# binned_residuals: Binned residuals for binomial logistic regression In performance: Assessment of Regression Models Performance

 binned_residuals R Documentation

## Binned residuals for binomial logistic regression

### Description

Check model quality of binomial logistic regression models.

### Usage

``````binned_residuals(
model,
term = NULL,
n_bins = NULL,
show_dots = NULL,
ci = 0.95,
ci_type = c("exact", "gaussian", "boot"),
residuals = c("deviance", "pearson", "response"),
iterations = 1000,
verbose = TRUE,
...
)
``````

### Arguments

 `model` A `glm`-object with binomial-family. `term` Name of independent variable from `x`. If not `NULL`, average residuals for the categories of `term` are plotted; else, average residuals for the estimated probabilities of the response are plotted. `n_bins` Numeric, the number of bins to divide the data. If `n_bins = NULL`, the square root of the number of observations is taken. `show_dots` Logical, if `TRUE`, will show data points in the plot. Set to `FALSE` for models with many observations, if generating the plot is too time-consuming. By default, `show_dots = NULL`. In this case `binned_residuals()` tries to guess whether performance will be poor due to a very large model and thus automatically shows or hides dots. `ci` Numeric, the confidence level for the error bounds. `ci_type` Character, the type of error bounds to calculate. Can be `"exact"` (default), `"gaussian"` or `"boot"`. `"exact"` calculates the error bounds based on the exact binomial distribution, using `binom.test()`. `"gaussian"` uses the Gaussian approximation, while `"boot"` uses a simple bootstrap method, where confidence intervals are calculated based on the quantiles of the bootstrap distribution. `residuals` Character, the type of residuals to calculate. Can be `"deviance"` (default), `"pearson"` or `"response"`. It is recommended to use `"response"` only for those models where other residuals are not available. `iterations` Integer, the number of iterations to use for the bootstrap method. Only used if `ci_type = "boot"`. `verbose` Toggle warnings and messages. `...` Currently not used.

### Details

Binned residual plots are achieved by "dividing the data into categories (bins) based on their fitted values, and then plotting the average residual versus the average fitted value for each bin." (Gelman, Hill 2007: 97). If the model were true, one would expect about 95% of the residuals to fall inside the error bounds.

If `term` is not `NULL`, one can compare the residuals in relation to a specific model predictor. This may be helpful to check if a term would fit better when transformed, e.g. a rising and falling pattern of residuals along the x-axis is a signal to consider taking the logarithm of the predictor (cf. Gelman and Hill 2007, pp. 97-98).

### Value

A data frame representing the data that is mapped in the accompanying plot. In case all residuals are inside the error bounds, points are black. If some of the residuals are outside the error bounds (indicated by the grey-shaded area), blue points indicate residuals that are OK, while red points indicate model under- or over-fitting for the relevant range of estimated probabilities.

### Note

`binned_residuals()` returns a data frame, however, the `print()` method only returns a short summary of the result. The data frame itself is used for plotting. The `plot()` method, in turn, creates a ggplot-object.

### References

Gelman, A., and Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.

### Examples

``````model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
result <- binned_residuals(model)
result

# look at the data frame
as.data.frame(result)

# plot
if (require("see")) {
plot(result, show_dots = TRUE)
}

``````

performance documentation built on Nov. 2, 2023, 5:48 p.m.