| min_prior {modelbpp} | R Documentation |
Minimum Prior
Description
Find the minimum prior probability required to achieve the desired BIC posterior probability.
Usage
min_prior(bic, bpp_target, target_name = "original")
Arguments
bic |
A named vector of BIC values for a set of models. |
bpp_target |
A value from zero to 1. The desired BIC posterior probability. |
target_name |
The name of the
original model, as appeared in the
names of |
Details
It assumes that all models other than the original model have the same prior probabilities.
This function is called by
model_set() or print.model_set()
and usually users do
not need to call it. It is exported
for advanced users.
Value
A scalar. The required prior probability.
Author(s)
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
References
Wu, H., Cheung, S. F., & Leung, S. O. (2020). Simple use of BIC to assess model selection uncertainty: An illustration using mediation and moderation models. Multivariate Behavioral Research, 55(1), 1–16. doi:10.1080/00273171.2019.1574546
See Also
model_set() and
print.model_set()
Examples
library(lavaan)
dat <- dat_path_model
mod <-
"
x3 ~ a*x1 + b*x2
x4 ~ a*x1
ab := a*b
"
fit <- sem(mod, dat_path_model, fixed.x = TRUE)
out <- model_set(fit)
min_prior(out$bic, bpp_target = .8)