dlso {salso} | R Documentation |
Latent Structure Optimization Based on Draws
Description
This function provides a partition to summarize a partition distribution
using the draws-based latent structure optimization (DLSO) method, which is
also known as the least-squares clustering method (Dahl 2006). The method
seeks to minimize an estimation criterion by picking the minimizer among the
partitions supplied. The implementation currently supports the minimization
of several partition estimation criteria. For details on these criteria, see
partition.loss
.
Usage
dlso(truth, loss = VI(), estimate = NULL)
Arguments
truth |
An integer vector of cluster labels for |
loss |
The loss function to use, as indicated by |
estimate |
An integer vector of cluster labels having the same length as
|
Value
An integer vector giving the estimated partition, encoded using cluster labels.
References
D. B. Dahl (2006), Model-Based Clustering for Expression Data via a Dirichlet Process Mixture Model, in Bayesian Inference for Gene Expression and Proteomics, Kim-Anh Do, Peter Müller, Marina Vannucci (Eds.), Cambridge University Press.
See Also
partition.loss
, psm
,
summary.salso.estimate
, salso
Examples
data(iris.clusterings)
# R_CARGO \dontrun{
# R_CARGO # Example disabled since Cargo was not found when installing from source package.
# R_CARGO # You can still run the example if you install Cargo. Hint: cargo::install().
dlso(iris.clusterings, loss=VI())
dlso(iris.clusterings, loss=binder())
# Compute expected loss using all draws, but pick the best among the first 10.
dlso(iris.clusterings, loss=VI(), estimate=iris.clusterings[1:10,])
# R_CARGO }