Extract posterior samples of parameters, weighted across models.
Weighting is done by comparing posterior model probabilities, via bayesfactor_models.
weighted_posteriors(..., prior_odds = NULL, missing = 0, verbose = TRUE)# S3 method for data.frame
weighted_posteriors(..., prior_odds = NULL, missing = 0, verbose = TRUE)
# S3 method for stanreg
weighted_posteriors(
...,
prior_odds = NULL,
missing = 0,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL
)
# S3 method for brmsfit
weighted_posteriors(
...,
prior_odds = NULL,
missing = 0,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL
)
# S3 method for BFBayesFactor
weighted_posteriors(
...,
prior_odds = NULL,
missing = 0,
verbose = TRUE,
iterations = 4000
)
Fitted models (see details), all fit on the same data, or a single BFBayesFactor object (see 'Details').
Optional vector of prior odds for the models compared to the first model (or the denominator, for BFBayesFactor objects). For data.frames, this will be used as the basis of weighting.
An optional numeric value to use if a model does not contain a parameter that appears in other models. Defaults to 0.
Toggle off warnings.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.
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.
For BayesFactor models, how many posterior samples to draw.
A data frame with posterior distributions (weighted across models) .
Note that across models some parameters might play different roles. For example,
the parameter A plays a different role in the model Y ~ A + B (where it is a main effect)
than it does in the model Y ~ A + B + A:B (where it is a simple effect). In many cases centering
of predictors (mean subtracting for continuous variables, and effects coding via contr.sum or
orthonormal coding via contr.bayes for factors) can reduce this issue. In any case
you should be mindful of this issue.
See bayesfactor_models details for more info on passed models.
Note that for BayesFactor models, posterior samples cannot be generated from intercept only models.
This function is similar in function to brms::posterior_average.
Clyde, M., Desimone, H., & Parmigiani, G. (1996). Prediction via orthogonalized model mixing. Journal of the American Statistical Association, 91(435), 1197-1208.
Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. 10.31234/osf.io/wgb64
Rouder, J. N., Haaf, J. M., & Vandekerckhove, J. (2018). Bayesian inference for psychology, part IV: Parameter estimation and Bayes factors. Psychonomic bulletin & review, 25(1), 102-113.
van den Bergh, D., Haaf, J. M., Ly, A., Rouder, J. N., & Wagenmakers, E. J. (2019). A cautionary note on estimating effect size.
bayesfactor_inclusion for Bayesian model averaging.
# NOT RUN {
if (require("rstanarm") && require("see")) {
stan_m0 <- stan_glm(extra ~ 1,
data = sleep,
family = gaussian(),
refresh = 0,
diagnostic_file = file.path(tempdir(), "df0.csv")
)
stan_m1 <- stan_glm(extra ~ group,
data = sleep,
family = gaussian(),
refresh = 0,
diagnostic_file = file.path(tempdir(), "df1.csv")
)
res <- weighted_posteriors(stan_m0, stan_m1)
plot(eti(res))
}
## With BayesFactor
if (require("BayesFactor")) {
extra_sleep <- ttestBF(formula = extra ~ group, data = sleep)
wp <- weighted_posteriors(extra_sleep)
describe_posterior(extra_sleep, test = NULL)
describe_posterior(wp$delta, test = NULL) # also considers the null
}
## weighted prediction distributions via data.frames
if (require("rstanarm")) {
m0 <- stan_glm(
mpg ~ 1,
data = mtcars,
family = gaussian(),
diagnostic_file = file.path(tempdir(), "df0.csv"),
refresh = 0
)
m1 <- stan_glm(
mpg ~ carb,
data = mtcars,
family = gaussian(),
diagnostic_file = file.path(tempdir(), "df1.csv"),
refresh = 0
)
# Predictions:
pred_m0 <- data.frame(posterior_predict(m0))
pred_m1 <- data.frame(posterior_predict(m1))
BFmods <- bayesfactor_models(m0, m1)
wp <- weighted_posteriors(pred_m0, pred_m1,
prior_odds = BFmods$BF[2]
)
# look at first 5 prediction intervals
hdi(pred_m0[1:5])
hdi(pred_m1[1:5])
hdi(wp[1:5]) # between, but closer to pred_m1
}
# }
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