```
# NOT RUN {
## Poisson regression for the number of seizures in epileptic patients
## using student_t priors for population-level effects
## and half cauchy priors for standard deviations of group-level effects
fit1 <- brm(count ~ log_Age_c + log_Base4_c * Trt
+ (1|patient) + (1|obs),
data = epilepsy, family = poisson(),
prior = c(prior(student_t(5,0,10), class = b),
prior(cauchy(0,2), class = sd)))
## generate a summary of the results
summary(fit1)
## plot the MCMC chains as well as the posterior distributions
plot(fit1, ask = FALSE)
## extract random effects standard devations and covariance matrices
VarCorr(fit1)
## extract group specific effects of each level
ranef(fit1)
## predict responses based on the fitted model
head(predict(fit1))
## plot marginal effects of each predictor
plot(marginal_effects(fit1), ask = FALSE)
## investigate model fit
WAIC(fit1)
pp_check(fit1)
## Ordinal regression modeling patient's rating of inhaler instructions
## category specific effects are estimated for variable 'treat'
fit2 <- brm(rating ~ period + carry + cs(treat),
data = inhaler, family = sratio("cloglog"),
prior = set_prior("normal(0,5)"), chains = 2)
summary(fit2)
plot(fit2, ask = FALSE)
WAIC(fit2)
head(predict(fit2))
## Survival regression modeling the time between the first
## and second recurrence of an infection in kidney patients.
fit3 <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
data = kidney, family = lognormal())
summary(fit3)
plot(fit3, ask = FALSE)
plot(marginal_effects(fit3), ask = FALSE)
## Probit regression using the binomial family
n <- sample(1:10, 100, TRUE) # number of trials
success <- rbinom(100, size = n, prob = 0.4)
x <- rnorm(100)
data4 <- data.frame(n, success, x)
fit4 <- brm(success | trials(n) ~ x, data = data4,
family = binomial("probit"))
summary(fit4)
## Simple non-linear gaussian model
x <- rnorm(100)
y <- rnorm(100, mean = 2 - 1.5^x, sd = 1)
data5 <- data.frame(x, y)
fit5 <- brm(bf(y ~ a1 - a2^x, a1 + a2 ~ 1, nl = TRUE),
data = data5,
prior = c(prior(normal(0, 2), nlpar = a1),
prior(normal(0, 2), nlpar = a2)))
summary(fit5)
plot(marginal_effects(fit5), ask = FALSE)
## Normal model with heterogeneous variances
data_het <- data.frame(y = c(rnorm(50), rnorm(50, 1, 2)),
x = factor(rep(c("a", "b"), each = 50)))
fit6 <- brm(bf(y ~ x, sigma ~ 0 + x), data = data_het)
summary(fit6)
plot(fit6)
marginal_effects(fit6)
# extract estimated residual SDs of both groups
sigmas <- exp(posterior_samples(fit6, "^b_sigma_"))
ggplot(stack(sigmas), aes(values)) +
geom_density(aes(fill = ind))
## Quantile regression predicting the 25%-quantile
fit7 <- brm(bf(y ~ x, quantile = 0.25), data = data_het,
family = asym_laplace())
summary(fit7)
marginal_effects(fit7)
## use the future package for parallelization
library(future)
plan(multiprocess)
fit7 <- update(fit7, future = TRUE)
# }
# NOT RUN {
# }
```

Run the code above in your browser using DataCamp Workspace