autoplot.tune_covlmc: Create a complete ggplot for the results of automatic COVLMC...

In mixvlmc: Variable Length Markov Chains with Covariates

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

Description

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

Usage

## S3 method for class 'tune_covlmc'
autoplot(object, ...)

Arguments

object	a tune_covlmc object
...	additional parameters (not used currently)

Details

The graphical representation proposed by this function is complete, while the one produced by plot.tune_covlmc() 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_covlmc() 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$week %in% 10:12, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.5, 1))))
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
dts_best_model_tune <- tune_covlmc(dts, dts_cov, criterion = "AIC")
covlmc_plot <- ggplot2::autoplot(dts_best_model_tune)
print(covlmc_plot)

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