Plot Method for AddiVortesFit

Generates comprehensive diagnostic plots for a fitted AddiVortesFit object. This function creates multiple diagnostic plots including residuals, MCMC traces for sigma, and tessellation complexity over iterations.

Usage

# S3 method for class 'AddiVortesFit'
plot(
  x,
  x_train,
  y_train,
  sigma_trace = NULL,
  which = c(1, 2, 3),
  ask = FALSE,
  ...
)

Arguments

x

An object of class AddiVortesFit, typically the result of a call to AddiVortes().

x_train

A matrix of the original training covariates.

y_train

A numeric vector of the original training true outcomes.

sigma_trace

An optional numeric vector of sigma values from MCMC samples. If not provided, the method will attempt to extract it from the model object.

which

A numeric vector specifying which plots to generate: 1 = Residuals plot, 2 = Sigma trace, 3 = Tessellation complexity trace, 4 = Predicted vs Observed. Default is c(1, 2, 3).

ask

Logical; if TRUE, the user is asked to press Enter before each plot.

...

Additional arguments passed to plotting functions.

Value

This function is called for its side effect of creating plots and returns NULL invisibly.

Details

The function generates up to four diagnostic plots:

1. Residuals Plot: Residuals vs fitted values with smoothed trend line

2. Sigma Trace: MCMC trace plot for the error variance parameter

3. Tessellation Complexity: Trace of average tessellation size over iterations

4. Predicted vs Observed: Scatter plot with confidence intervals

Examples

if (FALSE) { # \dontrun{
# Assuming 'fit' is a trained AddiVortesFit object
plot(fit, x_train = x_train_data, y_train = y_train_data)

# Show only specific plots
plot(fit, x_train = x_train_data, y_train = y_train_data, which = c(1, 3))

# With custom sigma trace
plot(fit, x_train = x_train_data, y_train = y_train_data, 
     sigma_trace = my_sigma_samples)
} # }