Skip to contents

Augment a beezdemand_hurdle Model with Fitted Values and Residuals

Source: R/hurdle-methods.R
augment.beezdemand_hurdle.Rd

Returns the original data with fitted values, residuals, and predictions from a hurdle demand model. This enables easy model diagnostics and visualization with the tidyverse.

Usage

# S3 method for class 'beezdemand_hurdle'
augment(x, newdata = NULL, ...)

Arguments

x

An object of class beezdemand_hurdle.

newdata

Optional data frame of new data for prediction. If NULL, uses the original data from the model.

...

Additional arguments (currently unused).

Value

A tibble containing the original data plus:

.fitted

Fitted demand values (natural scale)

.fitted_link

Fitted values on log scale (Part II mean)

.fitted_prob

Predicted probability of consumption (1 - P(zero))

.resid

Residuals on log scale for positive observations, NA for zeros

.resid_response

Residuals on response scale (y - .fitted)

Details

For two-part hurdle models:

  • .fitted gives predicted demand on the natural consumption scale

  • .fitted_prob gives the predicted probability of positive consumption

  • .resid is defined only for positive observations as log(y) - .fitted_link

  • Observations with zero consumption have .resid = NA since they are explained by Part I (the zero-probability component), not Part II

Examples

# \donttest{
data(apt)
fit <- fit_demand_hurdle(apt, y_var = "y", x_var = "x", id_var = "id")
#> Sample size may be too small for reliable estimation.
#>   Subjects: 10, Parameters: 12, Recommended minimum: 60 subjects.
#>   Consider using more subjects or the simpler 2-RE model.
#> Fitting HurdleDemand3RE model...
#>   Part II: zhao_exponential
#>   Subjects: 10, Observations: 160
#>   Fixed parameters: 12, Random effects per subject: 3
#>   Optimizing...
#>   Converged in 81 iterations
#>   Computing standard errors...
#> Done. Log-likelihood: 32.81
augmented <- augment(fit)

# Plot residuals
library(ggplot2)
ggplot(augmented, aes(x = .fitted, y = .resid)) +
  geom_point(alpha = 0.5) +
  geom_hline(yintercept = 0, linetype = "dashed")
#> Warning: Removed 14 rows containing missing values or values outside the scale range
#> (`geom_point()`).

# }