I have simulated some data and am trying to conduct a binary logistic regression on it.

- Are the evidence ratio equivalent to Bayes factor in this case?
- How do I check if the model fits the data?

GM and FB are binary and categorical

Age is continuous

```
#Regression for Age
BayesGM1 <- brm(formula = FB ~ Age,
data = G, family = bernoulli(link = "logit"),
warmup = 500, iter = 2000, chains = 2, inits= "0",cores=2, seed = 123)
# Regression BF for Age
BayesGM2 <- brm(formula = FB ~ Age + GM,
data = G, family = bernoulli(link = "logit"),
warmup = 500, iter = 2000, chains = 2, inits= "0",cores=2, seed = 123)
# interaction term
BayesGM3 <- brm(formula = FB ~ Age + GM + GM*Age,
data = G, family = bernoulli(link = "logit"),
warmup = 500, iter = 2000, chains = 2, inits= "0",cores=2, seed = 123)
brms_SummaryTable(BayesGM1, astrology=TRUE, hype=TRUE, panderize=TRUE, justify='lrrrrclr')
brms_SummaryTable(BayesGM2, astrology=TRUE, hype=TRUE, panderize=TRUE, justify='lrrrrclr')
brms_SummaryTable(BayesGM3, astrology=TRUE, hype=TRUE, panderize=TRUE, justify='lrrrrclr')
```

- Operating System: Mac OS Catalina
- brms Version: 2.14.4