AddiVortes (version 0.4.8)

plot.AddiVortesFit: Plot Method for AddiVortesFit

Description

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 AddiVortesFit
plot(
  x,
  x_train,
  y_train,
  sigma_trace = NULL,
  which = c(1, 2, 3),
  ask = FALSE,
  ...
)

Value

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

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.

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 credible intervals

Examples

if (FALSE) {
# 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)
}