Predict Method for Hurdle Demand Models

Returns predictions from a fitted hurdle demand model.

Usage

# S3 method for class 'beezdemand_hurdle'
predict(
  object,
  newdata = NULL,
  type = c("response", "link", "parameters", "probability", "demand"),
  prices = NULL,
  se.fit = FALSE,
  interval = c("none", "confidence"),
  level = 0.95,
  ...
)

Arguments

object

An object of class beezdemand_hurdle.

newdata

Optional data frame containing a price column matching the fitted object's x_var. If newdata includes the id column, subject-specific predictions are returned; otherwise population predictions are returned. If newdata is NULL, returns predictions for all subjects across a price grid.

type

One of:

"response"

Predicted consumption (part II)

"link"

Predicted log-consumption (linear predictor of part II)

"probability"

Predicted probability of zero consumption (part I)

"demand"

Predicted expected consumption = (1 - P0) * response

"parameters"

Subject-specific parameters (no .fitted column)

prices

Optional numeric vector of prices used only when newdata = NULL.

se.fit

Logical; if TRUE, includes a .se.fit column (delta-method via sdreport when available).

interval

One of "none" (default) or "confidence".

level

Confidence level when interval = "confidence".

...

Unused.

Value

For type = "parameters", a tibble of subject-level parameters. Otherwise, a tibble containing the newdata columns plus .fitted and helper columns predicted_log_consumption, predicted_consumption, prob_zero, and expected_consumption. When requested, .se.fit and .lower/.upper are included.

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

# Get subject-specific parameters
pars <- predict(fit, type = "parameters")

# Predict demand at specific prices
demand <- predict(fit, type = "demand", prices = c(0, 0.5, 1, 2, 5))
# }