plot.Cksegs.1d.dp: Plot Optimal Univariate Segmentation Results

In Ckmeans.1d.dp: Optimal, Fast, and Reproducible Univariate Clustering

Description

Plot optimal univariate segmentation results returned from Cksegs.1d.dp.

Usage

## S3 method for class 'Cksegs.1d.dp'
plot(x, xlab=NULL, ylab=NULL, main=NULL,
      sub=NULL, col.clusters=NULL, ...)

Arguments

x	an object of class as returned by Cksegs.1d.dp.
xlab	a character string. The x-axis label for the plot.
ylab	a character string. The x-axis label for the plot.
main	a character string. The title for the plot.
sub	a character string. The subtitle for the plot.
col.clusters	a vector of colors, defined either by integers or by color names. If the length is shorter than the number of clusters, the colors will be reused.
...	arguments passed to plot function in package graphics.

Details

The function plot.Cksegs.1d.dp shows segments as horizontal lines from the univariate segmentation results obtained from function Cksegs.1d.dp. It uses different colors to indicate segments.

Value

An object of class "Cksegs.1d.dp" defined in Cksegs.1d.dp.

Author(s)

Joe Song

References

Wang, H. and Song, M. (2011) Ckmeans.1d.dp: optimal k-means clustering in one dimension by dynamic programming. The R Journal 3(2), 29–33. Retrieved from https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Wang+Song.pdf

Examples

# Example: clustering data generated from a Gaussian
#          mixture model of three components
x <- c(rnorm(50, mean=-1, sd=0.3),
       rnorm(50, mean=1, sd=0.3),
       rnorm(50, mean=3, sd=0.3))

y <- x^3
res <- Cksegs.1d.dp(y, x=x)
plot(res, lwd=2)

Ckmeans.1d.dp documentation built on July 22, 2020, 5:09 p.m.