```
if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
### Linear regression
mtcars$mpg10 <- mtcars$mpg / 10
fit <- stan_glm(
mpg10 ~ wt + cyl + am,
data = mtcars,
QR = TRUE,
# for speed of example only (default is "sampling")
algorithm = "fullrank",
refresh = 0
)
plot(fit, prob = 0.5)
plot(fit, prob = 0.5, pars = "beta")
plot(fit, "hist", pars = "sigma")
# \donttest{
### Logistic regression
head(wells)
wells$dist100 <- wells$dist / 100
fit2 <- stan_glm(
switch ~ dist100 + arsenic,
data = wells,
family = binomial(link = "logit"),
prior_intercept = normal(0, 10),
QR = TRUE,
refresh = 0,
# for speed of example only
chains = 2, iter = 200
)
print(fit2)
prior_summary(fit2)
# ?bayesplot::mcmc_areas
plot(fit2, plotfun = "areas", prob = 0.9,
pars = c("(Intercept)", "arsenic"))
# ?bayesplot::ppc_error_binned
pp_check(fit2, plotfun = "error_binned")
### Poisson regression (example from help("glm"))
count_data <- data.frame(
counts = c(18,17,15,20,10,20,25,13,12),
outcome = gl(3,1,9),
treatment = gl(3,3)
)
fit3 <- stan_glm(
counts ~ outcome + treatment,
data = count_data,
family = poisson(link="log"),
prior = normal(0, 2),
refresh = 0,
# for speed of example only
chains = 2, iter = 250
)
print(fit3)
bayesplot::color_scheme_set("viridis")
plot(fit3)
plot(fit3, regex_pars = c("outcome", "treatment"))
plot(fit3, plotfun = "combo", regex_pars = "treatment") # ?bayesplot::mcmc_combo
posterior_vs_prior(fit3, regex_pars = c("outcome", "treatment"))
### Gamma regression (example from help("glm"))
clotting <- data.frame(log_u = log(c(5,10,15,20,30,40,60,80,100)),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
fit4 <- stan_glm(
lot1 ~ log_u,
data = clotting,
family = Gamma(link="log"),
iter = 500, # for speed of example only
refresh = 0
)
print(fit4, digits = 2)
fit5 <- update(fit4, formula = lot2 ~ log_u)
# ?bayesplot::ppc_dens_overlay
bayesplot::bayesplot_grid(
pp_check(fit4, seed = 123),
pp_check(fit5, seed = 123),
titles = c("lot1", "lot2")
)
### Negative binomial regression
fit6 <- stan_glm.nb(
Days ~ Sex/(Age + Eth*Lrn),
data = MASS::quine,
link = "log",
prior_aux = exponential(1.5, autoscale=TRUE),
chains = 2, iter = 200, # for speed of example only
refresh = 0
)
prior_summary(fit6)
bayesplot::color_scheme_set("brightblue")
plot(fit6)
pp_check(fit6, plotfun = "hist", nreps = 5) # ?bayesplot::ppc_hist
# 80% interval of estimated reciprocal_dispersion parameter
posterior_interval(fit6, pars = "reciprocal_dispersion", prob = 0.8)
plot(fit6, "areas", pars = "reciprocal_dispersion", prob = 0.8)
# }
}
```

Run the code above in your browser using DataLab