plot.cp_model_lmer: Plot Method for Mixed-Effects Cross-Price Demand Models

Description

Creates a ggplot2 visualization of a fitted mixed-effects cross-price demand model (of class cp_model_lmer). This function allows you to plot:

Usage

# S3 method for cp_model_lmer
plot(
  x,
  data = NULL,
  inv_fun = identity,
  n_points = 100,
  title = NULL,
  xlab = "Price",
  ylab = "Consumption",
  x_trans = "identity",
  y_trans = "identity",
  point_size = 3,
  pred_type = c("fixed", "random", "all"),
  ...
)

Value

A ggplot2 object displaying the observed data points along with the prediction curves.

Arguments

x

A cp_model_lmer object (as returned by fit_cp_linear(type = "mixed", ...)).

data

Optional data frame containing columns x and y to be plotted. If not provided, object$data is used.

inv_fun

Optional function to inverse-transform predictions. Default is identity. Not typically used for linear models but included for API consistency.

n_points

Number of points to use in creating the prediction grid. Default is 100.

title

Optional title for the plot; default is NULL.

xlab

Label for the x-axis. Default is "Price".

ylab

Label for the y-axis. Default is "Consumption".

x_trans

Transformation for the x-axis; one of "identity", "log10", or "pseudo_log". Default is "identity".

y_trans

Transformation for the y-axis; one of "identity", "log10", or "pseudo_log". Default is "identity".

point_size

Size of the observed data points. Default is 3.

pred_type

Character string specifying which prediction components to plot:

"fixed": Plot only the fixed-effects (population) prediction.

"random"

Plot only the subject-specific predictions.

"all"

Plot both the fixed-effects and the subject-specific predictions.

The default is "fixed".

...

Additional arguments passed to predict.cp_model_lmer.

Details

"fixed": Only the population-level (fixed-effects) prediction.
"random": Only the subject-specific predictions.
"all": Both: the fixed-effects and the subject-specific predictions.

If the model includes group effects, separate lines will be drawn for each group.