# Chapter 11 Extending the Normal Regression Model

The Bayesian simple Normal linear regression model in Chapters 9 and 10 is like a bowl of plain rice – delicious on its own, while also providing a strong foundation to build upon.
In Chapter 11, you’ll consider extensions of this model that greatly expand its flexibility in broader modeling settings.
You’ll do so in an analysis of weather in Australia, the ultimate goal being to predict how hot it will be in the *afternoon* based on information we have in the *morning*.
We have a vague prior understanding here that, on an average day in Australia, the typical afternoon temperature is somewhere between 15 and 35 degrees Celsius.
Yet beyond this baseline assumption, we are unfamiliar with Australian weather patterns, thus will utilize weakly informative priors throughout our analysis.

To build upon our weak prior understanding of Australian weather, we’ll explore the `weather_WU`

data in the **bayesrules** package.
This wrangled subset of the `weatherAUS`

data in the `rattle`

package (Williams 2011) contains 100 days of weather data for each of two Australian cities: Uluru and Wollongong.
A call to `?weather_WU`

from the console will pull up a detailed codebook.

```
# Load some packages
library(bayesrules)
library(rstanarm)
library(bayesplot)
library(tidyverse)
library(broom.mixed)
library(tidybayes)
# Load the data
data(weather_WU)
%>%
weather_WU group_by(location) %>%
tally()
# A tibble: 2 x 2
location n<fct> <int>
1 Uluru 100
2 Wollongong 100
```

To simplify things, we’ll retain only the variables on afternoon temperatures (`temp3pm`

) and a subset of possible predictors that we’d have access to in the morning:

```
<- weather_WU %>%
weather_WU select(location, windspeed9am, humidity9am, pressure9am, temp9am, temp3pm)
```

Let’s begin our analysis with the familiar: a simple Normal regression model of `temp3pm`

with one *quantitative* predictor, the morning temperature `temp9am`

, both measured in degrees Celsius.
As you might expect, there’s a positive association between these two variables – the warmer it is in the morning, the warmer it tends to be in the afternoon:

```
ggplot(weather_WU, aes(x = temp9am, y = temp3pm)) +
geom_point(size = 0.2)
```

To *model* this relationship, let \(Y_i\) denote the 3pm temperature and \(X_{i1}\) denote the 9am temperature on a given day \(i\).
Notice that we’re representing our predictor by \(X_{i1}\) here, instead of simply \(X_i\), in order to distinguish it from other predictors used later in the chapter.
Then the Bayesian Normal regression model of \(Y\) by \(X_1\) is represented by (11.1):

\[\begin{equation} \begin{split} Y_i | \beta_0, \beta_1, \sigma & \stackrel{ind}{\sim} N\left(\mu_i, \sigma^2\right) \;\; \text{ with } \;\; \mu_i = \beta_0 + \beta_1X_{i1} \\ \beta_{0c}& \sim N\left(25, 5^2\right) \\ \beta_1 & \sim N\left(0, 3.1^2\right) \\ \sigma & \sim \text{Exp}(0.13) \; .\\ \end{split} \tag{11.1} \end{equation}\]

Consider the independent priors utilized by this model:

- Since 0 degree mornings are rare in Australia, it’s difficult to state our prior understanding of the typical afternoon temperature on such a rare day, \(\beta_0\). Instead, the Normal prior model on the
**centered intercept**\(\beta_{0c}\) reflects our prior understanding that the average afternoon temperature on a*typical*day is somewhere between 15 and 35 degrees (\(25 \pm 2*5\)). - The
**weakly informative priors**for \(\beta_1\) and \(\sigma\) are auto-scaled by`stan_glm()`

to reflect our lack of prior information about Australian weather, as well as*reasonable*ranges for these parameters based on the simple scales of our temperature data. - The fact that the Normal prior for \(\beta_1\) is centered around 0 reflects a default, conservative prior assumption that the relationship between 3pm and 9am temperatures might be positive (\(\beta_1 > 0\)), negative (\(\beta_1 < 0\)), or even non-existent (\(\beta_1 = 0\)).

We simulate the model posterior below and encourage you to follow this up with a check of the prior model specifications and some MCMC diagnostics (which all look good!):

```
<- stan_glm(
weather_model_1 ~ temp9am,
temp3pm data = weather_WU, family = gaussian,
prior_intercept = normal(25, 5),
prior = normal(0, 2.5, autoscale = TRUE),
prior_aux = exponential(1, autoscale = TRUE),
chains = 4, iter = 5000*2, seed = 84735)
# Prior specification
prior_summary(weather_model_1)
# MCMC diagnostics
mcmc_trace(weather_model_1, size = 0.1)
mcmc_dens_overlay(weather_model_1)
mcmc_acf(weather_model_1)
neff_ratio(weather_model_1)
rhat(weather_model_1)
```

The simulation results provide finer insight into the association between afternoon and morning temperatures.
Per the 80% credible interval for \(\beta_1\), there’s an 80% posterior probability that for every one degree increase in `temp9am`

, the average increase in `temp3pm`

is somewhere between 0.98 and 1.1 degrees.
Further, per the 80% credible interval for standard deviation \(\sigma\), this relationship is fairly strong – observed afternoon temperatures tend to fall somewhere between only 3.87 and 4.41 degrees from what we’d expect based on the corresponding morning temperature.

```
# Posterior credible intervals
posterior_interval(weather_model_1, prob = 0.80)
10% 90%
2.9498 5.449
(Intercept) 0.9803 1.102
temp9am 3.8739 4.409 sigma
```

But is this a “good” model?
We’ll more carefully address this question in Section 11.5.
For now, we’ll leave you with a quick `pp_check()`

which illustrates that *we can do better* (Figure 11.2).
Though the 50 sets of afternoon temperature data simulated from the `weather_model_1`

posterior (light blue) tend to capture the general center and spread of the afternoon temperatures we actually observed (dark blue), *none* capture the bimodality in these temperatures.
That is, none reflect the fact that there’s a batch of temperatures around 20 degrees and another batch around 35 degrees.

`pp_check(weather_model_1)`

This comparison of the observed and posterior simulated temperature data indicates that, though `temp9am`

contains *some* useful information about `temp3pm`

, it doesn’t tell us *everything*.
Throughout Chapter 11 we’ll expand upon `weather_model_1`

in the hopes of improving our model of afternoon temperatures.
The main idea is this.
If we’re in Australia at 9am, the current temperature isn’t the *only* factor that might help us predict 3pm temperature.
Our model might be improved by incorporating information about our location or humidity or air pressure and so on.
It might be further improved by incorporating *multiple* predictors in the same model!

- Extend the Normal linear regression model of a quantitative response variable \(Y\) to settings in which we have:
- a
**categorical**predictor \(X\); **multiple**predictors (\(X_1,X_2,...,X_p\)); or- predictors which
**interact**;

- a
- Compare and evaluate competing models of \(Y\) to answer the question of “which model should I choose?”; and
- Consider the
**bias-variance trade-off**in building a regression model of \(Y\) with multiple predictors.

## 11.1 Utilizing a categorical predictor

Again, suppose we find ourselves in Australia at 9am. Our exact location can shed some light on what temperature we can expect at 3pm. Density plots of the temperatures in both locations indicate that it tends to be colder in the southeastern coastal city of Wollongong than in the desert climate of Uluru:

```
ggplot(weather_WU, aes(x = temp3pm, fill = location)) +
geom_density(alpha = 0.5)
```

Given the useful information it provides, we’re tempted to incorporate `location`

into our temperature analysis.
We can!
But in doing so, it’s important to recognize that `location`

would be a **categorical predictor variable**.
We are either in `Uluru`

or `Wollongong`

.
Though this impacts the details, we can model the relationship between `temp3pm`

and `location`

much as we modeled the relationship between `temp3pm`

and the quantitative `temp9am`

predictor.

