Minimum Prior

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 bic.

Value

A scalar. The required prior probability.

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. doi:10.1080/00273171.2019.1574546

See also

model_set() and print.model_set()

Author

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

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)
#> 
#> Generate 2 less restrictive model(s):
#> 
  |                                                  | 0 % ~calculating  
  |+++++++++++++++++++++++++                         | 50% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
#> 
#> Generate 2 more restrictive model(s):
#> 
  |                                                  | 0 % ~calculating  
  |+++++++++++++++++++++++++                         | 50% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
#> 
#> Check for duplicated models (5 model[s] to check):
#> 
  |                                                        
  |                                                  |   0%
  |                                                        
  |+++++                                             |  10%
  |                                                        
  |++++++++++                                        |  20%
  |                                                        
  |+++++++++++++++                                   |  30%
  |                                                        
  |++++++++++++++++++++                              |  40%
  |                                                        
  |+++++++++++++++++++++++++                         |  50%
  |                                                        
  |++++++++++++++++++++++++++++++                    |  60%
  |                                                        
  |+++++++++++++++++++++++++++++++++++               |  70%
  |                                                        
  |++++++++++++++++++++++++++++++++++++++++          |  80%
  |                                                        
  |+++++++++++++++++++++++++++++++++++++++++++++     |  90%
  |                                                        
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
#> 
#> Fit the 5 model(s) (duplicated models removed):
min_prior(out$bic, bpp_target = .8)
#> [1] 0.9999998