polr.R
In rstanarm: Bayesian Applied Regression Modeling via Stan

## ---- 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())

## ----polr-tobgp-mass----------------------------------------------------------
library(MASS)
print(polr(tobgp ~ agegp + alcgp, data = esoph), digits = 1)

## ----polr-tobgp-mcmc, results="hide"------------------------------------------
library(rstanarm)
post0 <- stan_polr(tobgp ~ agegp + alcgp, data = esoph, 
                   prior = R2(0.25), prior_counts = dirichlet(1),
                   seed = 12345)

## -----------------------------------------------------------------------------
print(post0, digits = 1)

## ---- polr-tobgp-cutpoints, echo=FALSE----------------------------------------
zeta_medians <- round(apply(rstan::extract(post0$stanfit, pars = "zeta")[[1]], 
                            2, median), digits = 2)

## ----polr-birthwt-recodes-----------------------------------------------------
data("birthwt", package = "MASS")
birthwt$race <- factor(birthwt$race, levels = 1:3, 
                       labels = c("white", "black", "other"))
birthwt$bwt <- birthwt$bwt / 1000 # convert from grams to kilograms
birthwt$low <- factor(birthwt$low, levels = 0:1, labels = c("no", "yes"))

## ----polr-stan_lm, results="hide"---------------------------------------------
post1 <- stan_lm(-bwt ~ smoke + age + race + ptl + ht + ftv,
                 data = birthwt, prior = R2(0.5), 
                 seed = 12345)

## -----------------------------------------------------------------------------
print(post1)

## ----polr-birthwt-mcmc, results="hide"----------------------------------------
post2 <- stan_polr(low ~ smoke + age + race + ptl + ht + ftv, data = birthwt,
                   prior = R2(0.5), prior_counts = dirichlet(c(1,1)), 
                   method = "probit", seed = 12345)

## ---- polr-loo-plot-----------------------------------------------------------
plot(loo(post2))

## ----polr-birthwt-comparison--------------------------------------------------
round(cbind(Linear = coef(post1), Ordinal = coef(post2), 
            Rescaled = coef(post1) / sigma(post1)), 3)