### 11.1.1 Building the model

For day \(i\), let \(Y_i\) denote the 3pm temperature and \(X_{i2}\) be an **indicator** for the location being Wollongong.
That is, \(X_{i2}\) is 1 if we’re in Wollongong and 0 otherwise:

\[X_{i2} = \begin{cases} 1 & \text{ Wollongong} \\ 0 & \text{ otherwise (i.e. Uluru)} \\ \end{cases}\]

Since \(X_{i2}\) is 0 if we’re in Uluru, we can think of Uluru as the **baseline** or **reference level** of the `location`

variable.
(Uluru is the default reference level since it’s first alphabetically.)
Then the **Bayesian Normal regression model** between 3pm temperature and location has the same structure as (11.1), with a \(N(25,5^2)\) prior model for the centered intercept \(\beta_{0c}\) and weakly informative prior models for \(\beta_1\) and \(\sigma\) tuned by `stan_glm()`

:

\[\begin{equation} \begin{array}{lcrl} \text{data:} & \hspace{.05in} & Y_i|\beta_0,\beta_1,\sigma & \stackrel{ind}{\sim} N(\mu_i, \; \sigma^2) \;\; \text{ with } \;\; \mu_i = \beta_0 + \beta_1 X_{i2}\\ \text{priors:} & & \beta_{0c} & \sim N\left(25, 5^2\right) \\ & & \beta_1 & \sim N\left(0, 38^2 \right) \\ & & \sigma & \sim \text{Exp}(0.13) \; .\\ \end{array} \tag{11.2} \end{equation}\]

Though the model *structure* is unchanged, the *interpretation* is impacted by the categorical nature of \(X_{i2}\).
The model mean \(\mu_i = \beta_0 + \beta_1 X_{i2}\) no longer represents a *line* with intercept \(\beta_0\) and slope \(\beta_1\).
In fact, \(\mu_i = \beta_0 + \beta_1 X_{i2}\) more simply describes the typical 3pm temperature under only two scenarios.
**Scenario 1:** For Uluru, \(X_{i2} = 0\) and the typical 3pm temperature simplifies to

\[\beta_0 + \beta_1 \cdot 0 = \beta_0 \; .\]

**Scenario 2:** For Wollongong, \(X_{i2} = 1\) and the typical 3pm temperature is

\[\beta_0 + \beta_1 \cdot 1 = \beta_0 + \beta_1 \; .\]

That is, the typical 3pm temperature in Wollongong is equal to that in Uluru (\(\beta_0\)) plus some *adjustment* or tweak \(\beta_1\).
Putting this together, we can reinterpret the model parameters as follows:

**Intercept coefficient**\(\beta_0\) represents the typical 3pm temperature in Uluru (\(X_2 = 0\)).**Wollongong coefficient**\(\beta_1\) represents the typical*difference*in 3pm temperature in Wollongong (\(X_2 = 1\))*versus*Uluru (\(X_2 = 0\)). Thus, \(\beta_1\)*technically*still measures the typical change in 3pm temperature for a one unit increase in \(X_2\), it’s just that this increase from 0 to 1 corresponds to a change in the`location`

category.- The
**standard deviation**parameter \(\sigma\) still represents the**variability**in \(Y\) at a given \(X_2\) value. Thus here, \(\sigma\) measures the standard deviation in 3pm temperatures in Wollongong (when \(X_2 = 1\)) and in Uluru (when \(X_2 = 0\)).

We can now interpret our prior models in (11.2) with this in mind.
First, the Normal prior model on the **centered intercept** \(\beta_{0c}\) reflects a prior understanding that the average afternoon temperature in *Uluru* is somewhere between 15 and 35 degrees.
Further, the **weakly informative** Normal prior for \(\beta_1\) is centered around 0, reflecting a default, conservative prior assumption that the average 3pm temperature in Wollongong might be greater (\(\beta_1 > 0\)), less (\(\beta_1 < 0\)), or even no different (\(\beta_1 = 0\)) from that in Uluru.
Finally, the **weakly informative** prior for \(\sigma\) expresses our lack of understanding about the degree to which 3pm temperatures vary at either location.

### 11.1.2 Simulating the posterior

To simulate the posterior model of (11.2) with weakly informative priors, we run four parallel Markov chains, each of length 10,000.
Other than changing the formula argument from `temp3pm ~ temp9am`

to `temp3pm ~ location`

, the syntax is equivalent to that for `weather_model_1`

:

```
<- stan_glm(
weather_model_2 ~ location,
temp3pm data = weather_WU, family = gaussian,
prior_intercept = normal(25, 5),
prior = normal(0, 2.5, autoscale = TRUE),
prior_aux = exponential(1, autoscale = TRUE),
chains = 4, iter = 5000*2, seed = 84735)
```

Trace plots and autocorrelation plots (omitted here for space), as well as density plots (Figure 11.4) suggest that our posterior simulation has sufficiently stabilized:

```
# MCMC diagnostics
mcmc_trace(weather_model_2, size = 0.1)
mcmc_dens_overlay(weather_model_2)
mcmc_acf(weather_model_2)
```

These density plots and the below numerical posterior summaries for \(\beta_0\) `(Intercept)`

, \(\beta_1\) (`locationWollongong`

), and \(\sigma\) (`sigma`

) reflect our posterior understanding of 3pm temperatures in Wollongong and Uluru.
Consider the message of the posterior median values for \(\beta_0\) and \(\beta_1\): the typical 3pm temperature is around 29.7 degrees in **Uluru** and, *comparatively*, around 10.3 degrees *lower* in **Wollongong**.
Combined then, we can say that the typical 3pm temperature in Wollongong is around 19.4 degrees (29.7 - 10.3).
For context, Figure 11.5 frames these posterior median estimates of 3pm temperatures in Uluru and Wollongong among the observed data.

```
# Posterior summary statistics
tidy(weather_model_2, effects = c("fixed", "aux"),
conf.int = TRUE, conf.level = 0.80) %>%
select(-std.error)
term estimate conf.low conf.high1 (Intercept) 29.715 29.019 30.425
2 locationWollongong -10.323 -11.319 -9.302
3 sigma 5.484 5.145 5.858
4 mean_PPD 24.565 23.859 25.262
```

Though the posterior medians provide a quick comparison of the typical temperatures in our two locations, they don’t reflect the full picture or our posterior *uncertainty* in this comparison.
To this end, we can compare the *entire* posterior model of the typical temperature in Uluru (\(\beta_0\)) to that in Wollongong (\(\beta_0 + \beta_1\)).
The 20,000 `(Intercept)`

Markov chain values provide a direct approximation of the \(\beta_0\) posterior, \(\left\lbrace \beta_0^{(1)},\beta_0^{(2)}, \ldots, \beta_0^{(20000)}\right\rbrace\).
As for \(\beta_0 + \beta_1\), we can approximate the posterior for this *function* of model parameters by the corresponding *function* of Markov chains.
Specifically, we can approximate the \(\beta_0 + \beta_1\) posterior using the chain produced by summing each pair of `(Intercept)`

(\(\beta_0\)) and `locationWollongong`

(\(\beta_1\)) chain values:

\[\left\lbrace \beta_0^{(1)} + \beta_1^{(1)}, \beta_0^{(2)}+ \beta_1^{(2)}, \ldots, \beta_0^{(20000)} + \beta_1^{(20000)}\right\rbrace \; .\]

The result below indicates that the typical Wollongong temperature is likely between 17 and 22 degrees, substantially below that in Uluru which is likely between 27 and 32 degrees.

```
as.data.frame(weather_model_2) %>%
mutate(uluru = `(Intercept)`,
wollongong = `(Intercept)` + locationWollongong) %>%
mcmc_areas(pars = c("uluru", "wollongong"))
```

## 11.2 Utilizing two predictors

We now know a couple of things about afternoon temperatures in Australia: (1) they’re positively associated with morning temperatures; and (2) they tend to be lower in Wollongong than in Uluru.
This makes us think.
If 9am temperature and location *both* tell us something about 3pm temperature, then maybe *together* they would tell us even more!
In fact, a plot of the sample data (Figure 11.7) reveals that, *no matter the location*, 3pm temperature linearly increases with 9am temperature.
Further, *at any given 9am temperature*, there is a consistent difference in 3pm temperature in Wollongong and Uluru.
Below, you will extend the **one predictor** Normal regression models you’ve studied thus far to a **two predictor** model of 3pm temperature using both `temp9am`

and `location`

.

```
ggplot(weather_WU, aes(y = temp3pm, x = temp9am, color = location)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
```

### 11.2.1 Building the model

Letting \(X_{i1}\) denote the 9am temperature and \(X_{i2}\) the location on day \(i\), the **multivariable Bayesian linear regression model** of 3pm temperature versus these two predictors is a small extension of the one predictor model (11.2).
We state this model here and tune the weakly informative priors using `stan_glm()`

below.

\[\begin{equation} \begin{array}{lcrl} \text{data:} & \hspace{.05in} & Y_i|\beta_0,\beta_1,\beta_2,\sigma & \stackrel{ind}{\sim} N(\mu_i, \; \sigma^2) \;\; \text{ with } \;\; \mu_i = \beta_0 + \beta_1X_{i1} + \beta_2X_{i2} \\ \text{priors:} & & \beta_{0c} & \sim N\left(25, 5^2 \right) \\ & & \beta_1 & \sim N\left(0, 3.11^2 \right) \\ & & \beta_2 & \sim N\left(0, 37.52^2 \right) \\ & & \sigma & \sim \text{Exp}(0.13) \; .\\ \end{array} \tag{11.3} \end{equation}\]

Interpreting the 9am temperature and location coefficients in this new model, \(\beta_1\) and \(\beta_2\), requires some care.
We can’t simply interpret \(\beta_1\) and \(\beta_2\) as we did in our first two models, (11.1) and (11.2), when using either predictor alone.
Rather, the meaning of our predictor coefficients changes depending upon the other predictors in the model.
Let’s again consider two scenarios.
**Scenario 1:** In *Uluru*, \(X_{i2} = 0\) and the relationship between 3pm and 9am temperature simplifies to the following formula for a *line*:

\[\beta_0 + \beta_1 X_{i1} + \beta_2 \cdot 0 = \beta_0 + \beta_1 X_{i1} \; .\]

**Scenario 2:** In *Wollongong*, \(X_{i2} = 1\) and the relationship between 3pm and 9am temperature simplifies to the following formula for a *different* line:

\[\beta_0 + \beta_1 X_{i1} + \beta_2 \cdot 1 = (\beta_0 + \beta_2) + \beta_1 X_{i1} \; .\]

Figure 11.8 combines Scenarios 1 and 2 into one picture, thereby illuminating the meaning of our regression coefficients:

**Intercept coefficient**\(\beta_0\) is the intercept of the Uluru line. Thus \(\beta_0\) represents the typical 3pm temperature in*Uluru*on a (theoretical) day in which it was*0 degrees*at 9am, i.e. when \(X_{1} = X_{2} = 0\).**9am temperature coefficient**\(\beta_1\) is the common*slope*of the Uluru and Wollongong lines. Thus \(\beta_1\) represents the typical change in 3pm temperature with every 1 degree increase in 9am temperature in*both*Uluru and Wollongong.**Location coefficient**\(\beta_2\) is the vertical distance, or adjustment, between the Wollongong versus Uluru line at any 9am temperature. Thus \(\beta_2\) represents the typical*difference*in 3pm temperature in Wollongong (\(X_{2} = 1\)) versus Uluru on days when they have*equal 9am temperature*\(X_1\).

