The transition counts behind the flow and network views, as a tidy data
frame. By default every consecutive move is aggregated over time (the
numbers transition_plot() draws); by_time = TRUE instead returns one
block per consecutive time pair, matching the alluvial flow_plot().
Usage
transition_table(x, ...)
# S3 method for class 'vasstra_sequences'
transition_table(x, by_time = FALSE, ...)
# S3 method for class 'vasstra_trajectories'
transition_table(x, by_time = FALSE, group = NULL, ...)
# S3 method for class 'vasstra'
transition_table(x, ...)Value
A tidy data frame. Aggregated: from, to, count, and
probability (row-normalized within from). With by_time = TRUE,
also from_time and to_time, with probability normalized within
each (from_time, from).
See also
transition_plot() and flow_plot() to draw these movements,
transition_centrality() for network centralities.
Examples
data("engagement", package = "VaSSTra")
fit <- vasstra(engagement, n_states = 3, n_trajectories = 3)
transition_table(fit)
#> from to count probability
#> 1 State 1 State 1 156 0.666666667
#> 2 State 2 State 1 73 0.152083333
#> 3 State 3 State 1 2 0.007142857
#> 4 State 1 State 2 73 0.311965812
#> 5 State 2 State 2 335 0.697916667
#> 6 State 3 State 2 73 0.260714286
#> 7 State 1 State 3 5 0.021367521
#> 8 State 2 State 3 72 0.150000000
#> 9 State 3 State 3 205 0.732142857
transition_table(fit, by_time = TRUE)
#> from_time to_time from to count probability
#> 1 1 2 State 1 State 1 19 0.54285714
#> 2 1 2 State 2 State 1 16 0.22857143
#> 3 1 2 State 3 State 1 0 0.00000000
#> 4 1 2 State 1 State 2 14 0.40000000
#> 5 1 2 State 2 State 2 41 0.58571429
#> 6 1 2 State 3 State 2 7 0.18918919
#> 7 1 2 State 1 State 3 2 0.05714286
#> 8 1 2 State 2 State 3 13 0.18571429
#> 9 1 2 State 3 State 3 30 0.81081081
#> 10 2 3 State 1 State 1 24 0.68571429
#> 11 2 3 State 2 State 1 11 0.17741935
#> 12 2 3 State 3 State 1 1 0.02222222
#> 13 2 3 State 1 State 2 11 0.31428571
#> 14 2 3 State 2 State 2 42 0.67741935
#> 15 2 3 State 3 State 2 14 0.31111111
#> 16 2 3 State 1 State 3 0 0.00000000
#> 17 2 3 State 2 State 3 9 0.14516129
#> 18 2 3 State 3 State 3 30 0.66666667
#> 19 3 4 State 1 State 1 22 0.61111111
#> 20 3 4 State 2 State 1 5 0.07462687
#> 21 3 4 State 3 State 1 0 0.00000000
#> 22 3 4 State 1 State 2 12 0.33333333
#> 23 3 4 State 2 State 2 53 0.79104478
#> 24 3 4 State 3 State 2 13 0.33333333
#> 25 3 4 State 1 State 3 2 0.05555556
#> 26 3 4 State 2 State 3 9 0.13432836
#> 27 3 4 State 3 State 3 26 0.66666667
#> 28 4 5 State 1 State 1 19 0.70370370
#> 29 4 5 State 2 State 1 12 0.15384615
#> 30 4 5 State 3 State 1 0 0.00000000
#> 31 4 5 State 1 State 2 8 0.29629630
#> 32 4 5 State 2 State 2 51 0.65384615
#> 33 4 5 State 3 State 2 11 0.29729730
#> 34 4 5 State 1 State 3 0 0.00000000
#> 35 4 5 State 2 State 3 15 0.19230769
#> 36 4 5 State 3 State 3 26 0.70270270
#> 37 5 6 State 1 State 1 22 0.70967742
#> 38 5 6 State 2 State 1 13 0.18571429
#> 39 5 6 State 3 State 1 0 0.00000000
#> 40 5 6 State 1 State 2 9 0.29032258
#> 41 5 6 State 2 State 2 48 0.68571429
#> 42 5 6 State 3 State 2 12 0.29268293
#> 43 5 6 State 1 State 3 0 0.00000000
#> 44 5 6 State 2 State 3 9 0.12857143
#> 45 5 6 State 3 State 3 29 0.70731707
#> 46 6 7 State 1 State 1 23 0.65714286
#> 47 6 7 State 2 State 1 11 0.15942029
#> 48 6 7 State 3 State 1 1 0.02631579
#> 49 6 7 State 1 State 2 12 0.34285714
#> 50 6 7 State 2 State 2 47 0.68115942
#> 51 6 7 State 3 State 2 5 0.13157895
#> 52 6 7 State 1 State 3 0 0.00000000
#> 53 6 7 State 2 State 3 11 0.15942029
#> 54 6 7 State 3 State 3 32 0.84210526
#> 55 7 8 State 1 State 1 27 0.77142857
#> 56 7 8 State 2 State 1 5 0.07812500
#> 57 7 8 State 3 State 1 0 0.00000000
#> 58 7 8 State 1 State 2 7 0.20000000
#> 59 7 8 State 2 State 2 53 0.82812500
#> 60 7 8 State 3 State 2 11 0.25581395
#> 61 7 8 State 1 State 3 1 0.02857143
#> 62 7 8 State 2 State 3 6 0.09375000
#> 63 7 8 State 3 State 3 32 0.74418605