Compare Parameter Estimates Across Models

Creates a forest plot (coefficient plot) comparing parameter estimates and confidence intervals across multiple fitted demand models.

Usage

plot_model_comparison(
  ...,
  model_list = NULL,
  labels = NULL,
  parameters = c("Q0", "alpha"),
  conf_level = 0.95,
  style = c("modern", "apa")
)

Arguments

...

Fitted model objects.

model_list

Optional named list of models.

labels

Character vector of model labels.

parameters

Character vector of parameter names to compare (default c("Q0", "alpha")).

conf_level

Numeric; confidence level for intervals (default 0.95).

style

Character; "modern" or "apa".

Value

A ggplot2 object.

Details

Uses tidy() methods to extract parameter estimates and standard errors. Confidence intervals are computed as estimate +/- z * SE. Parameters are matched by the term column from tidy() output.

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
plot_model_comparison(fit, labels = "Hurdle")

# }