### 11.2.2 Understanding the priors

Now that we have a better sense for the overall structure and assumptions behind our multivariable model of 3pm temperatures (11.3), let’s go back and examine the meaning of our priors in this context.
Beyond a prior for the centered intercept \(\beta_{0c}\) which reflected our basic understanding that the typical temperature on a typical day in Australia is likely between 15 and 35 degrees, our priors were weakly informatitve.
We simulate these *priors* below using `stan_glm()`

– the only difference between this and simulating the posteriors is the inclusion of `prior_PD = TRUE`

.
Also note that we represent the multivariable model structure by `temp3pm ~ temp9am + location`

:

```
<- stan_glm(
weather_model_3_prior ~ temp9am + location,
temp3pm data = weather_WU, family = gaussian,
prior_intercept = normal(25, 5),
prior = normal(0, 2.5, autoscale = TRUE),
prior_aux = exponential(1, autoscale = TRUE),
chains = 4, iter = 5000*2, seed = 84735,
prior_PD = TRUE)
```

From these priors, we then simulate and plot 100 different sets of 3pm temperature data (Figure 11.9).
First, notice from the left plot that our combined priors produce sets of 3pm temperatures that are centered around 25 degrees (per the \(\beta_{0c}\) prior), yet tend to range widely, from roughly -75 to 125 degrees.
This wide range is the result of our weakly informative priors and, though it spans unrealistic temperatures, it’s at least in the right ballpark.
After all, had we utilized *vague* instead of *weakly informative* priors, our prior simulated temperatures would span an even wider range, say on the order of *millions* of degrees Celsius.

Second, the plot at right displays our prior assumptions about the relationship between 3pm and 9am temperature at each location.
Per the prior model for slope \(\beta_1\) being centered at 0, these model lines reflect a conservative assumption that 3pm temperatures might be positively *or* negatively associated with 9am temperatures in both locations.
Further, per the prior model for the Wollongong coefficient \(\beta_2\) being centered at 0, the lack of a distinction among the model lines in the two locations reflects a conservative assumption that the typical 3pm temperature in Wollongong might be hotter, cooler, or no different than in Uluru on days with similar 9am temperatures.
In short, these prior assumptions reflect that when it comes to Australian weather, we’re just not sure what’s up.

```
set.seed(84735)
%>%
weather_WU add_predicted_draws(weather_model_3_prior, n = 100) %>%
ggplot(aes(x = .prediction, group = .draw)) +
geom_density() +
xlab("temp3pm")
%>%
weather_WU add_fitted_draws(weather_model_3_prior, n = 100) %>%
ggplot(aes(x = temp9am, y = temp3pm, color = location)) +
geom_line(aes(y = .value, group = paste(location, .draw)))
```

### 11.2.3 Simulating the posterior

Finally, let’s combine our prior assumptions with data to simulate the posterior of (11.3).
Instead of starting the `stan_glm()`

syntax from scratch, we can `update()`

the `weather_model_3_prior`

by setting `prior_PD = FALSE`

:

`<- update(weather_model_3_prior, prior_PD = FALSE) weather_model_3 `

This simulation produces 20,000 posterior plausible sets of regression parameters \(\beta_0\) `(Intercept)`

, 9am temperature coefficient \(\beta_1\) (`temp9am`

), location coefficient \(\beta_2\) (`locationWollongong`

), and regression standard deviation \(\sigma\) (`sigma`

):

```
head(as.data.frame(weather_model_3), 3)
(Intercept) temp9am locationWollongong sigma1 13.05 0.8017 -7.663 2.392
2 12.73 0.8174 -7.839 2.445
3 11.81 0.8615 -7.648 2.414
```

Combined, these 20,000 parameter sets present us with 20,000 posterior plausible relationships between temperature and location.
The 100 relationships plotted below provide a representative glimpse.
Across *all* 100 scenarios, 3pm temperature is positively associated with 9am temperature and tends to be higher in Uluru than in Wollongong.
Further, relative to the prior simulated relationships in Figure 11.9, these posterior relationships are very consistent – we have a much clearer understanding of 3pm temperatures in light of information from the `weather_WU`

data.

```
%>%
weather_WU add_fitted_draws(weather_model_3, n = 100) %>%
ggplot(aes(x = temp9am, y = temp3pm, color = location)) +
geom_line(aes(y = .value, group = paste(location, .draw)), alpha = .1) +
geom_point(data = weather_WU, size = 0.1)
```

Inspecting the parameters themselves provides necessary details.
In both Uluru and Wollongong, i.e. **when controlling for location**, there’s an 80% chance that for every one degree increase in 9am temperature, the typical increase in 3pm temperature is somewhere between 0.82 and 0.89 degrees.
**When controlling for 9am temperature**, there’s an 80% chance that the typical 3pm temperature in Wollongong is somewhere between 6.6 and 7.51 degrees *lower* than that in Uluru.

```
# Posterior summaries
posterior_interval(weather_model_3, prob = 0.80,
pars = c("temp9am", "locationWollongong"))
10% 90%
0.8196 0.8945
temp9am -7.5068 -6.5999 locationWollongong
```

### 11.2.4 Posterior prediction

Next, let’s *use* this model to predict 3pm temperature on specific days.
For example, consider a day in which it’s 10 degrees at 9am in both Uluru and Wollongong.
To simulate and subsequently plot the posterior predictive models of 3pm temperatures in these locations, we can call `posterior_predict()`

and `mcmc_areas()`

.
Roughly speaking, we can anticipate 3pm temperatures between 15 and 25 degrees in Uluru, and cooler temperatures between 8 and 18 in Wollongong:

```
# Simulate a set of predictions
set.seed(84735)
<-
temp3pm_prediction posterior_predict(weather_model_3,
newdata = data.frame(temp9am = c(10, 10),
location = c("Uluru", "Wollongong")))
# Plot the posterior predictive models
mcmc_areas(temp3pm_prediction) +
::scale_y_discrete(labels = c("Uluru", "Wollongong")) +
ggplot2xlab("temp3pm")
```

## 11.3 Optional: Utilizing interaction terms

In the two-predictor model in Section 11.2, we assumed that the relationship between 3pm temperature (\(Y\)) and 9am temperature (\(X_1\)) was the *same* in both locations (\(X_2\)).
Or, visually, the relationship between \(Y\) and \(X_1\) had *equal slopes* \(\beta_1\) in both locations.
This assumption isn’t always appropriate – in some cases, our predictors **interact**.
We’ll explore interaction in this *optional* section, but you can skip right past to Section 11.4 if you choose.

Consider the relationship of 3pm temperature (\(Y\)) with location (\(X_2\)) and 9am humidity (\(X_3\)).
Visually, the relationships exhibit quite different slopes – afternoon temperatures and morning humidity are *negatively* associated in Uluru, yet weakly *positively* associated in Wollongong.

```
ggplot(weather_WU, aes(y = temp3pm, x = humidity9am, color = location)) +
geom_point(size = 0.5) +
geom_smooth(method = "lm", se = FALSE)
```

Think about this dynamic another way: the relationship between 3pm temperature and 9am humidity *varies* by location.
Equivalently, the relationship between 3pm temperature and location *varies* by 9am humidity level.
More technically, we say that *the location and humidity predictors* **interact**.

**Interaction**

Two predictors \(X_1\) and \(X_2\) **interact** if the association between \(X_1\) and \(Y\) varies depending upon the level of \(X_2\).

