changepoint.influence (version 1.0.2)

ParameterStability: Parameter Stability Graphic

Description

Plots the middle detail level of the changepoint parameter stability according to the influence measure.

Usage

ParameterStability(influence,original.mean=NULL,digits=6,ylab='',xlab='Index',
  cpt.col='red',cpt.width=3,...)

Value

The function returns a plot (silently).

Arguments

influence

The influence as calculated the influence() function provided within this package. This is a list object.

original.mean

A vector, length n, of the mean under the original segmentation at each timepoint.

digits

The number of significant figures to round the mean values to before plotting. (Purely to reduce the number of points plotted to make the graphics smaller for storage and loading)

ylab, xlab

The labels for the x- and y-axis, character vector expected.

cpt.col

Colour of the original parameter vector when plotted. Any values accepted by the col plotting argument are allowed.

cpt.width

Width of the original parameter vector when plotted. Any values accepted by the lwd plotting argument are allowed.

...

Any other arguments to be passed to the plot() function.

Author

Rebecca Killick

Details

This function creates a more granular graphic to display the results of a changepoint influence analysis on the estimated segment parameter. The graphic depicts the observed segment parameters under the "delete" or "outlier" Influence analysis. The intensity of the grey denotes how often that parameter values was seen across all segmentations. We overlay this with the original segment parameters.

References

Wilms I, Killick R, Matteson DS (2022) Graphical Influence Diagnostics for Changepoint Models, Journal of Computational and Graphical Statistics, 31:3, 753--765 DOI: 10.1080/10618600.2021.2000873

See Also

influence-methods, StabilityOverview,LocationStability,InfluenceMap

Examples

#### Generate Simulated data example ####
set.seed(30)
x=c(rnorm(50),rnorm(50,mean=5),rnorm(1,mean=15),rnorm(49,mean=5),rnorm(50,mean=4))
xcpt = cpt.mean(x,method='PELT') # Get the changepoints via PELT

#### Get the influence for both delete and outlier options ####
x.inf = influence(xcpt)


#### Parameter Stability plot ####
ParameterStability(x.inf, original.mean = rep(param.est(xcpt)$mean, 
  times=diff(c(0,xcpt@cpts))), las = 1)
# note that the original mean is an n length vector and you can use the above code 
# to get this from the original changepoint locations.