prior_informed {RoBSA} | R Documentation |
Creates an informed prior distribution based on research
Description
prior_informed
creates an informed prior distribution based on past
research. The prior can be visualized by the plot
function.
Usage
prior_informed(name, parameter = NULL, type = "smd")
Arguments
name |
name of the prior distribution. There are many options based on prior psychological or medical research. For psychology, the possible options are
For medicine, the possible options are based on Bartoš et al. (2021)
who developed empirical prior distributions for the effect size and heterogeneity parameters of the
continuous standardized outcomes based on the Cochrane database of systematic reviews.
Use |
parameter |
parameter name describing what prior distribution is supposed to be produced in cases
where the |
type |
prior type describing what prior distribution is supposed to be produced in cases
where the |
Details
Further details can be found in van Erp et al. (2017), Gronau et al. (2017), and Bartoš et al. (2021).
Value
prior_informed
returns an object of class 'prior'.
References
Bartoš F, Gronau QF, Timmers B, Otte WM, Ly A, Wagenmakers E (2021).
“Bayesian model-averaged meta-analysis in medicine.”
Statistics in Medicine.
doi:10.1002/sim.9170.
Gronau QF, Van Erp S, Heck DW, Cesario J, Jonas KJ, Wagenmakers E (2017).
“A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: The case of felt power.”
Comprehensive Results in Social Psychology, 2(1), 123–138.
doi:10.1080/23743603.2017.1326760.
van Erp S, Verhagen J, Grasman RP, Wagenmakers E (2017).
“Estimates of between-study heterogeneity for 705 meta-analyses reported in Psychological Bulletin from 1990–2013.”
Journal of Open Psychology Data, 5(1).
doi:10.5334/jopd.33.
See Also
prior()
, prior_informed_medicine_names
Examples
# prior distribution representing expected effect sizes in social psychology
# based on prior elicitation with dr. Oosterwijk
p1 <- prior_informed("Oosterwijk")
# the prior distribution can be visualized using the plot function
# (see ?plot.prior for all options)
plot(p1)
# empirical prior distribution for the standardized mean differences from the oral health
# medical subfield based on meta-analytic effect size estimates from the
# Cochrane database of systematic reviews
p2 <- prior_informed("Oral Health", parameter = "effect", type = "smd")
print(p2)