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. The method to calcualte BIC based on Gaussian mixture models estimated on a univariate clustering is described in (Song and Zhong 2020).
An object of class "Ckmeans.1d.dp
" defined in Ckmeans.1d.dp
.
Joe Song
Song M, Zhong H (2020). “Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers.” Bioinformatics. doi: 10.1093/bioinformatics/btaa613, [Published online ahead of print, 2020 Jul 3].
# 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)