prune | R Documentation |
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.
## S4 method for signature 'EMM' prune(x, count_threshold, clusters = TRUE, transitions = FALSE, copy = TRUE, compact = TRUE) rare_clusters(x, count_threshold, ...) rare_transitions(x, count_threshold, ...)
x |
an object of class |
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 |
compact |
logical; tries make the data structure used for the temporal model more compact after pruning. |
... |
further arguments (currently not used). |
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.
remove_transitions
,
remove_clusters
,
compact
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.