### 11.3.1 Building the model

Consider modeling temperature (\(Y\)) by location (\(X_2\)) and humidity (\(X_3\)).
If we were to assume that location and humidity do *not* interact, then we would describe their relationship by \(\mu = \beta_0 + \beta_1 X_{2} + \beta_2 X_{3}\).
Yet to reflect the fact that the relationship between temperature and humidity is *modified* by location, we can incorporate a new predictor: the **interaction term**.
This new predictor is simply the *product* of \(X_2\) and \(X_3\):

\[\mu = \beta_0 + \beta_1 X_{2} + \beta_2 X_{3} + \beta_3 X_{2}X_{3} \; .\]

Thus the complete structure for our **multivariable Bayesian linear regression model** *with* an **interaction term** is as follows, where the weakly informative priors on the non-intercept parameters are auto-scaled by `stan_glm()`

below:

\[\begin{equation} \begin{array}{lcrl} \text{data:} & \hspace{.05in} & Y_i|\beta_0,\beta_1,\beta_2,\beta_3,\sigma & \stackrel{ind}{\sim} N(\mu_i, \; \sigma^2) \;\; \text{ with } \; \mu_i = \beta_0 + \beta_1X_{i2} + \beta_2X_{i3} + \beta_3X_{i2}X_{i3} \\ \text{priors:} & & \beta_{0c} & \sim N\left(25, 5^2 \right) \\ & & \beta_1 & \sim N\left(0, 37.52^2 \right) \\ & & \beta_2 & \sim N\left(0, 0.82^2 \right) \\ & & \beta_3 & \sim N\left(0, 0.55^2 \right) \\ & & \sigma & \sim \text{Exp}(0.13) \; .\\ \end{array} \tag{11.4} \end{equation}\]

To understand how our assumed structure for \(\mu\) matches our observation of an interaction between 9am humidity and location, let’s break this down into two scenarios, as usual.
**Scenario 1:** In *Uluru*, \(X_{2} = 0\) and the relationship between temperature and humidity simplifies to

\[\mu = \beta_0 + \beta_2 X_{3} \; .\]

**Scenario 2:** In *Wollongong*, \(X_{2} = 1\) and the relationship between temperature and humidity simplifies to

\[\mu = \beta_0 + \beta_1 + \beta_2 X_{3} + \beta_3 X_{3} = (\beta_0 + \beta_1) + (\beta_2 + \beta_3) X_3 \; .\]

Thus we essentially have two types of regression coefficients:

**Uluru coefficients**

As we see in Scenario 1, intercept \(\beta_0\) and humidity (slope) coefficient \(\beta_2\) encode the relationship between temperature and humidity in*Uluru*.**Modifications to the Uluru coefficients**

In comparing Scenario 2 to Scenario 1, the location and interaction coefficients, \(\beta_1\) and \(\beta_3\), encode how the relationship between temperature and humidity in Wollongong*compares*to that in Uluru. Location coefficient \(\beta_1\) captures the*difference*in intercepts, thus how the typical temperature in Wollongong compares to that in Uluru on days with 0% humidity (\(X_3 = 0\)). Interaction coefficient \(\beta_3\) captures the*difference*in slopes, thus how the change in temperature with humidity differs between the two cities.

### 11.3.2 Simulating the posterior

To simulate (11.4) using weakly informative priors, we can represent the multivariable model structure by `temp3pm ~ location + humidity9am + location:humidity9am`

, the last piece of this formula representing the interaction term:

```
<- stan_glm(
interaction_model ~ location + humidity9am + location:humidity9am,
temp3pm data = weather_WU, family = gaussian,
prior_intercept = normal(25, 5),
prior = normal(0, 2.5, autoscale = TRUE),
prior_aux = exponential(1, autoscale = TRUE),
chains = 4, iter = 5000*2, seed = 84735)
```

Posterior summary statistics for our regression parameters (\(\beta_0, \beta_1, \beta_2, \beta_3, \sigma\)) are printed below.
The corresponding posterior median relationships between temperature and humidity by city reflect what we observed in the data – the association between temperature and humidity is *negative* in Uluru and slightly *positive* in Wollongong:

\[\begin{array}{lrl} \text{Uluru:} & \mu & = 37.586 - 0.19 \text{ humidity9am} \\ \text{Wollongong:} & \mu & = (37.586 - 21.879) + (-0.19 + 0.246) \text{ humidity9am}\\ && = 15.707 + 0.056 \text{ humidity9am}\\ \end{array}\]

```
# Posterior summary statistics
tidy(interaction_model, effects = c("fixed", "aux"))
term estimate std.error1 (Intercept) 37.5856 0.90977
2 locationWollongong -21.8795 2.32868
3 humidity9am -0.1895 0.01931
4 locationWollongong:humidity9am 0.2455 0.03751
5 sigma 4.4699 0.22673
6 mean_PPD 24.5610 0.44834
posterior_interval(interaction_model, prob = 0.80,
pars = "locationWollongong:humidity9am")
10% 90%
:humidity9am 0.1973 0.2941 locationWollongong
```

The 200 posterior simulated models in Figure 11.13 capture our posterior uncertainty in these relationships and provide ample posterior evidence of a *significant* interaction between location and humidity – almost all Wollongong posterior models are *positive* and all Uluru posterior models are *negative*.
We can back this up with numerical evidence.
The 80% posterior credible interval for interaction coefficient \(\beta_3\) is entirely and well above 0, ranging from 0.197 to 0.294, suggesting that the association between temperature and humidity is *significantly more positive* in Wollongong than in Uluru.

```
%>%
weather_WU add_fitted_draws(interaction_model, n = 200) %>%
ggplot(aes(x = humidity9am, y = temp3pm, color = location)) +
geom_line(aes(y = .value, group = paste(location, .draw)), alpha = 0.1)
```

### 11.3.3 Do you need an interaction term?

Now that you’ve seen that interaction terms allow us to capture even more nuance in a relationship, you might feel eager to include them in every model.
Don’t.
There’s nothing sophisticated about a model stuffed with unnecessary predictors.
Though interaction terms *are* quite useful in some settings, they can overly complicate our models in others.
Here are a few things to consider when weighing whether or not to go down the rabbit hole of interaction terms:

**Context.**In the context of your analysis, does it make*sense*that the relationship between \(Y\) and one predictor \(X_1\)*varies*depending upon the value of another predictor \(X_2\)?**Visualizations.**As with our example here, interactions might reveal themselves when visualizing the relationships between \(Y\), \(X_1\), and \(X_2\).**Hypothesis tests.**Suppose we do include an interaction term in our model, \(\mu = \beta_0 + \beta_1X_1 + \beta_2X_2 + \beta_3X_1X_2\). If there’s significant posterior evidence that \(\beta_3 \ne 0\), then it can make sense to include the interaction term. Otherwise, it’s typically a good idea to get rid of it.

Let’s practice the first two ideas with some informal examples.
The `bike_users`

data is from the same source as the `bikes`

data in Chapter 9.
Like the `bikes`

data, it includes information about daily Capital Bikeshare ridership.
Yet `bike_users`

contains data on *both* registered, paying bikeshare members (who tend to ride more often and use the bikes for commuting) and casual riders (who tend to just ride the bikes every so often):

```
data(bike_users)
%>%
bike_users group_by(user) %>%
tally()
# A tibble: 2 x 2
user n<fct> <int>
1 casual 267
2 registered 267
```

We’ll use this data as a *whole* and to examine patterns among casual riders alone and registered riders alone:

```
<- bike_users %>%
bike_casual filter(user == "casual")
<- bike_users %>%
bike_registered filter(user == "registered")
```

To begin, let \(Y_c\) denote the number of casual riders and \(Y_r\) denote the number of registered riders on a given day. As with model (11.4), consider an analysis of ridership in which we have two predictors, one quantitative and one categorical: temperature (\(X_1\)) and weekend status (\(X_2\)). The observed relationships of \(Y_c\) and \(Y_r\) with \(X_1\) and \(X_2\) are shown below. Syntax is included for the former and is similar for the latter.

```
ggplot(bike_casual, aes(y = rides, x = temp_actual, color = weekend)) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "casual riders")
```

In their relationship with casual ridership \(Y_c\) (left), it seems that temperature and weekend status *do* interact.
Ridership increases with temperature on both weekends and weekdays, yet the *rate* of this increase *differs*, being more rapid on weekends.
In contrast, in their relationship with registered ridership \(Y_r\) (right), it seems that temperature and weekend status do *not* interact.
Ridership increases with temperature at roughly the *same* rate on weekends and weekdays.
Both observations *make sense*.
Whereas registered riders tend to be more utility driven in their bikeshare use, casual riders tend to hop on for more occasional fun.
Thus casual ridership, more than registered ridership, might see disproportional increases in ridership on warm weekends than on warm weekdays.

Next, in a model of casual ridership \(Y_c\), suppose that we have two quantitative predictors: temperature (\(X_1\)) and humidity (\(X_2\)).
Figure 11.15 takes two approaches to visualizing this relationship.
The left plot utilizes color, yet the fact that this relationship lives in three dimensions makes it more challenging to identify any trends.
For visualization purposes then, one strategy is to “cut” one predictor (here humidity) into categories (here high and low) based on its quantitative scale (Figure 11.15 right).
Though “cutting” or “discretizing” humidity greatly oversimplifies the relationship among our original variables, it does reveal a *potential* interaction between \(X_1\) and \(X_2\).
Mainly, it appears that the increase in ridership with temperature is slightly quicker when the humidity is low.
If you’ve ever biked in warm, humid weather, *this makes sense*.
High humidity can make hot weather pretty miserable.
Our plots provide evidence that other people might agree.

```
ggplot(bike_casual, aes(y = rides, x = temp_actual, color = humidity)) +
geom_point()
ggplot(bike_casual,
aes(y = rides, x = temp_actual,
color = cut(humidity, 2, labels = c("low","high")))) +
geom_smooth(method = "lm", se = FALSE) +
labs(color = "humidity_level") +
lims(y = c(0, 2500))
```

Finally, consider what it would mean for *categorical* predictors to interact.
Let’s model \(Y\), the total ridership on any given day, by three potential predictors: user type \(X_1\) (casual or registered), weather category \(X_2\), and weekend status \(X_3\).
The three weather categories here range from “1,” pleasant weather, to “3,” more severe weather.
Figure 11.16 plots \(Y\) versus \(X_1\) and \(X_2\) (left) and \(Y\) versus \(X_1\) and \(X_3\) (right).
Upon examining the results, take the following quiz.

