Reporting Models' Bayes FactorSource:
Create reports of Bayes factors for model comparison.
Object of class
Effect size interpretation set of rules (see interpret_bf).
Should very large or very small values be reported with a scientific format (e.g., 4.24e5), or as truncated values (as "> 1000" and "< 1/1000").
Should values smaller than 1 be represented as ratios?
Arguments passed to or from other methods.
An object of class
Specific components of reports (especially for stats models):
Other types of reports:
Template file for supporting new models:
library(bayestestR) # Bayes factor - models mo0 <- lm(Sepal.Length ~ 1, data = iris) mo1 <- lm(Sepal.Length ~ Species, data = iris) mo2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris) mo3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris) BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0) r <- report(BFmodels) r #> Bayes factors were computed using the BIC approximation, by which BF10 = #> exp((BIC0 - BIC1)/2). Compared to the (Intercept only) model (the least #> supported model), we found extreme evidence (BF = 1.70e+29) in favour of the #> Species model; extreme evidence (BF = 5.84e+55) in favour of the Species + #> Petal.Length model (the most supported model); extreme evidence (BF = 2.20e+54) #> in favour of the Species * Petal.Length model. # Bayes factor - inclusion inc_bf <- bayesfactor_inclusion(BFmodels, prior_odds = c(1, 2, 3), match_models = TRUE) r <- report(inc_bf) r #> Bayesian model averaging (BMA) was used to obtain the average evidence for each #> predictor. Since each model has a prior probability (here we used subjective #> prior odds of 1, 2, 3), it is possible to sum the prior probability of all #> models that include a predictor of interest (the prior inclusion probability), #> and of all models that do not include that predictor (the prior exclusion #> probability). After the data are observed, we can similarly consider the sums #> of the posterior models' probabilities to obtain the posterior inclusion #> probability and the posterior exclusion probability. The change from prior to #> posterior inclusion odds is the Inclusion Bayes factor. For each predictor, #> averaging was done only across models that did not include any interactions #> with that predictor; additionally, for each interaction predictor, averaging #> was done only across models that contained the main effect from which the #> interaction predictor was comprised. This was done to prevent Inclusion Bayes #> factors from being contaminated with non-relevant evidence (see Mathot, 2017). #> We found extreme evidence (BF = 3.90e+55) in favour of including Species, with #> models including Species having an overall posterior probability of 95%; #> extreme evidence (BF = 6.89e+26) in favour of including Petal.Length, with #> models including Petal.Length having an overall posterior probability of 95%; #> strong evidence (BF = 1/26.52) against including Petal.Length:Species, with #> models including Petal.Length:Species having an overall posterior probability #> of 5%. as.data.frame(r) #> Terms | Pr(prior) | Pr(posterior) | Inclusion BF #> --------------------------------------------------------------- #> Species | 0.43 | 0.95 | 128.00 #> Petal.Length | 0.29 | 0.95 | 61.80 #> Petal.Length:Species | 0.43 | 0.05 | 1/-3.05e-01 #> #> Across matched models only. #> With custom prior odds of [1, 2, 3].