Predict from a Mixed-Effects Cross-Price Demand Model — predict.cp_model

Generates predictions from a mixed-effects cross-price demand model (of class cp_model_lmer). The function supports two modes:

Usage

# S3 method for class 'cp_model_lmer'
predict(object, newdata = NULL, pred_type = c("fixed", "random"), ...)

Arguments

object: A cp_model_lmer object (as returned by fit_cp_linear(type = "mixed", ...)).
newdata: A data frame containing at least an x column. For pred_type = "random", an id column is required. If absent, the function extracts unique ids from object$data and expands the grid accordingly. If no ids are available, a default id of 1 is used (with a warning).
pred_type: Character string specifying the type of prediction: either "fixed" (population-level) or "random" (subject-specific). The default is "fixed".
...: Additional arguments passed to the underlying predict function.

Value

A data frame containing all columns of newdata plus a column y_pred with the corresponding predictions.

Details

"fixed": Returns predictions based solely on the fixed-effects component (using re.form = NA).
"random": Returns subject-specific predictions (fixed plus random effects) (using re.form = NULL).

Examples

# \donttest{
data(etm)
fit <- fit_cp_linear(etm, type = "mixed")
new_prices <- data.frame(x = c(2, 4, 8, 16, 32, 64))
predict(fit, newdata = new_prices, pred_type = "fixed")
#>    x   y_pred
#> 1  2 2.951724
#> 2  4 3.065139
#> 3  8 3.291969
#> 4 16 3.745629
#> 5 32 4.652950
#> 6 64 6.467591
# }