Cumulative Residuals (CURE) Plot for Count Models

This function generates a Cumulative Residuals (CURE) plot for count models, including those with random parameters, estimated using the flexCountReg package.

Usage

cureplot(
  model,
  data = NULL,
  indvar = NULL,
  method = "Simulated",
  n_resamples = 0,
  ...
)

Arguments

model

A model object estimated using this R package.

data

Optional dataframe. If not provided, the data used to fit the model will be used.

indvar

Optional independent variable name (character string). This is the continuous independent variable to plot the cumulative residuals against. If not provided, the plot will be against the predicted values.

method

Optional parameter to pass to the predict function. This is only used for random parameters models (e.g., "Simulated" or "Individual"). For further details, see predict.flexCountReg.

n_resamples

Number of resamples for potential resampling in the CURE plot confidence bands. Default is 0 (no bands).

...

Additional arguments passed to cure_plot.

Value

A CURE plot generated with cureplots.

Examples

# \donttest{
## Example using a Negative Binomial model
data("washington_roads")
washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)

nb_model <- countreg(Total_crashes ~ lnaadt + lnlength + speed50 + 
                            ShouldWidth04 + AADTover10k,
                            data = washington_roads, family = 'nb2', 
                            method = 'NM', max.iters = 500)
                            
# 1. Plot against fitted values (default) with confidence bands
cureplot(nb_model, n_resamples = 20)
#> Covariate: indvar_values
#> CURE data frame was provided. Its first column, Predicted Values, will be used.


# 2. Plot against a specific covariate (e.g., lnlength)
cureplot(nb_model, indvar = "lnlength", n_resamples = 20)
#> Covariate: indvar_values
#> CURE data frame was provided. Its first column, lnlength, will be used.

# }