For these examples of logistic regression diagnostics, we’ll consider a simple bivariate setting where the model is misspecified:

```
logistic_pop <- population(
x1 = predictor("rnorm", mean = 0, sd = 10),
x2 = predictor("runif", min = 0, max = 10),
y = response(0.7 + 0.2 * x1 + x1^2 / 100 - 0.2 * x2,
family = binomial(link = "logit"))
)
logistic_data <- sample_x(logistic_pop, n = 100) |>
sample_y()
fit <- glm(y ~ x1 + x2, data = logistic_data, family = binomial)
```

In other words, the population relationship is \[ \begin{align*} Y \mid X = x &\sim \text{Bernoulli}(\mu(x)) \\ \mu(x) &= \operatorname{logit}^{-1}\left(0.7 + 0.2 x_1 + \frac{x_1^2}{100} - 0.2 x_2\right), \end{align*} \] but we chose to fit a model that does not allow a quadratic term for \(x_1\).

Before fitting the model, we might conduct exploratory data analysis to determine what model is appropriate. In linear regression, scatterplots of the predictors versus the response variable would be helpful, but with a binary outcome these are much harder to interpret.

Instead, an empirical logit plot can help us visualize the
relationship between predictor and response. We break the range of
`x1`

into bins, and within each bin, calculate the mean value
of `x1`

and `y`

for observations in that bin. We
then transform the mean of `y`

through the link function; in
logistic regression, this is the logit, so we transform from a fraction
to the log-odds. If the logistic model is well-specified,
`x1`

and the logit of `y`

should be linearly
related. The logits of 0 and 1 are \(-\infty\) and \(+\infty\), so taking averages of
`y`

within bins ensures the logits are on a more reasonable
range.

The `bin_by_quantile()`

function groups the data into
bins, while `empirical_link()`

can calculate the empirical
value of a variable on the link scale, for any GLM family:

```
logistic_data |>
bin_by_quantile(x1, breaks = 6) |>
summarize(x = mean(x1),
response = empirical_link(y, binomial)) |>
ggplot(aes(x = x, y = response)) +
geom_point() +
labs(x = "X1", y = "logit(Y)")
```

This looks suspiciously nonlinear.

Similarly for `x2`

:

```
logistic_data |>
bin_by_quantile(x2, breaks = 6) |>
summarize(x = mean(x2),
response = empirical_link(y, binomial)) |>
ggplot(aes(x = x, y = response)) +
geom_point() +
labs(x = "X2", y = "logit(Y)")
```

This looks more linear, though it is difficult to assess. We could
also use `model_lineup()`

to examine similar plots when the
model is correctly specified, to tell if these plots indicate a serious
problem.

Once we have fit the model, ordinary standardized residuals are not very helpful for noticing the misspecification:

```
augment(fit) |>
ggplot(aes(x = .fitted, y = .std.resid)) +
geom_point() +
geom_smooth(se = FALSE) +
labs(x = "Fitted value", y = "Residual")
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
```

Nor are plots of standardized residuals against the predictors:

```
augment_longer(fit) |>
ggplot(aes(x = .predictor_value, y = .std.resid)) +
geom_point() +
geom_smooth(se = FALSE) +
facet_wrap(vars(.predictor_name), scales = "free_x") +
labs(x = "Predictor", y = "Residual")
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
```

We see a hint of something in the smoothed line on the left, but it is hard to judge what that means. Because our outcome is binary, the residuals are divided into two clumps (\(Y = 0\) and \(Y = 1\)), making their distribution hard to interpret and trends hard to spot.

For each predictor, we plot the predictor versus \(Y\). We plot the smoothed curve of fitted values (red) as well as a smoothed curve of response values (blue):

```
augment_longer(fit, type.predict = "response") |>
ggplot(aes(x = .predictor_value)) +
geom_point(aes(y = y)) +
geom_smooth(aes(y = .fitted), color = "red") +
geom_smooth(aes(y = y)) +
facet_wrap(vars(.predictor_name), scales = "free_x") +
labs(x = "Predictor", y = "Y")
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
```

The red line is a smoothed version of \(\hat \mu(x)\) versus \(X_1\), while the blue line averages \(Y\) (which is 0 or 1, so the average is the true fraction of 1s) versus \(X_1\). Comparing the two lines helps us evaluate if the model is well-specified.

This again suggests something may be going on with `x1`

,
but it’s hard to tell what specifically might be wrong.

The partial residuals make the quadratic shape of the relationship much clearer:

```
partial_residuals(fit) |>
ggplot(aes(x = .predictor_value, y = .partial_resid)) +
geom_point() +
geom_smooth() +
geom_line(aes(x = .predictor_value, y = .predictor_effect)) +
facet_wrap(vars(.predictor_name), scales = "free") +
labs(x = "Predictor", y = "Partial residual")
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
```

See the `partial_residuals()`

documentation for more
information how these are computed and interpreted.

Binned residuals bin the observations based on their predictor values, and average the residual value in each bin. This avoids the problem that individual residuals are hard to interpret because \(Y\) is only 0 or 1:

```
binned_residuals(fit) |>
ggplot(aes(x = predictor_mean, y = resid_mean)) +
facet_wrap(vars(predictor_name), scales = "free") +
geom_point() +
labs(x = "Predictor", y = "Residual mean")
```

This is comparable to the marginal model plots above: where the marginal model plots show a smoothed curve of fitted values and a smoothed curve of actual values, the binned residuals show the average residuals, which are actual values minus fitted values. We can think of the binned residual plot as showing the difference between the lines in the marginal model plot.

We can also bin by the fitted values of the model: