plot.Ckmeans.1d.dp {Ckmeans.1d.dp} | R Documentation |
Plot optimal univariate clustering results returned from Ckmeans.1d.dp
.
## S3 method for class 'Ckmeans.1d.dp' plot(x, xlab=NULL, ylab=NULL, main=NULL, sub=NULL, col.clusters=NULL, ...) ## S3 method for class 'Ckmedian.1d.dp' plot(x, xlab=NULL, ylab=NULL, main=NULL, sub=NULL, col.clusters=NULL, ...)
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 |
The functions plot.Ckmeans.1d.dp
and plot.Ckmedian.1d.dp
visualize the input data as sticks whose heights are the weights. They use different colors to indicate clusters.
An object of class "Ckmeans.1d.dp
" or "Ckmedian.1d.dp
" defined in Ckmeans.1d.dp
.
Joe Song
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
# 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)) res <- Ckmeans.1d.dp(x) plot(res) y <- (rnorm(length(x)))^2 res <- Ckmeans.1d.dp(x, y=y) plot(res) res <- Ckmedian.1d.dp(x) plot(res)