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

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 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)

Value

a ggplot object

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.

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())