rEMM (version 1.0-11)

recluster: Reclustering EMM states

Description

Use various clustering methods to recluster states/clusters in an EMM. The centers of the clusters in the EMM object are used as data points by the reclustering algorithm. States/centers put by reclustering into the same cluster are merged to produce a new reclustered EMM.

Usage

# S4 method for EMM
recluster_hclust(x, k=NULL, h=NULL, method="average", 
    ...,prune=NULL, copy=TRUE)
# S4 method for EMM
recluster_kmeans(x, k, ..., prune=NULL, copy=TRUE)
# S4 method for EMM
recluster_pam(x, k, ..., prune=NULL, copy=TRUE)
# S4 method for EMM
recluster_reachability(x, h, ..., prune=NULL, copy=TRUE)
# S4 method for EMM
recluster_tNN(x, threshold=NULL, ..., prune=NULL, copy=TRUE)
# S4 method for EMM
recluster_transitions(x, threshold=NULL, ..., prune=NULL, copy=TRUE)

Arguments

x

an "EMM" object.

k

number of clusters.

h

heights where the dendrogram tree should be cut.

threshold

threshold used on the dissimilarity to join clusters for tNN. If no threshold is specified then the threshold stored in the EMM is used.

method

clustering method used by hclust.

...

additional arguments passed on to the clustering algorithm.

prune

logical; prune states with less than prune counts before reclustering.

copy

logical; make a copy of x before reclustering? Otherwise the function will change x!

Value

An object of class "EMM" or, if copy=FALSE a refernece to the changed object passed as x.

Clustering information is available as the attribute "cluster_info". The information provided depends in the clustering algorithm (see hclust, kmeans and pam).

Details

For recluster_kmeans k can also be a set of initial cluster centers (see argument centers for kmeans in package stats).

For recluster_hclust k or h can also be a vector. The result is then a list with several (nested) EMMs, one for each value.

For recluster_reachability reclusters all clusters which are reachable from each other. A cluster \(j\) is reachable from \(i\) if \(j\)'s center is closer to \(i\)'s center than h or if \(j\) is reachable by any cluster reachable by \(i\).

For recluster_tNN reclusters such that two clusters with centers less than the threshold apart will be reclustered into a single cluster. This is useful, for example, after combining two models.

For recluster_transitions does not recluster clusters! It find groups of clusters which are overlapping (centers are less than 2 thresholds apart) and then redistributes the transition weights such that all members of one group are connected to all the members of the other group using the same weight.

See Also

merge_clusters, prune, kmeans, hclust, pam

Examples

# NOT RUN {
data(EMMsim)
emm <- EMM(threshold = .2)
build(emm, EMMsim_train)

## do reclustering on a copy of the emm and plot dendrogram
emm_hc <- recluster_hclust(emm, h = 0.6)

attr(emm_hc, "cluster_info")

## compare original and clustered EMM
op <- par(mfrow = c(2, 2), pty = "m")   
plot(emm, method= "MDS", main ="original EMM", data = EMMsim_train) 
plot(attr(emm_hc, "cluster_info")$dendrogram)
abline(h=0.6, col="red")
plot(emm_hc, method="MDS", main ="clustered EMM", data = EMMsim_train) 
plot(emm_hc, method="MDS", main ="clustered EMM") 
par(op)
# }

Run the code above in your browser using DataCamp Workspace