# Chapter 2

Reproducing Chapter 2 of Bayesian Methods for Hackers in R + Stan

J. Duncan
01-08-2021

## 2.2 Modeling Approaches

### 2.2.10 Example: Challenger Space Shuttle Disaster

library(tidyverse)
library(cmdstanr)
library(posterior)
library(bayesplot)
library(tidybayes)
library(latex2exp)
library(modelr)

url <- "https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter2_MorePyMC/data/challenger_data.csv"

data <- read_csv(url, col_types = "cdi") %>%
janitor::clean_names() %>%
mutate(
date = if_else(
nchar(date) >= 10,
parse_date(date, format = "%m/%d/%Y"),
parse_date(date, format = "%m/%d/%y")
)
) %>%
drop_na()


Let’s visualize this data to make sure all is well:

theme_set(theme_tidybayes())

data %>%
ggplot(aes(x=temperature, y=damage_incident)) +
geom_point(alpha = 0.5) +
labs(
title = "Defects of the Space Shuttle O-Rings vs temperature",
x = "Outside temperautre (Farenheit)",
y = "Damage Incident"
) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 1)) +
xlim(50, 85) https://jrnold.github.io/bayesian_notes/binomial-models.html

map_df(
list(-1, -3, 5),
~ tibble(
x = seq(-4, 4, length.out = 101),
y = plogis(x * .x),
Parameter = paste0("Beta: ", .x)
)
) %>%
ggplot(aes(x = x, y = y, colour = Parameter)) +
geom_line() ### 2.2.11 The Normal Distribution

data_list <- data %>%
rename(damage = damage_incident) %>%
compose_data()

mod <- cmdstanr::cmdstan_model("models/ch2-mod.stan")
mod$print()  data { int<lower=0> n; vector[n] temperature; int<lower=0,upper=1> damage[n]; } parameters { real alpha; real beta; } model { alpha ~ normal(0, sqrt(1000)); beta ~ normal(0, sqrt(1000)); damage ~ bernoulli_logit(beta * temperature + alpha); } generated quantities { vector[n] yrep; for (i in 1:n){ yrep[i] = bernoulli_logit_rng(beta * temperature[i] + alpha); } } fit <- mod$sample(
data = data_list,
seed = 123,
chains = 4,
parallel_chains = 4,
refresh = 1000,
iter_sampling = 3000
)

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All 4 chains finished successfully.
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Total execution time: 1.1 seconds.
fit$summary()  # A tibble: 26 x 10 variable mean median sd mad q5 q95 rhat ess_bulk <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 lp__ -11.3 -11.0 1.02 0.743 -13.3 -10.3 1.00 2616. 2 alpha 17.4 16.6 7.77 7.66 6.16 31.2 1.00 1526. 3 beta -0.267 -0.256 0.114 0.112 -0.469 -0.104 1.00 1536. 4 yrep 0.448 0 0.497 0 0 1 1.00 11428. 5 yrep 0.232 0 0.422 0 0 1 1.00 11617. 6 yrep 0.270 0 0.444 0 0 1 1.00 11845. 7 yrep 0.327 0 0.469 0 0 1 1.00 11921. 8 yrep 0.376 0 0.484 0 0 1 1.00 11480. 9 yrep 0.156 0 0.363 0 0 1 1.00 11547. 10 yrep 0.128 0 0.334 0 0 1 1.00 10240. # … with 16 more rows, and 1 more variable: ess_tail <dbl> https://mpopov.com/blog/2020/09/07/pivoting-posteriors/ tidy_draws.CmdStanMCMC <- function(model, ...) { return(as_draws_df(model$draws()))
}

fit %>%
gather_draws(alpha, beta) %>%
ggplot(aes(x = .value)) +
geom_histogram() +
facet_wrap(~.variable, scales = "free", ncol = 1) +
labs(
title = TeX("Posterior Distributions of $\\alpha$ and $\\beta$"),
y = NULL
) Using comments from discourse:

Extract posterior parameter samples into R and do prediction in R with new-data-for-prediction.

post_df <- spread_draws(fit, alpha, beta)
t <- seq(min(data$temperature) - 5, max(data$temperature) + 5, length.out = 50)

pred_df <- tibble(temperature = t) %>%
mutate(nest(post_df)) %>% # nest all posterior parameters samples
mutate(
data = map2(
data,
temperature,
~ mutate(.x, pred = plogis(alpha + beta * .y)) # predict
)
) %>%
unnest(data)


Now for the visualizations. First let’s look at realizations and then will summarize all of the realizations:

pred_df %>%
group_by(temperature) %>%
sample_draws(100) %>%
ggplot(aes(x = temperature)) +
geom_line(aes(y = pred, group = .draw), alpha = 0.3) +
geom_point(data = data, aes(y = damage_incident), alpha = 0.3) +
labs(
title = "Posterior probability estimates given temp; realizations",
y = "probability estimate"
) Now for all of the realizations:

pred_df %>%
group_by(temperature) %>%
median_hdci(pred, .width = c(0.95, 0.8)) %>%
ggplot(aes(x = temperature)) +
geom_lineribbon(aes(y = pred, ymin = .lower, ymax = .upper), color = "#08519C") +
geom_point(data = data, aes(y = damage_incident), alpha = 0.5) +
scale_fill_brewer() +
labs(
title = "Posterior probability estimates given temp",
y = "probability estimate"
) ### 2.2.12 What about the day of the Challenger disaster?

On the day of the Challenger disaster, the outside temperature was 31 degrees Fahrenheit. What is the posterior distribution of a defect occurring, given this temperature? The distribution is plotted below. It looks almost guaranteed that the Challenger was going to be subject to defective O-rings.

prob_31 <- post_df %>%
mutate(pred = plogis(alpha + beta * 31))

prob_31 %>%
ggplot(aes(x = pred)) +
stat_dots(quantiles = 25) +
labs(
title = TeX("Posterior distribution of probability of defect, given $t = 31$"),
x = "probability of defect occurring in O-ring",
y = NULL
) ## 2.3 Is Our Model Appropriate?

### 2.3.1 Separation Plots

Where expected number of defects for the Bernoulli random variable is given as:

$E[S] = \sum_{i=0}^N E[X_i] = \sum_{i=0}^N p_i$ Preparing our data for the separation plot:

sep_data <- fit %>%
spread_draws(yrep[day]) %>%
mean_hdci(yrep) %>%
left_join(mutate(data, day = row_number())) %>%
arrange(yrep) %>%
mutate(idx = row_number())

exp_def <- sep_data %>%
summarize(exp_def = sum(yrep)) %>%
pull()


Now let’s review the separation plot:

sep_data %>%
ggplot(aes(x=idx)) +
geom_col(aes(y=damage_incident), alpha = 0.3) +
geom_step(aes(y = yrep)) +
geom_vline(xintercept = 23 - exp_def, linetype = "dotted") +
labs(
title = "Separation Plot",
subtitle = paste0("with ", round(exp_def, 1), " expected defects"),
y = NULL,
x = NULL
) The snaking-line is the sorted probabilities, blue gray bars denote defects, and empty space denote non-defects. As the probability rises, we see more and more defects occur. On the right hand side, the plot suggests that as the posterior probability is large (line close to 1), then more defects are realized. This is good behaviour. Ideally, all the blue gray bars should be close to the right-hand side, and deviations from this reflect missed predictions.