TRAC: Creating a Markov Model from a Regular Clustering

Create an Markov model from a regular clustering (k-means or PAM) of sequence data.

Usage

TRAC(x, data = NULL, centers = NULL, measure = "euclidean")

Arguments

x

a clustering object (result of kmeans or PAM), a data set (a data matrix), or a vector with (integer) cluster assignments.

data

the data used for clustering (only used if x is a cluster assignment vector).

centers

if x is a cluster assignment vector, then a data.frame or matrix with the cluster centers needs to be supplies. Otherwise, centers is ignored.

measure

used distance measure.

Value

A EMM object representing the clustering of sequence data.

Details

The order is inferred from the order in the original data set.

Examples

data("EMMsim")

## using kmeans
cl <- kmeans(EMMsim_train, 10)
emm <- TRAC(cl)
emm
#> EMM with 10 states/clusters.
#>  Measure: euclidean 
#>  Threshold: 0.499165 
#>  Centroid: TRUE 
#>  Lambda: 0 
plot(emm, method = "MDS")


## using a cluster assignment vector (taken from the k-means clustering above)
x <- cl$cluster
emm <- TRAC(x, data = EMMsim_train)
emm
#> EMM with 10 states/clusters.
#>  Measure: euclidean 
#>  Threshold: 0.4028334 
#>  Centroid: TRUE 
#>  Lambda: 0 
plot(emm, method = "MDS")