BayesCamp 2019 tutorial - Get up to speed with Bayes test script

bayescamp_get_up_to_speed_with_bayes_test_script.R

# Prior to the tutorial make sure that the script below runs without error on your R installation.
# You first need to install the following packages:
# install.packages(c("rstanarm", "prophet", "CausalImpact", "rstan"))

library(rstanarm)
library(prophet)
library(CausalImpact)
library(rstan)
library(ggridges)


# This will test that rstanarm works
# Don't be alarmed if you get a warning about "divergent transitions "
fit <- stan_lm(mpg ~ wt + qsec + am, data = mtcars, prior = R2(0.75))
plot(fit, prob = 0.8)

# This tests that prophet is working
history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
                      y = sin(1:366/200) + rnorm(366)/10)
m <- prophet(history)
future <- make_future_dataframe(m, periods = 365)
forecast <- predict(m, future)
plot(m, forecast)

# This tests that CausalImpact is working
# First simulating some data
x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 52)
y <- 1.2 * x1 + rnorm(52)
y[41:52] <- y[41:52] + 10
data <- cbind(y, x1)
pre.period <- c(1, 40)
post.period <- c(41, 52)
# Then running CausalImpact
impact <- CausalImpact(data, pre.period, post.period)
plot(impact)

# This tests that rstan is working
stanmodelcode <- "
data {
  int<lower=0> N;
  real y[N];
} 

parameters {
  real mu;
} 

model {
  mu ~ normal(0, 10);
  y ~ normal(mu, 1);
} 
"
y <- rnorm(20) 
dat <- list(N = 20, y = y) 
fit <- stan(model_code = stanmodelcode, data = dat) 
print(fit)
plot(fit)

# Test that ggridges works
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(rel_min_height = 0.005) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  theme_ridges()