Prune States and/or Transitions

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

rare_transitions(x, count_threshold, ...)

rare_clusters(x, count_threshold, ...)

# S4 method for class 'EMM'
prune(
  x,
  count_threshold,
  clusters = TRUE,
  transitions = FALSE,
  copy = TRUE,
  compact = TRUE
)

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.

...

further arguments (currently not used).

clusters

logical; prune clusters?

transitions

logical; prune transitions?

copy

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

compact

logical; tries make the data structure used for the temporal model more compact after pruning.

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, compact

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)
#>           1           2           3           4           5           6 
#> 0.002441406 0.067382812 0.007812500 0.265625000 0.531250000 0.125000000 
#>           7 
#> 1.000000000 
transition_matrix(emm_l, type="counts")
#>          1        2        3        4       5     6 7
#> 1 1.000000 1.004883 1.000000 1.000000 1.00000 1.000 1
#> 2 1.001953 1.000000 1.007812 1.000000 1.00000 1.125 1
#> 3 1.000000 1.000000 1.000000 1.015625 1.00000 1.000 1
#> 4 1.000000 1.000000 1.000000 1.000000 1.53125 1.000 1
#> 5 1.000000 1.062500 1.000000 1.000000 1.00000 1.000 2
#> 6 1.000000 1.000000 1.000000 1.250000 1.00000 1.000 1
#> 7 1.000000 1.000000 1.000000 1.000000 1.00000 1.000 1

## rare state/transitions
rare_clusters(emm_l, count_threshold=0.1)
#> [1] "1" "2" "3"
rare_transitions(emm_l, count_threshold=0.1)
#>      from to 
#> [1,] "2"  "1"
#> [2,] "1"  "2"
#> [3,] "5"  "2"
#> [4,] "2"  "3"
#> [5,] "3"  "4"

## 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)