The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Using grouped sequence data with tna

TNA also enables the analysis of transition networks constructed from grouped sequence data. In this example, we first fit a mixed Markov model to the engagement data using the seqHMM package and build a grouped TNA model based on this model. First, we load the packages we will use for this example.

library("tna")
library("tibble")
library("dplyr")
library("gt")
library("seqHMM")
data("engagement", package = "tna")

We simulate transition probabilities to initialize the model.

set.seed(265)
tna_model <- tna(engagement)
n_var <- length(tna_model$labels)
n_clusters <- 3
trans_probs <- simulate_transition_probs(n_var, n_clusters)
init_probs <- list(
  c(0.70, 0.20, 0.10),
  c(0.15, 0.70, 0.15),
  c(0.10, 0.20, 0.70)
)

Next, we building and fit the model (this step takes some time to compute, the final model object is also available in the tna package as engagement_mmm).

mmm <- build_mmm(
  engagement,
  transition_probs = trans_probs,
  initial_probs = init_probs
)
fit_mmm <- fit_model(
  modelTrans,
  global_step = TRUE,
  control_global = list(algorithm = "NLOPT_GD_STOGO_RAND"),
  local_step = TRUE,
  threads = 60,
  control_em = list(restart = list(times = 100, n_optimum = 101))
)

Now, we create a new model using the cluster information from the model. Alternatively, if sequence data is provided to group_model(), the group assignments can be provided with the group argument.

tna_model_clus <- group_model(fit_mmm$model)

We can summarize the cluster-specific models

summary(tna_model_clus) |>
  gt() |>
  fmt_number(decimals = 2)
metric Cluster 1 Cluster 2 Cluster 3
Node Count 3.00 3.00 3.00
Edge Count 9.00 8.00 8.00
Network Density 1.00 1.00 1.00
Mean Distance 0.11 0.24 0.30
Mean Out-Strength 1.00 1.00 1.00
SD Out-Strength 0.21 0.35 0.47
Mean In-Strength 1.00 1.00 1.00
SD In-Strength 0.00 0.00 0.00
Mean Out-Degree 3.00 2.67 2.67
SD Out-Degree 0.00 0.58 0.58
Centralization (Out-Degree) 0.00 0.25 0.25
Centralization (In-Degree) 0.00 0.25 0.25
Reciprocity 1.00 0.80 0.80

and their initial probabilities

bind_rows(lapply(tna_model_clus, \(x) x$inits), .id = "Cluster") |>
  gt() |>
  fmt_percent()
Cluster 1 Cluster 2 Cluster 3
33.98% 75.00% 0.00%
32.35% 8.33% 0.00%
33.67% 16.67% 100.00%

as well as transition probabilities.

transitions <- lapply(
  tna_model_clus,
  function(x) {
    x$weights |>
      data.frame() |>
      rownames_to_column("From\\To") |>
      gt() |>
      tab_header(title = names(tna_model_clus)[1]) |>
      fmt_percent()
  }
)
transitions[[1]]
Cluster 1
From\To Active Average Disengaged
Active 85.99% 8.92% 5.09%
Average 31.21% 54.21% 14.58%
Disengaged 4.79% 16.18% 79.03%
 
transitions[[2]]
Cluster 1
From\To Active Average Disengaged
Active 84.09% 15.91% 0.00%
Average 9.26% 62.96% 27.78%
Disengaged 15.56% 51.11% 33.33%
 
transitions[[3]]
Cluster 1
From\To Active Average Disengaged
Active 58.33% 12.50% 29.17%
Average 15.28% 81.94% 2.78%
Disengaged 0.00% 60.00% 40.00%

We can also plot the cluster-specific transitions

layout(t(1:3))
plot(tna_model_clus, vsize = 20, edge.label.cex = 2)

Just like ordinary TNA models, we can prune the rare transitions

pruned_clus <- prune(tna_model_clus, threshold = 0.1)

and plot the cluster transitions after pruning

layout(t(1:3))
plot(pruned_clus, vsize = 20, edge.label.cex = 2)

Centrality measures can also be computed for each cluster directly.

centrality_measures <- c(
  "BetweennessRSP",
  "Closeness",
  "InStrength",
  "OutStrength"
)
centralities_per_cluster <- centralities(
  tna_model_clus,
  measures = centrality_measures
)
plot(
  centralities_per_cluster, ncol = 4,
  colors = c("purple", "orange", "pink")
)

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.