- In their relationship with ridership, do user type and weather category appear to interact?
- In their relationship with ridership, do user type and weekend status appear to interact?
- In the context of our Capital Bikeshare analysis, explain why your answers to 1 and 2 make sense.

```
# Example syntax
ggplot(bike_users, aes(y = rides, x = user, fill = weather_cat)) +
geom_boxplot()
```

In their relationship with ridership, user type and weather category do not appear to interact, at least not significantly.
Among both casual and registered riders, ridership tends to decrease as weather worsens.
Further, the degree of these decreases from one weather category to the next are certainly not equal, but they are *similar*.
In contrast, in their relationship with ridership, user type and weekend status *do* appear to interact – the relationship between ridership and weekend status *varies* by user status.
Whereas casual ridership is greater on weekends than on weekdays, registered ridership is greater on weekdays.
Again, we might have *anticipated* this interaction given that casual and registered riders tend to use the bikeshare service to different ends.

The examples above have focused on examining interactions through visualizations and context.
It remains to determine whether these interactions are actually *significant* or meaningful, thus whether we should include them in the corresponding models.
To this end, we could simulate each model and formally test the significance of the interaction coefficient.
In our opinion, after building up the necessary intuition, this formality is the easiest step.

## 11.4 Dreaming bigger: utilizing more than 2 predictors!

Let’s return to our Australian weather analysis.
We can keep going!
To improve our understanding and posterior predictive accuracy of afternoon temperatures, we can incorporate more and more predictors into our model.
Now that you’ve explored models that include a quantitative predictor, a categorical predictor, and *both*, you are equipped to generalize regression models to include any number of predictors.
Let’s revisit the `weather_WU`

data:

```
%>%
weather_WU names()
1] "location" "windspeed9am" "humidity9am"
[4] "pressure9am" "temp9am" "temp3pm" [
```

Beyond `location`

(\(X_1\)), `temp9am`

(\(X_2\)), and `humidity9am`

(\(X_3\)), our sample data includes two more potential predictors of `temp3pm`

: `windspeed9am`

(km/hr) and atmospheric `pressure9am`

(hpa) denoted \(X_4\) and \(X_5\), respectively.
We’ll put it all out there for our final model of Chapter 11 by including all five possible predictors of \(Y\), without the complication of interaction terms.
Simply to preserve space as our models grow, we do not write out the weakly informative priors for the regression coefficients (\(\beta_1, \beta_2, \ldots, \beta_5\)).
These are all Normal and centered 0, with prior standard deviations that can be obtained by the `prior_summary()`

below:

\[\begin{equation} \begin{array}{lcrl} \text{data:} & \hspace{.05in} & Y_i|\beta_0,\beta_1,\ldots,\beta_5,\sigma & \stackrel{ind}{\sim} N(\mu_i, \; \sigma^2) \;\; \text{ with } \;\; \mu_i = \beta_0 + \beta_1X_{i1} + \cdots + \beta_5X_{i5} \\ \text{priors:} & & \beta_{0c} & \sim N(25, 5^2) \\ && \beta_1, \ldots, \beta_5 & \sim N(0, \text{(some weakly informative sd)}^2) \\ & & \sigma & \sim \text{Exp}(0.13) \; .\\ \end{array} \tag{11.5} \end{equation}\]

For our previous three models of `temp3pm`

, we paused to define each and every model parameter.
Here we have seven: (\(\beta_0, \beta_1, \ldots, \beta_5, \sigma\)).
It’s excessive and unnecessary to define each.
Rather, we can turn to the *principles* that we developed throughout our first three model analyses.

**Principles for interpreting multivariable model parameters**

In a multivariable regression model of \(Y\) informed by \(p\) predictors \((X_1,X_2,\ldots,X_p)\),

\[\mu = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \beta_p X_p \; ,\]

we can interpret the \(\beta_i\) regression coefficients as follows.
First, the intercept coefficient \(\beta_0\) represents the typical \(Y\) outcome when all predictors are 0, \(X_1 = X_2 = \cdots = X_p = 0\).
Further, *when controlling for* or *holding constant* the other \(X_j\) predictors in our model:

- the \(\beta_i\) coefficient corresponding to a
**quantitative predictor**\(X_{i}\) can be interpreted as the typical change in \(Y\) per one unit increase in \(X_{i}\); - the \(\beta_i\) coefficient corresponding to a
**categorical level**\(X_{i}\) can be interpreted as the typical*difference*in \(Y\) for level \(X_{i}\) versus the reference or baseline level.

As such, our interpretation of any predictor’s coefficient depends in part on what other predictors are in our model.

Let’s put these principles into practice in our analysis of (11.5).
To simulate the model posterior, we define the model structure using the shortcut syntax `temp3pm ~ .`

instead of `temp3pm ~ location + windspeed9am + (the rest of the predictors!)`

.
Without having to write them all out, this indicates that we’d like to use all `weather`

predictors in our model.^{52}

```
<- stan_glm(
weather_model_4 ~ .,
temp3pm data = weather_WU, family = gaussian,
prior_intercept = normal(25, 5),
prior = normal(0, 2.5, autoscale = TRUE),
prior_aux = exponential(1, autoscale = TRUE),
chains = 4, iter = 5000*2, seed = 84735)
# Confirm prior specification
prior_summary(weather_model_4)
# Check MCMC diagnostics
mcmc_trace(weather_model_4)
mcmc_dens_overlay(weather_model_4)
mcmc_acf(weather_model_4)
```

We can now pick through the posterior simulation results for our seven model parameters, here simplified to their corresponding 95% credible intervals:

```
# Posterior summaries
posterior_interval(weather_model_4, prob = 0.95)
2.5% 97.5%
-23.92282 98.96185
(Intercept) -7.19827 -5.66547
locationWollongong -0.05491 0.02975
windspeed9am -0.05170 -0.01511
humidity9am -0.08256 0.03670
pressure9am 0.72893 0.87456
temp9am 2.10497 2.57180 sigma
```

These intervals provide insight into some big picture questions.
When controlling for the other model predictors, which predictors are **significantly associated** with `temp3pm`

?
Are these associations **positive or negative**?
Try to answer these big picture questions for yourself in the quiz below.
Though there’s some gray area, one set of reasonable answers is in the footnotes.^{53}

When controlling for the other predictors included in `weather_model_4`

, which predictors…

- have a significant
*positive*association with`temp3pm`

? - have a significant
*negative*association with`temp3pm`

? - are
*not*significantly associated with`temp3pm`

?

Let’s see how you did.
To begin, the 95% posterior credible interval for the `temp9am`

coefficient \(\beta_1\), (0.73, 0.87), is the only one that lies entirely *above* 0.
This provides us with hearty evidence that, even when controlling for the four other predictors in the model, there’s a significant **positive association** between morning and afternoon temperatures.
In contrast, the 95% posterior credible intervals for the `locationWollongong`

and `humidity9am`

coefficients lie entirely *below* 0, suggesting that both factors are **negatively associated** with `temp3pm`

.
For example, when controlling for the other model factors, there’s a 95% chance that the typical temperature in Wollongong is between 5.67 and 7.2 degrees lower than in Uluru.
The `windspeed9am`

and `pressure9am`

coefficients are the only to have 95% credible intervals which straddle 0.
Though both intervals lie mostly *below* 0, suggesting afternoon temperature is negatively associated with morning windspeed and atmospheric pressure when controlling for the other model predictors, the waffling evidence invites some skepticism and follow-up questions.

## 11.5 Model evaluation & comparison

Throughout Chapter 11, we’ve explored five different models of 3pm temperatures in Australia. We’ll consider four of these here:

Model | Formula |
---|---|

`weather_model_1` |
`temp3pm ~ temp9am` |

`weather_model_2` |
`temp3pm ~ location` |

`weather_model_3` |
`temp3pm ~ temp9am + location` |

`weather_model_4` |
`temp3pm ~ .` |

Naturally, you might have wondered which of these is the “best” model of `temp3pm`

.
To solve this mystery, we can explore our three model evaluation questions from Chapter 10:

How

*fair*is each model?

Since the context for their analysis and data collection is the same, these four models are equally*fair*.How

*wrong*is each model?

Visual posterior predictive checks (Figure 11.17) suggest that the assumed structures underlying`weather_model_3`

and`weather_model_4`

better capture the bimodality in`temp3pm`

. Thus these models are*less wrong*than`weather_model_1`

and`weather_model_2`

.How

*accurate*are each model’s posterior predictions?

