Description Usage Arguments Value References
Compute the Distance in Measure between the clustering induced by a kernel density estimator (based on a sample x and a bandwidth h) and the population clustering defined by a K-component normal mixture density.
1 |
x |
(vector) the data to be partitioned. |
h |
the bandwidth to be used to estimate the density via KDE. |
mus |
vector of means of the mixture components. |
sigmas |
vector of standard deviations of the mixture components. |
props |
vector of mixing proportions of the mixture components. |
plot |
if true, the true density and the estimated one are displayed. |
the value of the Distance in Measure.
Chacón, J.E. (2015). A population background for nonparametric density-based clustering. Statistical Science 30(4): 518-532.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.