plot.Cksegs.1d.dp {Ckmeans.1d.dp} | R Documentation |

## Plot Optimal Univariate Segmentation Results

### 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 |

`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 |

### 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*version 4.3.5 Index]