State Transition Network Centralities

Tidy centralities of the state transition network, one row per state. The network is built by Nestimate::build_tna() and the measures come from Nestimate::net_centrality(), so they match tna::centralities().

Usage

transition_centrality(x, ...)

# S3 method for class 'vasstra_sequences'
transition_centrality(
  x,
  measures = c("InStrength", "OutStrength"),
  weights = c("probability", "count"),
  loops = FALSE,
  ...
)

# S3 method for class 'vasstra_trajectories'
transition_centrality(
  x,
  measures = c("InStrength", "OutStrength"),
  weights = c("probability", "count"),
  loops = FALSE,
  group = NULL,
  ...
)

# S3 method for class 'vasstra'
transition_centrality(x, ...)

Arguments

x

A vasstra_sequences, vasstra_trajectories, or vasstra object.

...

Not used.

measures

Centrality measures to compute, passed to Nestimate::net_centrality(). Defaults to c("InStrength", "OutStrength"); "all" returns every built-in measure.

weights

"probability" (default) uses row-normalized transition probabilities from Nestimate::build_tna(); "count" uses raw transition counts from Nestimate::build_ftna().

loops

Include self-transitions in the computation. Default FALSE, matching Nestimate::net_centrality().

group

Optional trajectory label restricting the network to one trajectory's subjects.

Value

A tidy data frame with one row per state and one column per requested measure.

Details

In-strength is the total incoming transition weight, so the state with the largest in-strength is the one the cohort most often moves into. Self-transitions are excluded by default: with loops = TRUE a persistent state scores highly merely because its members stay put, which is a different claim from attracting movement.

See also

transition_plot() to draw the network.

Examples

data("engagement", package = "VaSSTra")
fit <- vasstra(engagement, n_states = 3, n_trajectories = 3)
transition_centrality(fit)
#>     state InStrength OutStrength
#> 1 State 1  0.1592262   0.3333333
#> 2 State 2  0.5726801   0.3020833
#> 3 State 3  0.1713675   0.2678571
transition_centrality(fit, weights = "count")
#>     state InStrength OutStrength
#> 1 State 1         75          78
#> 2 State 2        146         145
#> 3 State 3         77          75