We’ll address this question below using the three approaches we learned in Chapter 10: visualization, cross-validation, and ELPD.

```
# Posterior predictive checks. For example:
pp_check(weather_model_1)
```

### 11.5.1 Evaluating predictive accuracy using visualizations

Visualizations provide a powerful first approach to assessing the quality of our models’ posterior predictions.
To begin, we’ll use all four models to construct posterior predictive models for each case in the `weather`

dataset.
Syntax is provided for `weather_model_1`

and is similar for the other models.

```
set.seed(84735)
<- posterior_predict(weather_model_1, newdata = weather_WU) predictions_1
```

Figure 11.18 illustrates the resulting posterior predictive models of `temp3pm`

from `weather_model_1`

(left) and `weather_model_2`

(right).
Both accurately capture the observed behavior in `temp3pm`

– the majority of observed 3pm temperatures fall within the bounds of their predictive models based on 9am temperatures alone (`weather_model_1`

) or location alone (`weather_model_2`

).

```
# Posterior predictive models for weather_model_1
ppc_intervals(weather_WU$temp3pm, yrep = predictions_1,
x = weather_WU$temp9am, prob = 0.5, prob_outer = 0.95) +
labs(x = "temp9am", y = "temp3pm")
# Posterior predictive models for weather_model_2
ppc_violin_grouped(weather_WU$temp3pm, yrep = predictions_2,
group = weather_WU$location, y_draw = "points") +
labs(y = "temp3pm")
```

Further, not only do the `weather_model_3`

posterior predictive models also well anticipate the observed 3pm temperatures (Figure 11.19), they’re much *narrower* than those from `weather_model_1`

and `weather_model_2`

.
Essentially, using both `temp9am`

and `location`

, instead of either predictor alone, produces more precise posterior predictions of `temp3pm`

.

```
# Posterior predictive models for weather_model_3
ppc_intervals_grouped(weather_WU$temp3pm, yrep = predictions_3,
x = weather_WU$temp9am, group = weather_WU$location,
prob = 0.5, prob_outer = 0.95,
facet_args = list(scales = "fixed")) +
labs(x = "temp9am", y = "temp3pm")
```

What about `weather_model_4`

?!
Well, with five predictors, this high dimensional model is simply too difficult to visualize.
We thus have three motivations for not assessing posterior predictive accuracy using visual evidence alone: (1) we can’t actually visualize the posterior predictive quality of every model; (2) even if we could, it can be difficult to ascertain the comparative predictive quality of our models from visuals alone; and (3) the visuals above illustrate how well our models predict the same data points we used to build them (not how well they’d predict temperatures on days we haven’t yet observed), thus might give overly optimistic assessments of predictive accuracy.
This is where cross-validated and ELPD numerical summaries can really help.

### 11.5.2 Evaluating predictive accuracy using cross-validation

To numerically assess the posterior predictive quality of our four models, we’ll apply the \(k\)-fold cross-validation procedure from Chapter 10.
Recall that this procedure effectively trains the model on one set of data and tests it on another (multiple times over), producing estimates of posterior predictive accuracy that are more honest than if we trained and tested the model using the same data.
To this end, we’ll run a 10-fold cross-validation for each of our four weather models.
Code is shown for `weather_model_1`

and can be extended to the other three models:

```
set.seed(84735)
prediction_summary_cv(model = weather_model_1, data = weather_WU, k = 10)
```

Table 11.1 summarizes the resulting cross-validation statistics.

model | mae | mae scaled | within 50 | within 95 |
---|---|---|---|---|

weather model 1 | 3.285 | 0.79 | 0.405 | 0.97 |

weather model 2 | 3.653 | 0.661 | 0.495 | 0.935 |

weather model 3 | 1.142 | 0.483 | 0.67 | 0.96 |

weather model 4 | 1.206 | 0.522 | 0.64 | 0.95 |

Utilize Table 11.1 to compare our four models.

- If your goal were to explore the relationship between
`temp3pm`

and`location`

without controlling for any other factors, which model would you use? - If your goal were to maximize the predictive quality of your model and you could only choose
*one*predictor to use in your model, would you choose`temp9am`

or`location`

? - Which of the four models produces the best overall predictions?

Quiz questions 1 through 3 present different frameworks by which to define which of our four models is “best.”
By the framework defined in Question 1, `weather_model_2`

is best – it’s the only model which studies the exact relationship of interest (that of `temp3pm`

versus `location`

).
By the framework defined in Question 2 in which we can only pick one predictor, `weather_model_1`

seems best.
That is, `temp9am`

alone seems a better predictor of `temp3pm`

than `location`

alone.
Across all test cases, the posterior mean predictions of `temp3pm`

calculated from `weather_model_1`

tend to be 3.3 degrees from the *observed* 3pm temperature.
The error is higher among the posterior mean predictions calculated from `weather_model_2`

(3.7 degrees).^{54}

Finally, by the framework defined by Question 3, we’re assuming that we can utilize any number or combination of the available predictors.
With this flexibility, `weather_model_3`

appears to provide the “best” posterior predictions of `temp3pm`

– it has the smallest `mae`

and `mae_scaled`

among the four models and among the highest `within_50`

and `within_95`

coverage statistics.
Let’s put this into context.
In utilizing both `temp9am`

and `location`

predictors, `weather_model_3`

convincingly outperforms the models which use either predictor alone (`weather_model_1`

and `weather_model_2`

).
Further, `weather_model_3`

seems to *narrowly* outperform that which uses all five predictors (`weather_model_4`

).
In model building, *more isn’t always better*.
The fact that `weather_model_4`

has higher prediction errors indicates that its additional three predictors (`windspeed9am`

, `humidity9am`

, `pressure9am`

) don’t substantially improve our understanding about `temp3pm`

if we already have information about `temp9am`

and `location`

.
Thus in the name of simplicity and efficiency, we would be happy to pick the smaller `weather_model_3`

.

### 11.5.3 Evaluating predictive accuracy using ELPD

In closing out this section, we’ll compare the posterior predictive accuracy of our four models with respect to their **expected log-predictive densities (ELPD)**.
Recall the basic idea from Section 10.3.3: the larger the expected logged posterior predictive pdf across a new set of data points \(y_{\text{new}}\), \(\log(f(y_{\text{new}} | y))\), the more accurate the posterior predictions of \(y_{\text{new}}\).
To begin, calculate the ELPD for each model:

```
# Calculate ELPD for the 4 models
set.seed(84735)
<- loo(weather_model_1)
loo_1 <- loo(weather_model_2)
loo_2 <- loo(weather_model_3)
loo_3 <- loo(weather_model_4)
loo_4
# Results
c(loo_1$estimates[1], loo_2$estimates[1],
$estimates[1], loo_4$estimates[1])
loo_31] -568.4 -625.7 -461.1 -457.6 [
```

Though the ELPDs don’t provide interpretable metrics for the posterior predictive accuracy of any *single* model, they *are* useful in *comparing* the posterior predictive accuracy of multiple models.
We can do so using `loo_compare()`

:

```
# Compare the ELPD for the 4 models
loo_compare(loo_1, loo_2, loo_3, loo_4)
elpd_diff se_diff0.0 0.0
weather_model_4 -3.5 4.0
weather_model_3 -110.8 18.1
weather_model_1 -168.1 21.5 weather_model_2
```

To begin, `loo_compare()`

lists the models in order from from best to worst, or from the highest ELPD to the lowest: `weather_model_4`

, `weather_model_3`

, `weather_model_1`

, `weather_model_2`

.
The remaining output details the *extent* to which each model compares to the best model, `weather_model_4`

.
For example, the ELPD for `weather_model_3`

is estimated to be 3.5 lower (worse) than that of `weather_model_4`

(-461.1 - -457.6).
Further, this *estimated* difference in ELPD has a corresponding **standard error** of 4.0.
That is, we believe that the *true* difference in the ELPDs for `weather_model_3`

and `weather_model_4`

is within roughly 2 standard errors, or 8 units, of the estimated -3.5 difference.

The ELPD model comparisons are consistent with our conclusions based upon the 10-fold cross-validation comparisons.
Mainly, `weather_model_1`

outperforms `weather_model_2`

, and `weather_model_3`

outperforms both of them.
Further, the distinction between `weather_model_3`

and `weather_model_4`

is cloudy.
Though the ELPD for `weather_model_4`

is slightly higher (better) than that of `weather_model_3`

, it is not *significantly* higher.
An ELPD difference of *zero* is within two standard errors of the estimated difference: -3.5 \(\pm\) 2*4 = (-11.5, 4.5).
Hence we don’t have strong evidence that the posterior predictive accuracy of `weather_model_4`

is significantly superior to that of `weather_model_3`

, or vice versa.
Again, since `weather_model_3`

is *simpler* and achieves similar predictive accuracy, we’d personally choose to use it over the more complicated `weather_model_4`

.

