modelbpp (version 0.1.6)

min_prior: 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")

Value

A scalar. The required prior probability.

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 bic.

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

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.

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. tools:::Rd_expr_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)