cureplots

Cumulative residual (CURE) plots assess the goodness-of-fit of a covariate in a generalized linear regression model, usually a negative binomial regression or a Poisson regression. The package cureplots produces CURE plots for the requested variables produced with ggplot2 or a table to easily produce a customized plot with the desired package.

Installation

Install the latest CRAN version with

install.packages("cureplots")

You can install the development version of cureplots from GitHub with the following:

# install.packages("devtools")
devtools::install_github("gbasulto/cureplots")

Functions and Data

Name	Purpose
calculate_cure_dataframe	Calculate CURE dataframe. Useful to produce customized CURE plots or CURE plots with variables not included in the model or transformations of variables that were included in the model (e.g., CURE plot for AADT when log(AADT) was included in the model).
cure_plot	Produce default CURE plot by either providing model and variable to plot or an output from calculate_cure_dataframe function.
resample_residuals	Resample cumulative residuals to overlay to CURE plots and better interpret results.
washington_roads	Curated dataframe of crashes in Washington roads.

Functions in cureplots

Basic Example

The example below shows

• How to produce a cure plot directly from the model object and
• How to produce the table to customize a plot.

A Poisson GLM model is adjusted to simulated data using the package glm. The functions also work with the gam package.

library(cureplots)

## basic example

set.seed(2000)

## Define parameters
beta <- c(-1, 0.3, 3)

## Simulate idependent variables
n <- 900
AADT <- c(runif(n, min = 2000, max = 150000))
nlanes <- sample(x = c(2, 3, 4), size = n, replace = TRUE)
LNAADT <- log(AADT)

## Simulate dependent variable
theta <- exp(beta[1] + beta[2] * LNAADT + beta[3] * nlanes)
y <- rpois(n, theta)

## Fit model
mod <- glm(y ~ LNAADT + nlanes, family = poisson)

## Calculate residuals
res <- residuals(mod, type = "response")

## Calculate CURE plot data
cure_df <- calculate_cure_dataframe(AADT, res)
#> Covariate: AADT

head(cure_df)
#> # A tibble: 6 × 5
#>    AADT residual  cumres  lower upper
#>   <dbl>    <dbl>   <dbl>  <dbl> <dbl>
#> 1 2363.   -233.   -233.   -457.  457.
#> 2 2435.     17.2  -216.   -459.  459.
#> 3 2724.    246.     29.9  -666.  666.
#> 4 2978.  -1539.  -1509.  -3081. 3081.
#> 5 3007.    -19.5 -1528.  -3081. 3081.
#> 6 3149.   -338.  -1867.  -3151. 3151.

## Providing CURE data frame
cure_plot(cure_df)
#> CURE data frame was provided. Its first column, AADT, will be used.

## Providing glm object
cure_plot(mod, "LNAADT")
#> Covariate LNAADT will be used to produce CURE plot.

Example with Resampling

library(cureplots)

## Basic example

set.seed(2000)

## Define parameters.
beta <- c(-1, 0.3, 3)

## Simulate idependent variables
n <- 900
AADT <- c(runif(n, min = 2000, max = 150000))
nlanes <- sample(x = c(2, 3, 4), size = n, replace = TRUE)
LNAADT <- log(AADT)

## Simulate dependent variable
theta <- exp(beta[1] + beta[2] * LNAADT + beta[3] * nlanes)
y <- rpois(n, theta)

## Fit model
mod <- glm(y ~ LNAADT + nlanes, family = poisson)

## Calculate residuals
res <- residuals(mod, type = "response")

## Calculate CURE plot data
cure_df <- calculate_cure_dataframe(AADT, res)
#> Covariate: AADT



cure_plot(cure_df, n_resamples = 3)
#> CURE data frame was provided. Its first column, AADT, will be used.