Per-Class State Distribution as a Tidy Data Frame

Returns a tidy data.frame(group, state, count, proportion) with one row per (group, state) cell. Companion to state_frequencies (which counts unique states in raw sequence input); state_distribution() pulls the same shape of frame from a fitted Nestimate object so analyses don't have to reach for the underlying $data slot directly.

Usage

state_distribution(x, ...)

# S3 method for class 'netobject'
state_distribution(x, ...)

# S3 method for class 'htna'
state_distribution(x, ...)

# S3 method for class 'mcml'
state_distribution(x, include_macro = FALSE, ...)

# S3 method for class 'netobject_group'
state_distribution(x, ...)

# Default S3 method
state_distribution(x, ...)

Arguments

x

A netobject, netobject_group, mcml, or htna object.

...

Currently unused.

include_macro

For mcml: when TRUE, prepend a group = "macro" block aggregating across clusters. Ignored for the other classes.

Value

A data.frame with one row per (group, state) cell and columns group (character), state (character), count (integer), and proportion (numeric, within-group share). A single ungrouped network yields a single group labelled "all".

Details

Used internally by plot_state_frequencies as the data layer behind every chart, and surfaced as the $table slot of the returned state_freq object.

Examples

data(group_regulation_long, package = "Nestimate")
net <- build_network(group_regulation_long, method = "frequency",
                     format = "long", actor = "Actor", action = "Action",
                     order = "Time", group = "Course")
state_distribution(net)
#>    group      state count proportion
#> 1      A  consensus  3298 0.26618241
#> 2      A       plan  2805 0.22639225
#> 3      A    discuss  1960 0.15819209
#> 4      A    emotion  1517 0.12243745
#> 5      A   cohesion   923 0.07449556
#> 6      A coregulate   855 0.06900726
#> 7      A    monitor   602 0.04858757
#> 8      A  synthesis   290 0.02340597
#> 9      A      adapt   140 0.01129944
#> 10     B       plan  2445 0.25399958
#> 11     B  consensus  2226 0.23124870
#> 12     B    discuss  1453 0.15094536
#> 13     B    emotion   995 0.10336588
#> 14     B coregulate   817 0.08487430
#> 15     B   cohesion   590 0.06129233
#> 16     B    monitor   565 0.05869520
#> 17     B  synthesis   274 0.02846458
#> 18     B      adapt   261 0.02711407
#> 19     C       plan  1373 0.24886714
#> 20     C  consensus  1273 0.23074134
#> 21     C    discuss   854 0.15479427
#> 22     C    emotion   563 0.10204821
#> 23     C coregulate   461 0.08355991
#> 24     C    monitor   349 0.06325902
#> 25     C   cohesion   326 0.05909009
#> 26     C  synthesis   165 0.02990756
#> 27     C      adapt   153 0.02773246