rEMM (version 1.0-5)

prune: Prune States and/or Transitions

Description

Simplifies an EMM and/or the clustering by removing all clusters/states and/or transitions which have a count of equal or smaller than a given threshold.

Usage

## S3 method for class 'EMM':
prune(x, count_threshold, clusters = TRUE, transitions = FALSE,
    copy = TRUE)
rare_clusters(x, count_threshold, ...)
rare_transitions(x, count_threshold, ...)

Arguments

x

an object of class "EMM"

count_threshold

all states/edges with a count of less or equal to the threshold are removed from the model.

clusters

logical; prune clusters?

transitions

logical; prune transitions?

copy

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

...

further arguments (currently not used).

Value

• prune returns invisibly an object of class EMM. If copy=FALSE then it returns a reference to the changes object passed as x. rare_clusters returns a vector of names of rare clusters. rare_transitions returns a data.frame of rare transitions.

See Also

remove_transitions, remove_clusters

Examples

data("EMMTraffic")

## For the example we use a very high learning rate
emm_l <- EMM(threshold=0.2, measure="eJaccard", lambda = 1)
build(emm_l, EMMTraffic)

## show state counts and transition counts
cluster_counts(emm_l)
transition_matrix(emm_l, type="counts")

## rare state/transitions
rare_clusters(emm_l, count_threshold=0.1)
rare_transitions(emm_l, count_threshold=0.1)

## remove all states with a threshold of 0.1
emm_lr <- prune(emm_l, count_threshold=0.1)

## compare graphs
op <- par(mfrow = c(1, 2), pty = "m")
plot(emm_l, main = "EMM with high learning rate")
plot(emm_lr, main = "Simplified EMM")
par(op)