cNORM (version 3.3.1)

diagnostics.betabinomial: Diagnostic Information for Beta-Binomial Model

Description

This function provides diagnostic information for a fitted beta-binomial model from the cnorm.betabinomial function. It returns various metrics related to model convergence, fit, and complexity. In case, age and raw scores are provided, the function as well computes R2, rmse and bias for the norm scores based on the manifest and predicted norm scores.

Usage

diagnostics.betabinomial(model, age = NULL, score = NULL, weights = NULL)

Value

A list containing the following diagnostic information:

• converged: Logical indicating whether the optimization algorithm converged.

• n_evaluations: Number of function evaluations performed during optimization.

• n_gradient: Number of gradient evaluations performed during optimization.

• final_value: Final value of the objective function (negative log-likelihood).

• message: Any message returned by the optimization algorithm.

• AIC: Akaike Information Criterion.

• BIC: Bayesian Information Criterion.

• max_gradient: Maximum absolute gradient at the solution (if available).

Arguments

model

An object of class "cnormBetaBinomial", typically the result of a call to cnorm.betabinomial().

age

An optional vector with age values

score

An optional vector with raw values

weights

An optional vector with weights

Details

The AIC and BIC are calculated as: AIC = 2k - 2ln(L) BIC = ln(n)k - 2ln(L) where k is the number of parameters, L is the maximum likelihood, and n is the number of observations.

Examples

if (FALSE) {
# Fit a beta-binomial model
model <- cnorm.betabinomial(ppvt$age, ppvt$raw)

# Get diagnostic information
diag_info <- diagnostics.betabinomial(model)

# Print the diagnostic information
print(diag_info)

# Summary the diagnostic information
summary(diag_info)

# Check if the model converged
if(diag_info$converged) {
  cat("Model converged successfully.\n")
} else {
  cat("Warning: Model did not converge.\n")
}

# Compare AIC and BIC
cat("AIC:", diag_info$AIC, "\n")
cat("BIC:", diag_info$BIC, "\n")
}