prune: Prune a Variable Length Markov Chain (VLMC)

Description

This function prunes a VLMC.

Usage

prune(vlmc, alpha = 0.05, cutoff = NULL, ...)
# S3 method for vlmc
prune(vlmc, alpha = 0.05, cutoff = NULL, ...)
# S3 method for vlmc_cpp
prune(vlmc, alpha = 0.05, cutoff = NULL, ...)

Value

a pruned VLMC

Arguments

vlmc: a fitted VLMC model.
alpha: number in (0,1] (default: 0.05) cut off value in quantile scale for pruning.
cutoff: positive number: cut off value in native (log likelihood ratio) scale for pruning. Defaults to the value obtained from alpha. Takes precedence over alpha if specified.
...: additional arguments for the prune function.

Details

In general, pruning a VLMC is more efficient than constructing two VLMC (the base one and pruned one). Up to numerical instabilities, building a VLMC with a a cut off and then pruning it with a b cut off (with a>b) should produce the same VLMC than building directly the VLMC with a b cut off. Interesting cut off values can be extracted from a VLMC using the cutoff() function.

As automated model selection is provided by tune_vlmc(), the direct use of cutoff should be reserved to advanced exploration of the set of trees that can be obtained from a complex one, e.g. to implement model selection techniques that are not provided by tune_vlmc().

Examples

Run this code

pc <- powerconsumption[powerconsumption$week == 5, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.25, 0.5, 0.75, 1))))
base_model <- vlmc(dts, alpha = 0.1)
model_cuts <- cutoff(base_model)
pruned_model <- prune(base_model, model_cuts[3])
draw(pruned_model)
direct_simple <- vlmc(dts, alpha = model_cuts[3])
draw(direct_simple)
# pruned_model and direct_simple should be identical
all.equal(pruned_model, direct_simple)