**Comparing models via ELPD**

Let \(\text{ELPD}_1\) and \(\text{ELPD}_2\) denote the estimated ELPDs for two different models, models 1 and 2.
Further, let \(\text{se}_{\text{diff}}\) denote the standard error of the estimated difference in the ELPDs, \(\text{ELPD}_2 - \text{ELPD}_1\).
Then the posterior predictive accuracy of model 1 is *significantly* greater than that of model 2 if:

- \(\text{ELPD}_1 > \text{ELPD}_2\); and
- \(\text{ELPD}_2\) is at least two standard errors below \(\text{ELPD}_1\). Equivalently, the difference in \(\text{ELPD}_2\) and \(\text{ELPD}_1\) is at least two standard errors below 0: \((\text{ELPD}_2 - \text{ELPD}_1) + 2\text{se}_{\text{diff}} < 0\).

### 11.5.4 The bias-variance trade-off

Our four weather models illustrate the fine balance we must strike in building statistical models.
If our goal is to build a model that produces accurate predictions of our response variable \(Y\), we want to include *enough* predictors so that we have ample information about \(Y\), but not *so many* predictors that our model becomes **overfit** to our sample data.
This riddle is known as the **bias-variance trade-off**.
To examine this trade-off, we will take two *separate* 40-day samples of Wollongong weather:

```
# Take 2 separate samples
set.seed(84735)
<- weather_australia %>%
weather_shuffle filter(temp3pm < 30, location == "Wollongong") %>%
sample_n(nrow(.))
<- weather_shuffle %>% head(40)
sample_1 <- weather_shuffle %>% tail(40) sample_2
```

We’ll use both samples to separately explore the relationship between 3pm temperature (\(Y\)) by day of year (\(X\)), from 1 to 365.
The pattern that emerges in the `sample_1`

data is what you might expect for a city in the southern hemisphere.
Temperatures are higher at the beginning and end of the year, and lower in the middle of the year:

```
# Save the plot for later
<- ggplot(sample_1, aes(y = temp3pm, x = day_of_year)) +
g geom_point()
g
```

Consider three different **polynomial models** for the relationship between temperature and day of year:

\[\begin{array}{lrl} \text{model 1:} & \mu & = \beta_0 + \beta_1 X \\ \text{model 2:} & \mu & = \beta_0 + \beta_1 X + \beta_2 X^2\\ \text{model 3:} & \mu & = \beta_0 + \beta_1 X + \beta_2 X^2 + \beta_3 X^3 + \cdots + \beta_{12} X^{12} \\ \end{array}\]

Don’t panic.
We don’t expect you to interpret the polynomial terms or their coefficients.
Rather, we want you to take in the fact that each polynomial term, a transformation of \(X\), is a separate predictor.
Thus model 1 has 1 predictor, model 2 has 2, and model 3 has 12.
In obtaining posterior estimates of these three models, we know two things from past discussions.
First, our two different data samples will produce different estimates (they’re using different information!).
Second, models 1, 2, and 3 vary in quality – one is better than the others.
Before examining the nuances, take a quick quiz.^{55}

- Which of models 1, 2, and 3 would be the most
**variable**from sample to sample? That is, for which model will the sample 1 and 2 estimates of the relationship between temperature and day of year differ the most? - Which of models 1, 2, and 3 is the most
**biased**? That is, which model will tend to be the furthest from the observed behavior in the data?

Figure 11.21 illustrates the three different models as estimated by both samples 1 and 2. For sample 1, these figures can be replicated as follows:

```
+ geom_smooth(method = "lm", se = FALSE)
g + stat_smooth(method = "lm", se = FALSE, formula = y ~ poly(x, 2))
g + stat_smooth(method = "lm", se = FALSE, formula = y ~ poly(x, 12)) g
```

Let’s connect what we see in this figure with some technical concepts and terminology.
Starting at one extreme, model 1 assumes a linear relationship between temperature and day of year.
Samples 1 and 2 produce similar estimates of this linear relationship.
This stability is reassuring – no matter what data we happen to have, our posterior understanding of model 1 will be similar.
However, model 1 turns out to be *overly simple* and *rigid*.
It systematically underestimates temperatures on summer days and overestimates temperatures on winter days.
Putting this together, we say that model 1 has **low variance** from sample to sample, but **high bias**.

To correct this bias, we can incorporate more *flexibility* into our model with the inclusion of more predictors, here polynomial terms.
Yet we can get carried away.
Consider the extreme case of model 3 which assumes a 12th order polynomial relationship between temperature and day of year.
Within both samples, model 3 *does* seem to be better than model 1 at following the trends in the relationship, hence is *less biased*.
However, this decrease in bias comes at a cost.
Since model 3 is structured to pick up tiny, sample-specific details in the relationship, samples 1 and 2 produce quite different estimates of model 3, or two very distinct wiggly model lines.
In this case, we say that model 3 has **low bias** but **high variance** from sample to sample.
Utilizing this highly variable model would have two serious consequences.
First, the results would be **unstable** – different data might produce very different model estimates, hence conclusions about the relationship between temperature and day of year.
Second, the results would be **overfit** to our sample data – the tiny, local trends in *our* sample likely don’t extend to the general daily weather patterns in Wollongong.
As a result, this model wouldn’t do a good job of predicting temperatures for *future* days.

Bringing this all together, in assuming a quadratic structure for the relationship between temperature and day of year, model 2 provides some good middle ground. It is neither too biased (simple) nor too variable (overfit). That is, it strikes a nice balance in the bias-variance trade-off.

**Bias-variance trade-off**

A model is said to have **high bias** if it tends to be “far” from the observed relationship in the data; and **high variance** if estimates of the model significantly differ depending upon what data is used.
In model building, there are trade-offs between bias and variability:

- Overly simple models with few or no predictors tend to have
*high bias*but*low variability*(high stability). - Overly complicated models with lots of predictors tend to have
*low bias*but*high variability*(low stability).

The goal is to build a model which strikes a good balance, enjoying relatively low bias *and* low variance.

Though the bias-variance trade-off speaks to the performance of a model across multiple *different* datasets, we don’t actually have to go out and collect multiple datasets to hedge against the extremes of the bias-variance trade-off.
Nor do we have to rely on intuition and guesswork.
The raw and cross-validated posterior prediction summaries that we’ve utilized throughout Chapters 10 and 11 can help us identify overly simple models and overfit models.
To explore this process with our three models of `temp3pm`

by `day_of_year`

, we first simulate the posteriors for each model.
We provide complete syntax for `model_1`

.
The syntax for `model_2`

and `model_3`

is similar, but would use formulas `temp3pm ~ poly(day_of_year, 2)`

and `temp3pm ~ poly(day_of_year, 12)`

, respectively.

```
<- stan_glm(
model_1 ~ day_of_year,
temp3pm data = sample_1, family = gaussian,
prior_intercept = normal(25, 5),
prior = normal(0, 2.5, autoscale = TRUE),
prior_aux = exponential(1, autoscale = TRUE),
chains = 4, iter = 5000*2, seed = 84735)
# Ditto the syntax for models 2 and 3
<- stan_glm(temp3pm ~ poly(day_of_year, 2), ...)
model_2 <- stan_glm(temp3pm ~ poly(day_of_year, 12), ...) model_3
```

Next, we calculate raw and cross-validated posterior prediction summaries for each model.
We provide syntax for `model_1`

– it’s similar for `model_2`

and `model_3`

.

```
set.seed(84735)
prediction_summary(model = model_1, data = sample_1)
prediction_summary_cv(model = model_1, data = sample_1, k = 10)$cv
```

Check out the results in Table 11.2.
By both measures, raw and cross-validated, `model_1`

tends to have the greatest prediction errors.
This suggests that `model_1`

is **overly simple**, or possibly biased, in comparison to the other two models.
At the other extreme, notice that the cross-validated prediction error for `model_3`

(2.12 degrees) is roughly double the raw prediction error (1.06 degrees).
Thus `model_3`

is doubly worse at predicting temperatures for days we haven’t yet observed than temperatures for days in the `sample_1`

data.
This suggests that `model_3`

is **overfit**, or overly optimized, to the `sample_1`

data we used to build it.
As such, the discrepancy in its raw and cross-validated prediction errors tips us off that `model_3`

has low bias but **high variance** – a *different* sample of data might lead to very different posterior results.
Between the extremes of `model_1`

and `model_3`

, `model_2`

presents the best option with a *relatively* low raw prediction error and the *lowest* cross-validated prediction error.

model | raw mae | cross-validated mae |
---|---|---|

model 1 | 2.79 | 2.83 |

model 2 | 1.53 | 1.86 |

model 3 | 1.06 | 2.12 |

## 11.6 Chapter summary

In Chapter 11, we expanded our regression toolkit to include **multivariable** Bayesian Normal linear regression models.
Letting \(Y\) denote a quantitative response variable and (\(X_1,X_2,\ldots,X_p\)) a set of \(p\) predictor variables which can be quantitative, categorical, interaction terms, or any combination thereof:

\[\begin{array}{lcrl} \text{data:} & \hspace{.05in} & Y_i|\beta_0,\beta_1,\ldots,\beta_p,\sigma & \stackrel{ind}{\sim} N(\mu_i, \; \sigma^2) \;\; \text{ with } \; \mu_i = \beta_0 + \beta_1X_{i1} + \cdots + \beta_p X_{ip} \\ \text{priors:} & & \beta_{0c} & \sim N\left(m_0, s_0^2 \right) \\ & & \beta_{1} & \sim N\left(m_1, s_1^2 \right) \\ && \vdots & \\ & & \beta_{p} & \sim N\left(m_p, s_p^2 \right) \\ & & \sigma & \sim \text{Exp}(l) \; .\\ \end{array}\]

By including more than one predictor, multivariable regression can improve our predictions and understanding of \(Y\).
Yet we can take it *too* far.
This conundrum is known as the **bias-variance trade-off**.
If our model is too simple or rigid (including, say, only one predictor), its estimate of the model mean \(\mu\) can be **biased**, or far from the actual observed relationship in the data.
If we greedily include more and more and more predictors into a regression model, its estimates of \(\mu\) can become **overfit** to our sample data and highly **variable** from sample to sample.
Thus in model building, we aim to strike a balance – a model which enjoys relatively low bias and low variability.

