autoplot.tune_vlmc: Create a complete ggplot for the results of automatic VLMC...

In mixvlmc: Variable Length Markov Chains with Covariates

Create a complete ggplot for the results of automatic VLMC complexity selection

Description

This function prepares a plot of the results of tune_vlmc() using ggplot2. The result can be passed to print() to display the result.

Usage

## S3 method for class 'tune_vlmc'
autoplot(object, cutoff = c("quantile", "native"), ...)

Arguments

object	a tune_vlmc object
cutoff	the scale used for the cut off criterion (default "quantile")
...	additional parameters (not used currently)

Details

The graphical representation proposed by this function is complete, while the one produced by plot.tune_vlmc() is minimalistic. We use here the faceting capabilities of ggplot2 to combine on a single graphical representation the evolution of multiple characteristics of the VLMC during the pruning process, while plot.tune_vlmc() shows only the selection criterion or the log likelihood. Each facet of the resulting plot shows a quantity as a function of the cut off expressed in quantile or native scale.

Value

a ggplot object

Examples

pc <- powerconsumption[powerconsumption$week %in% 10:11, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.5, 1))))
dts_best_model_tune <- tune_vlmc(dts, criterion = "BIC")
vlmc_plot <- ggplot2::autoplot(dts_best_model_tune)
print(vlmc_plot)
## simple post customisation
print(vlmc_plot + ggplot2::geom_point())

mixvlmc documentation built on June 8, 2025, 12:35 p.m.