##### https://github.com/cran/cutpointr

Tip revision:

**4408233eb8624dea85ecf18e86d50c296165c3f2**authored by**Christian Thiele**on**13 April 2022, 17:12:29 UTC****version 1.1.2** Tip revision:

**4408233**cutpointr_plotting.Rmd

```
---
title: "Plotting with cutpointr"
author: "Christian Thiele"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Plotting with cutpointr}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(fig.width = 6, fig.height = 5, fig.align = "center")
options(rmarkdown.html_vignette.check_title = FALSE)
load("vignettedata/vignettedata.Rdata")
```
**cutpointr** includes several convenience functions for plotting data from a
`cutpointr` object. These include:
- `plot_cutpointr`: General purpose plotting function for cutpointr or roc_cutpointr objects
- `plot_cut_boot`: Plot the bootstrapped distribution of optimal cutpoints
- `plot_metric`: If `maximize_metric` or `minimize_metric` was used this function
plots all possible cutoffs on the x-axis vs. the respective metric values on
the y-axis. If bootstrapping was run, a confidence interval based on the
bootstrapped distribution of metric values at each cutpoint can be displayed.
To display no confidence interval set `conf_lvl = 0`.
- `plot_metric_boot`: Plot the distribution of out-of-bag metric values
- `plot_precision_recall`: Plot the precision recall curve
- `plot_sensitivity_specificity`: Plot all cutpoints vs. sensitivity and specificity
- `plot_roc`: Plot the ROC curve
- `plot_x`: Plot the distribution of the predictor variable
```{r, fig.width=4, fig.height=3}
library(cutpointr)
set.seed(123)
opt_cut_b_g <- cutpointr(suicide, dsi, suicide, gender, boot_runs = 500)
plot_cut_boot(opt_cut_b_g)
plot_metric(opt_cut_b_g, conf_lvl = 0.9)
plot_metric_boot(opt_cut_b_g)
plot_precision_recall(opt_cut_b_g)
plot_sensitivity_specificity(opt_cut_b_g)
plot_roc(opt_cut_b_g)
```
All plot functions, except for the standard plot method that returns
a composed plot, return `ggplot` objects
than can be further modified. For example, changing labels, title, and the theme
can be achieved this way:
```{r, fig.width=4, fig.height=3}
library(ggplot2)
p <- plot_x(opt_cut_b_g)
p + ggtitle("Distribution of dsi") +
theme_minimal() +
xlab("Depression score")
```
## Flexible plotting function
Using `plot_cutpointr` any metric can be chosen to be plotted on the x- or
y-axis and results of `cutpointr()` as well as `roc()` can be plotted.
If a `cutpointr` object is to be plotted, it is thus irrelevant which `metric`
function was chosen for cutpoint estimation. Any metric that can be calculated
based on the ROC curve can be subsequently plotted as only the true / false
positives / negatives over all cutpoints are needed.
That way, not only the above plots can be produced, but also any
combination of two metrics (or metric functions) and / or cutpoints. The built-in
metric functions as well as user-defined functions or anonymous functions can
be supplied to `xvar` and `yvar`. If bootstrapping was run, confidence intervals
can be plotted around the y-variable. This is especially useful if the cutpoints,
available in the `cutpoints` function, are placed on the x-axis.
Note that confidence intervals can only be correctly plotted if the values of
`xvar` are constant across bootstrap samples. For example, confidence intervals
for TPR by FPR (a ROC curve) cannot be plotted easily, as the values of the false
positive rate vary per bootstrap sample.
```{r, fig.width=4, fig.height=3, cache=FALSE}
set.seed(1234)
opt_cut_b <- cutpointr(suicide, dsi, suicide, boot_runs = 500)
plot_cutpointr(opt_cut_b, xvar = cutpoints, yvar = sum_sens_spec, conf_lvl = 0.9)
plot_cutpointr(opt_cut_b, xvar = fpr, yvar = tpr, aspect_ratio = 1, conf_lvl = 0)
plot_cutpointr(opt_cut_b, xvar = cutpoint, yvar = tp, conf_lvl = 0.9) +
geom_point()
```
## Manual plotting
Since `cutpointr` returns a `data.frame` with the original data, bootstrap
results, and the ROC curve in nested tibbles, these data can be conveniently
extracted and plotted manually. The relevant
nested tibbles are in the columns `data`, `roc_curve` and `boot`. The following
is an example of accessing and plotting the grouped data.
```{r, fig.width=4, fig.height=3}
library(dplyr)
library(tidyr)
opt_cut_b_g |>
select(data, subgroup) |>
unnest(cols = data) |>
ggplot(aes(x = suicide, y = dsi)) +
geom_boxplot(alpha = 0.3) +
facet_grid(~subgroup)
```
```