blockcpd (version 1.0.0)

confidence_plot: Plot to check reported change-points

Description

Plots the estimates of how likely it is for the model to detect a change at any given point. True change-points should have confidence near $100%$, while non change-points should have a confidence near $0%$. It might also be difficult to detect a true change-point at the given sample size. In this case, it should fluctuate in the middle.

Usage

confidence_plot(
  model,
  scale = "percentage",
  index_values = NULL,
  index_variable_name = "Index",
  pkg = "base"
)

Value

No return value.

Arguments

model

A blockcpd model object.

scale

A string describing the scale which the y-scale should is plotted. Possible values are "percentage", "probability" and "frequency".

index_values

A numerical vector of size ncol that contains the values of the the variable corresponding to the change points.

index_variable_name

Name of the variable segmented.

pkg

Graphical package to be used for plotting. Current values are "base".

Examples

td = rcpd(nrow = 10, ncol = 10)
model = fit_blockcpd(td$data_matrix, bootstrap = TRUE)
confidence_plot(model)