Nothing
## ---- SETTINGS-knitr, include=FALSE-------------------------------------------
stopifnot(require(knitr))
opts_chunk$set(
comment=NA,
message = FALSE,
warning = FALSE,
eval = identical(Sys.getenv("NOT_CRAN"), "true"),
dev = "png",
dpi = 150,
fig.asp = 0.618,
fig.width = 5,
out.width = "60%",
fig.align = "center"
)
## ---- SETTINGS-gg, include=TRUE-----------------------------------------------
library(ggplot2)
library(bayesplot)
theme_set(bayesplot::theme_default())
## ---- default-prior-1, results="hide"-----------------------------------------
library("rstanarm")
default_prior_test <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1)
## ---- default-prior-summary---------------------------------------------------
prior_summary(default_prior_test)
## ---- echo=FALSE--------------------------------------------------------------
priors <- prior_summary(default_prior_test)
fr2 <- function(x) format(round(x, 2), nsmall = 2)
## ---- no-autoscale, results="hide"--------------------------------------------
test_no_autoscale <-
update(
default_prior_test,
prior = normal(0, 5),
prior_intercept = student_t(4, 0, 10),
prior_aux = cauchy(0, 3)
)
## ---- no-autoscale-prior-summary----------------------------------------------
prior_summary(test_no_autoscale)
## -----------------------------------------------------------------------------
p <- 1 - 2 * pnorm(-250, mean = 0, sd = 500)
print(paste("Pr(-250 < theta < 250) =", round(p, 2)))
## ---- fig.cap="_There is much more probability mass outside the interval (-250, 250)._"----
theta <- rnorm(1e5, mean = 0, sd = 500)
p_approx <- mean(abs(theta) < 250)
print(paste("Pr(-250 < theta < 250) =", round(p_approx, 2)))
d <- data.frame(theta, clr = abs(theta) > 250)
library(ggplot2)
ggplot(d, aes(x = theta, fill = clr)) +
geom_histogram(binwidth = 5, show.legend = FALSE) +
scale_y_continuous(name = "", labels = NULL, expand = c(0,0)) +
scale_x_continuous(name = expression(theta), breaks = c(-1000, -250, 250, 1000))
## ---- flat-prior-1, echo=FALSE, results="hide"--------------------------------
flat_prior_test <- stan_glm(mpg ~ wt, data = mtcars, prior = NULL, iter = 10, chains = 1)
## ---- flat-prior-2, eval=FALSE------------------------------------------------
# flat_prior_test <- stan_glm(mpg ~ wt, data = mtcars, prior = NULL)
## ---- flat-prior-summary------------------------------------------------------
prior_summary(flat_prior_test)
## ---- eval=FALSE--------------------------------------------------------------
# my_prior <- normal(location = c(-10, 0), scale = c(5, 2))
# stan_glm(y ~ x1 + x2, data = dat, prior = my_prior)
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