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The tna package includes functionalities for finding
cliques of the transition network as well as discovering communities. We
begin by loading the package and the example data set
group_regulation.
library("tna")
#> 'tna' package version 1.1.0
#> ------------------------------------------------------
#> Tikka, S., López-Pernas, S., and Saqr, M. (2025).
#> tna: An R Package for Transition Network Analysis.
#> Applied Psychological Measurement.
#> https://doi.org/10.1177/01466216251348840
#> ------------------------------------------------------
#> Please type 'citation("tna")' for more citation information.
#> See the package website at https://sonsoles.me/tna/
data("group_regulation", package = "tna")We fit the TNA model to the data.
tna_model <- tna(group_regulation)
print(tna_model)
#> State Labels :
#>
#> adapt, cohesion, consensus, coregulate, discuss, emotion, monitor, plan, synthesis
#>
#> Transition Probability Matrix :
#>
#> adapt cohesion consensus coregulate discuss emotion
#> adapt 0.0000000000 0.27308448 0.47740668 0.02161100 0.05893910 0.11984283
#> cohesion 0.0029498525 0.02713864 0.49793510 0.11917404 0.05958702 0.11563422
#> consensus 0.0047400853 0.01485227 0.08200348 0.18770738 0.18802338 0.07268131
#> coregulate 0.0162436548 0.03604061 0.13451777 0.02335025 0.27360406 0.17208122
#> discuss 0.0713743356 0.04758289 0.32118451 0.08428246 0.19488737 0.10579600
#> emotion 0.0024673951 0.32534367 0.32040888 0.03419105 0.10186817 0.07684173
#> monitor 0.0111653873 0.05582694 0.15910677 0.05792045 0.37543615 0.09071877
#> plan 0.0009745006 0.02517460 0.29040117 0.01721618 0.06789021 0.14682475
#> synthesis 0.2346625767 0.03374233 0.46625767 0.04447853 0.06288344 0.07055215
#> monitor plan synthesis
#> adapt 0.03339882 0.01571709 0.000000000
#> cohesion 0.03303835 0.14100295 0.003539823
#> consensus 0.04661084 0.39579712 0.007584137
#> coregulate 0.08629442 0.23908629 0.018781726
#> discuss 0.02227284 0.01164262 0.140976968
#> emotion 0.03630596 0.09975326 0.002819880
#> monitor 0.01814375 0.21563154 0.016050244
#> plan 0.07552379 0.37420822 0.001786584
#> synthesis 0.01226994 0.07515337 0.000000000
#>
#> Initial Probabilities :
#>
#> adapt cohesion consensus coregulate discuss emotion monitor
#> 0.0115 0.0605 0.2140 0.0190 0.1755 0.1515 0.1440
#> plan synthesis
#> 0.2045 0.0195
plot(tna_model)
Next, we apply several community finding algorithms to the model (see
?communities for more details), and plot the results for
the leading_eigen algorithm.
cd <- communities(tna_model)
plot(cd, method = "leading_eigen")
Cliques can be obtained with the cliques function. Here
we look for dyads and triads by setting size = 2 and
size = 3, respectively. Finally, we plot the results.
layout(matrix(1:4, ncol = 2, byrow = TRUE))
dyads <- cliques(tna_model, size = 2, threshold = 0.2)
triads <- cliques(tna_model, size = 3, threshold = 0.05)
plot(dyads, ask = FALSE)
plot(triads, ask = FALSE)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.