Check if Prior is Informative

Source: R/check_prior.R

Performs a simple test to check whether the prior is informative to the posterior. This idea, and the accompanying heuristics, were discussed in Gelman et al. 2017.

check_prior(model, method = "gelman", simulate_priors = TRUE, ...)

# S3 method for class 'brmsfit'
check_prior(
  model,
  method = "gelman",
  simulate_priors = TRUE,
  effects = "fixed",
  component = "conditional",
  parameters = NULL,
  verbose = TRUE,
  ...
)

Arguments

model

A stanreg, stanfit, brmsfit, blavaan, or MCMCglmm object.

method

Can be "gelman" or "lakeland". For the "gelman" method, if the SD of the posterior is more than 0.1 times the SD of the prior, then the prior is considered as informative. For the "lakeland" method, the prior is considered as informative if the posterior falls within the 95% HDI of the prior.

simulate_priors

Should prior distributions be simulated using simulate_prior() (default; faster) or sampled via unupdate() (slower, more accurate).

...

Currently not used.

effects

Should variables for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed models. May be abbreviated.

For models of from packages brms or rstanarm there are additional options:

  • "fixed" returns fixed effects.

  • "random_variance" return random effects parameters (variance and correlation components, e.g. those parameters that start with sd_ or cor_).

  • "grouplevel" returns random effects group level estimates, i.e. those parameters that start with r_.

  • "random" returns both "random_variance" and "grouplevel".

  • "all" returns fixed effects and random effects variances.

  • "full" returns all parameters.

component

Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. See details in section Model Components. May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):

  • component = "all" returns all possible parameters.

  • If component = "location", location parameters such as conditional, zero_inflated, smooth_terms, or instruments are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

  • For component = "distributional" (or "auxiliary"), components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

parameters

Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like lp__ or prior_) are filtered by default, so only parameters that typically appear in the summary() are returned. Use parameters to select specific parameters for the output.

verbose

Toggle off warnings.

Value

A data frame with two columns: The parameter names and the quality of the prior (which might be "informative", "uninformative") or "not determinable" if the prior distribution could not be determined).

References

Gelman, A., Simpson, D., and Betancourt, M. (2017). The Prior Can Often Only Be Understood in the Context of the Likelihood. Entropy, 19(10), 555. doi:10.3390/e19100555

Examples

# \donttest{
library(bayestestR)
model <- rstanarm::stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
check_prior(model, method = "gelman")
#>     Parameter Prior_Quality
#> 1 (Intercept)   informative
#> 2          wt uninformative
#> 3          am uninformative
check_prior(model, method = "lakeland")
#>     Parameter Prior_Quality
#> 1 (Intercept)   informative
#> 2          wt   informative
#> 3          am   informative

# An extreme example where both methods diverge:
model <- rstanarm::stan_glm(mpg ~ wt,
  data = mtcars[1:3, ],
  prior = normal(-3.3, 1, FALSE),
  prior_intercept = normal(0, 1000, FALSE),
  refresh = 0
)
check_prior(model, method = "gelman")
#>     Parameter Prior_Quality
#> 1 (Intercept) uninformative
#> 2          wt   informative
check_prior(model, method = "lakeland")
#>     Parameter  Prior_Quality
#> 1 (Intercept)    informative
#> 2          wt misinformative
# can provide visual confirmation to the Lakeland method
plot(si(model, verbose = FALSE))

# }