beezdemand (version 0.2.0)

predict.beezdemand_nlme: Predict Method for beezdemand_nlme Objects

Description

Generates point predictions from a fitted beezdemand_nlme model. Predictions can be made at the population level (fixed effects only) or group/subject level (fixed + random effects). The output scale depends on the equation_form used during model fitting and whether inv_fun is applied.

Usage

# S3 method for beezdemand_nlme
predict(
  object,
  newdata = NULL,
  type = c("response", "link", "population", "individual"),
  level = 0,
  inv_fun = identity,
  se.fit = FALSE,
  interval = c("none", "confidence"),
  interval_level = 0.95,
  ...
)

Value

A tibble containing the original newdata columns plus .fitted. When requested, .se.fit and .lower/.upper are included (currently NA).

Arguments

object

A beezdemand_nlme object.

newdata

Optional data frame for which to make predictions. Must contain x_var and all factors specified in the original model. If group-level predictions are desired (level=1), the id_var column from the original fit must also be present in newdata and its levels should correspond to those in the original data for meaningful random effect application. If NULL, predictions are made for the data used in fitting the model.

type

One of "response" (default), "link", "population", or "individual". "population" and "individual" are aliases that set level to 0 or 1, respectively.

level

Integer, prediction level for nlme::predict.nlme():

• 0: Population predictions (based on fixed effects only).

• 1 (or higher, up to number of grouping levels in model): Group-specific predictions (fixed effects + random effects for the specified id_var level).

Default is 0.

inv_fun

Optional function to inverse-transform the predictions. Example: If y_var was log10-transformed during fitting and equation_form like "zben" produces predictions on that log10 scale, inv_fun = function(x) 10^x would convert predictions back to the original consumption scale. If equation_form was "simplified" (which models raw Y), inv_fun might be identity or not needed if predictions are already on the desired scale.

se.fit

Logical; if TRUE, includes a .se.fit column (currently NA because standard errors are not implemented for beezdemand_nlme predictions).

interval

One of "none" (default) or "confidence". When requested, .lower/.upper are returned as NA.

interval_level

Confidence level when interval = "confidence". Currently used only for validation.

...

Additional arguments passed to nlme::predict.nlme().

See Also

predict.nlme

Examples

# \donttest{
data(ko)
fit <- fit_demand_mixed(ko, y_var = "y_ll4", x_var = "x",
                        id_var = "monkey", equation_form = "zben")
# Population-level predictions
preds <- predict(fit, level = 0)

# Subject-level predictions
preds_subj <- predict(fit, level = 1)
# }