rasmusab · November 21, 2019 10:04
diff --git a/bayescamp_get_up_to_speed_with_bayes_test_script.R b/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()
	# 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()