## 11.7 Exercises

### 11.7.1 Conceptual Exercises

**Exercise 11.1 (Why multiple predictors?)**Briefly explain why we might want to build a regression model with more than one predictor.

**Exercise 11.2 (Categorical predictors: Cars)**Let’s say that you want to model a car’s miles per gallon in a city (\(Y\)) by the make of the car: Ford, Kia, Subaru, or Toyota. This relationship can be written as \(\mu = \beta_0 + \beta_1X_{1} + \beta_2X_{2}+\beta_3X_{3}\), where \(X_1, X_2, X_3\) are indicators for whether or not the cars are Kias, Subarus, or Toyotas, respectively:

- Explain why there is no indicator term for the Ford category.
- Interpret the regression coefficient \(\beta_2\).
- Interpret the regression coefficient \(\beta_0\).

**Exercise 11.3 (Categorical and quantitative predictors: Tomatoes)**You have recently taken up the hobby of growing tomatoes and hope to learn more about the factors associated with bigger tomatoes. As such, you plan to model a tomato’s weight in grams (\(Y\)) by the number of days it has been growing (\(X_1\)) and its type, Mr. Stripey or Roma. Suppose the expected weight of a tomato is a linear function of its age and type, \(\mu = \beta_0 + \beta_1X_{1} + \beta_2X_{2}\), where \(X_2\) is an indicator for Roma tomatoes.

- Interpret each regression coefficient, \(\beta_0\), \(\beta_1\), and \(\beta_2\).
- What would it mean if \(\beta_2\) were equal to zero?

**Exercise 11.4 (Interactions: Tomatoes)**Continuing your quest to understand tomato size, you incorporate an interaction term between the tomato grow time (\(X_1\)) and type (\(X_2\)) into your model: \(\mu = \beta_0 + \beta_1X_{1} + \beta_2X_{2}+ \beta_3X_{1}X_{2}\).

- Explain, in context, what it means for \(X_1\) and \(X_2\) to interact.
- Interpret \(\beta_3\).

**Exercise 11.5 (Interaction terms)**

- Sketch a model that
*would*benefit from including an interaction term between a categorical and quantitative predictor. - Sketch a model that would
*not*benefit from including an interaction term between a categorical and quantitative predictor. - Besides visualization, what are two other ways to determine if you should include interaction terms in your model?

**Exercise 11.6 (Improving your model: shoe size)**Let’s say you model a child’s shoe size (\(Y\)) by two predictors: the child’s age in years (\(X_1\)) and an indicator of whether the child knows how to swim (\(X_2\)).

- Generally speaking, why can it be beneficial to add predictors to models?
- Generally speaking, why can it be beneficial to remove predictors from models?
- What might you
*add*to this model to improve your predictions of shoe size? Why? - What might you
*remove*from this model to improve it? Why?

**Exercise 11.7 (What makes a good model?)**We don’t expect our regression models to be perfect, but we do want to do our best. It can be helpful to think about what we want and expect from our models.

- What are qualities of a good model?
- What are qualities of a bad model?

**Exercise 11.8 (Is our model good / better?)**What techniques have you learned in this chapter to assess and compare your models? Give a brief explanation for each technique.

**Exercise 11.9 (Bias-variance trade-off)**In your own words, briefly explain what the bias-variance tradeoff is and why it is important.

### 11.7.2 Applied exercises

In the next exercises you will use the `penguins_bayes`

data in the **bayesrules** package to build various models of penguin `body_mass_g`

. Throughout, we’ll utilize weakly informative priors and a basic understanding that the average penguin weighs somewhere between 3,500 and 4,500 grams. Further, one predictor of interest is penguin `species`

: Adelie, Chinstrap, or Gentoo. We’ll get our first experience with a 3-level predictor like this in Chapter 12. If you’d like to work with only 2 levels as you did in Chapter 11, you can utilize the `penguin_data`

which includes only Adelie and Gentoo penguins:

```
# Alternative penguin data
<- penguins_bayes %>%
penguin_data filter(species %in% c("Adelie", "Gentoo"))
```

**Exercise 11.10 (Penguins! Main effects)**Let’s begin our analysis of penguin

`body_mass_g`

by exploring its relationship with `flipper_length_mm`

and `species`

.
- Plot and summarize the observed relationship between these three variables among the sampled penguins.
- Use
`stan_glm()`

to simulate a posterior Normal regression model of`body_mass_g`

by`flipper_length_mm`

and`species`

, without an interaction term. - Create and interpret both visual and numerical diagnostics of your MCMC simulation.
- Produce a
`tidy()`

summary of this model. Interpret the non-intercept coefficients’ posterior median values in context. - Simulate, plot, and describe the posterior predictive model for the body mass of an Adelie penguin that has a flipper length of 197.

**Exercise 11.11 (Penguins! Interaction)**Building from the previous exercise, our next goal is to model

`body_mass_g`

by `flipper_length_mm`

and `species`

*with*an interaction term between these two predictors.

- Use
`stan_glm()`

to simulate the posterior for this model, with four chains at 10000 iterations each. - Simulate and plot 50 posterior model lines. Briefly describe what you learn from this plot.
- Produce a
`tidy()`

summary for this model. Based on the summary, do you have evidence that the interaction terms are necessary for this model? Explain your reasoning.

**Exercise 11.12 (Penguins! 3 predictors)**Next, let’s explore a model of

`body_mass_g`

by **three**predictors:

`flipper_length_mm`

, `bill_length_mm`

, and `bill_depth_mm`

. Do not use any interactions in this model.
- Use
`stan_glm()`

to simulate the posterior for this model. - Use
`posterior_interval()`

to produce 95% credible intervals for the model parameters. - Based on these 95% credible intervals, when controlling for the other predictors in the model, which predictors have a significant positive association with body mass, which have significant negative association with body mass, and which have no significant association?

**Exercise 11.13 (Penguins! Comparing models)**Consider 4 separate models of

`body_mass_g`

:
model | formula |
---|---|

1 | `body_mass_g ~ flipper_length_mm` |

2 | `body_mass_g ~ species` |

3 | `body_mass_g ~ flipper_length_mm + species` |

4 | `body_mass_g ~ flipper_length_mm + bill_length_mm + bill_depth_mm` |

Simulate these four models using the

`stan_glm()`

function.Produce and compare the

`pp_check()`

plots for the four models.Use 10-fold cross-validation to assess and compare the posterior predictive quality of the four models using the

`prediction_summary_cv()`

. NOTE: We can only predict body mass for penguins that have complete information on our model predictors. Yet two penguins have NA values for multiple of these predictors. To remove these two penguins, we`select()`

our columns of interest*before*removing penguins with NA values. This way, we don’t throw out penguins just because they’re missing information on variables we don’t care about:`<- penguins_bayes %>% penguins_complete select(flipper_length_mm, body_mass_g, species, %>% bill_length_mm, bill_depth_mm) na.omit()`

Evaluate and compare the ELPD posterior predictive accuracy of the four models.

In summary, which of these four models is “best?” Explain.

### 11.7.3 Open-ended exercises

**Exercise 11.14 (Penguins on your own!)**Build three different models of penguin

`bill_length_mm`

. *You*get to choose which predictors to use, and whether to include interaction terms. Evaluate and compare these models. Which do you prefer and why?

**Exercise 11.15 (Weather on your own)**Use the

`weather_perth`

data in the **bayesrules**package to develop a “successful” model of afternoon temperatures (

`temp3pm`

) in Perth, Australia. *You*get to choose which predictors to use, and whether to include interaction terms. Be sure to evaluate your model and explain your modeling process.

### References

*Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery*. Use r! Springer. https://www.amazon.com/gp/product/1441998896/ref=as_li_qf_sp_asin_tl?ie=UTF8&tag=togaware-20&linkCode=as2&camp=217145&creative=399373&creativeASIN=1441998896.

This shortcut should be used with caution! First make sure that you actually want to use every variable in the

`weather`

data.↩︎1.

`temp9am`

; 2.`location`

,`humidity9am`

; 3.`windspeed9am`

,`pressure9am`

↩︎There’s some nuance here. Though

`weather_model_1`

has a lower`mae`

than`weather_model_2`

, it’s not best by*all*measures. However, the discrepancies aren’t big enough to make us change our minds here.↩︎Answer: 1 = model 3; 2 = model 1↩︎