plotBIC {Ckmeans.1d.dp} | R Documentation |
Plot Bayesian information criterion (BIC) as a function of the number of clusters obtained from optimal univariate clustering results returned from Ckmeans.1d.dp
. The BIC normalized by sample size (BIC/n) is shown.
plotBIC( ck, xlab="Number of clusters k", ylab = "BIC/n", type="b", sub=paste("n =", length(ck$cluster)), main=paste("Bayesian information criterion", "(normalized by sample size)", sep="\n"), ... )
ck |
an object of class |
xlab |
a character string. The x-axis label for the plot. |
ylab |
a character string. The x-axis label for the plot. |
type |
the type of plot to be drawn. See |
main |
a character string. The title for the plot. |
sub |
a character string. The subtitle for the plot. |
... |
arguments passed to |
The function visualizes the input data as sticks whose heights are the weights. It uses different colors to indicate optimal k-means clusters.
An object of class "Ckmeans.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 two components x <- rnorm(50, mean=-1, sd=0.3) x <- append(x, rnorm(50, mean=1, sd=0.3) ) res <- Ckmeans.1d.dp(x) plotBIC(res) y <- (rnorm(length(x)))^2 res <- Ckmeans.1d.dp(x, y=y) plotBIC(res)