaults:
effect = "d"
(Cohen's d): Cauchy distribution with scale=0.707 on the overall
effect size (parameter d) and inverse gamma distribution with shape=1 and
scale=0.15 on the standard deviation of effect sizes across studies (parameter tau).
effect = "z"
(Fisher's z): Cauchy distribution with scale=0.354 on d and
inverse gamma with shape=1 and scale=0.075 on tau.
effect = "logOR"
(log odds ratio): Cauchy distribution with scale=1.283 on d and
inverse gamma with shape=1 and scale=0.272 on tau.
Currently, the same priors are used when specifying field = "medicine"
.
Default prior distributions can be plotted using plot_default
.
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Converting among effect sizes. In Introduction to MetaAnalysis (pp. 45–49). John Wiley & Sons, Ltd. doi: 10.1002/9780470743386.ch7
Gronau, Q. F., Erp, S. V., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E.J. (2017). A Bayesian modelaveraged metaanalysis of the power pose effect with informed and default priors: the case of felt power. Comprehensive Results in Social Psychology, 2(1), 123138. doi: 10.1080/23743603.2017.1326760
meta_bma
, plot_default
, transform_es
1 2 3 4 5 6 7  data(towels)
set.seed(123)
md < meta_default(logOR, SE, study, towels,
field = "psychology", effect = "logOR"
)
md
plot_forest(md)

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