Fits AD models of increasing orders and selects the best by BIC.
Usage
bic_order_cat(
y,
max_order = 2,
blocks = NULL,
homogeneous = TRUE,
n_categories = NULL,
criterion = "bic"
)Arguments
- y
Integer matrix of categorical data (n_subjects x n_time).
- max_order
Maximum order to consider. Default is 2.
- blocks
Optional block membership vector.
- homogeneous
Whether to use homogeneous parameters across blocks.
- n_categories
Number of categories (inferred if NULL).
- criterion
Which criterion to use: "bic" (default) or "aic".
Value
A list containing:
- table
Data frame with order, log_l, n_params, aic, bic
- bic
Named numeric vector of BIC values by order
- best_order
Order with lowest criterion value
- criterion
Criterion used for order selection (
"bic"or"aic")- fits
List of fitted models
Examples
if (FALSE) { # \dontrun{
y <- simulate_cat(100, 5, order = 1, n_categories = 2)
result <- bic_order_cat(y, max_order = 2)
print(result$table)
print(result$best_order)
} # }