trafficdeaths.Rmd

---
title: "Bayesian data analysis - traffic deaths in Finland"
author: "Aki Vehtari"
date: "First version 2017-09-28. Last modified `r format(Sys.Date())`."
output:
  html_document:
    fig_caption: yes
    toc: TRUE
    toc_depth: 2
    number_sections: TRUE
    toc_float:
      smooth_scroll: FALSE
    theme: readable
    code_download: true
---
# Setup  {.unnumbered}

```{r setup, include=FALSE}
knitr::opts_chunk$set(cache=FALSE, message=FALSE, error=FALSE, warning=TRUE, out.width='95%')
```

**Load packages**

```{r comment=NA}
library(ggplot2)
library(tidyr)
library(gridExtra)
library(rstanarm)
library(brms)
library(bayesplot)
theme_set(bayesplot::theme_default(base_family = "sans", base_size = 16))
library(patchwork)
library(loo)
library(rprojroot)
root<-has_file(".BDA_R_demos_root")$make_fix_file()
```

# Introduction

This notebook demonstrates time series analysis for traffic deaths per
year in Finland. Currently when the the number of traffic deaths
during previous year are reported, the press release claims that the
the traffic safety in Finland has improved or worsened depending
whether the number is smaller or larger than the year before. Time
series analysis can be used to separate random fluctuation from the
slowly changing traffic safety.

# Data

Read the data (there would data for earlier years, too, but this is
sufficient for the demonstration)

```{r }
# file preview shows a header row
deaths <- read.csv(root("demos_rstan", "trafficdeaths.csv"), header = TRUE)
head(deaths)
```

First plot just the data.

```{r }
deaths |>
  ggplot(aes(x=year, y=deaths)) +
  geom_point() +
  labs(y = 'Traffic deaths', x= "Year") +
  guides(linetype = "none")
```

# Poisson regression model

The number of deaths is count data, so we use Poisson observation
model.  We first fit log-linear model for the Poisson intensity, which
corresponds to assuming constant proportional change in the rate.

```{r }
fit_lin <- stan_glm(deaths ~ year, data=deaths, family=poisson,
 	            refresh=1000, iter=1000, chains=4, seed=583829, refresh=0)

```

ESS's and Rhat's are ok (see, e.g., [RStan
workflow](http://mc-stan.org/users/documentation/case-studies/rstan_workflow.html)). Let's
look at the posterior predictive distribution (median and 5% and 95%
intervals).

```{r }
x_predict <- seq(1993,2030)
N_predict <- length(x_predict)
y_predict_lin <- posterior_predict(fit_lin, newdata=data.frame(year=x_predict))
mu <- apply(t(y_predict_lin), 1, quantile, c(0.05, 0.5, 0.95)) %>%
  t() %>% data.frame(x = x_predict, .) %>% gather(pct, y, -x)
pfit <- ggplot() +
  geom_point(aes(year, deaths), data = deaths, size = 1) +
  geom_line(aes(x, y, linetype = pct), data = mu, color = 'red') +
  scale_linetype_manual(values = c(2,1,2)) +
  labs(x = 'Year', y = 'Traffic deaths') +
  guides(linetype = F)
(pfit)
```

Next we fit a non-linear spline model with `stan_gamm4`

```{r }
fit_gam <- stan_gamm4(deaths ~ year + s(year), data=deaths,
                      family=poisson, adapt_delta=0.999, 
                      refresh=1000, iter=2000, chain=4, seed=583829, refresh=0)
```

ESS is clearly smaller than for the linear model, but Rhat's are ok.

Let's look at the posterior predictive distribution.

```{r }
x_predict=seq(1993,2030)
N_predict=length(x_predict)
y_predict_gam <- posterior_predict(fit_gam, newdata=data.frame(year=x_predict))
mu <- apply(t(y_predict_gam), 1, quantile, c(0.05, 0.5, 0.95)) %>%
  t() %>% data.frame(x = x_predict, .) %>% gather(pct, y, -x)
pfit <- ggplot() +
  geom_point(aes(year, deaths), data = deaths, size = 1) +
  geom_line(aes(x, y, linetype = pct), data = mu, color = 'red') +
  scale_linetype_manual(values = c(2,1,2)) +
  labs(x = 'Year', y = 'Traffic deaths') +
  guides(linetype = F)
(pfit)
```

The predictive median is clearly nonlinear. The predictive mean for
future years stays at the same level as the most recent observations,
but uncertainty increases quickly.

Finally we fit Gaussian process centered on linear model. We use
brms for this:

```{r comment=NA}
fit_gp <- brm(deaths ~ year + gp(year), data=deaths,
                      family=poisson, adapt_delta=0.95, 
              refresh=1000, iter=2000, chain=4, seed=583829, refresh=0,
              backend='cmdstanr')

x_predict=seq(1993,2030)
N_predict=length(x_predict)
y_predict_gp <- posterior_predict(fit_gp, newdata=data.frame(year=x_predict))
mu <- apply(t(y_predict_gp), 1, quantile, c(0.05, 0.5, 0.95)) %>%
  t() %>% data.frame(x = x_predict, .) %>% gather(pct, y, -x)
pfit <- ggplot() +
  geom_point(aes(year, deaths), data = deaths, size = 1) +
  geom_line(aes(x, y, linetype = pct), data = mu, color = 'red') +
  scale_linetype_manual(values = c(2,1,2)) +
  labs(x = 'Year', y = 'Traffic deaths') +
  guides(linetype = F)
(pfit)
```

Finally we compare models using PSIS-LOO predictive performance estimates.

```{r }
(loo_lin<-loo(fit_lin, save_psis=TRUE))
(loo_gam<-loo(fit_gam, save_psis=TRUE))
(loo_gp<-loo(fit_gp, save_psis=TRUE))

loo_compare(loo_lin, loo_gam, loo_gp)
```

There are no practical differences in predictive performance, which is
partially due to small number of observations. Based on the posterior
predictive distributions there are clear differences in the future
predictions.
We can also look at the calibration of leave-one-out predictive
distributions

```{r }
ppc_loo_intervals(deaths$deaths,
                  y_predict_lin[,1:29],
                  psis_object=loo_lin$psis_object)+
  labs(title='PPC-LOO linear model')

ppc_loo_intervals(deaths$deaths,
                  y_predict_gp[,1:29],
                  psis_object=loo_gp$psis_object)+
  labs(title='PPC-LOO GP model')
```

There is a small difference in favor of GP model.
<br />

# Licenses {.unnumbered}

* Code &copy; 2017-2020, Aki Vehtari, licensed under BSD-3.
* Text &copy; 2017-2020, Aki Vehtari, licensed under CC-BY-NC 4.0.

# Original Computing Environment {.unnumbered}

```{r }
sessionInfo()
```

<br />