Package 'tna'

Description

Provides tools for performing transition network analysis (TNA), including functions for building TNA models, plotting transition networks, and calculating centrality measures. The package relies on the igraph for centrality measure calculations.

Author(s)

Sonsoles López-Pernas, Santtu Tikka, Mohammed Saqr

References

Saqr M., López-Pernas S., Törmänen T., Kaliisa R., Misiejuk K., Tikka S. (2025). Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK '25), 351-361.

Banerjee A., Chandrasekhar A., Duflo E., Jackson M. (2014). Gossip: Identifying Central Individuals in a Social Network. Working Paper.

Kivimaki, I., Lebichot, B., Saramaki, J., Saerens, M. (2016). Two betweenness centrality measures based on Randomized Shortest Paths. Scientific Reports, 6, 19668.

Serrano, M. A., Boguna, M., Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106, 6483-6488.

Zhang, B., Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1).

See Also

Useful links:

• https://github.com/sonsoleslp/tna/

• https://sonsoles.me/tna/

• Report bugs at https://github.com/sonsoleslp/tna/issues/

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna()

Description

Coerce a Specific Group from a group_tna Object into an igraph Object.

Usage

## S3 method for class 'group_tna'
as.igraph(x, which, ...)

Arguments

Value

An igraph object.

See Also

Helper functions as.igraph.matrix(), as.igraph.tna()

Description

Coerce a Weight Matrix into an igraph Object.

Usage

## S3 method for class 'matrix'
as.igraph(x, mode = "directed", ...)

Arguments

Value

An igraph object.

See Also

Helper functions as.igraph.group_tna(), as.igraph.tna()

Description

Coerce a tna Object into an igraph Object.

Usage

## S3 method for class 'tna'
as.igraph(x, mode = "directed", ...)

Arguments

Value

An igraph object.

See Also

Helper functions as.igraph.group_tna(), as.igraph.matrix()

Description

This function builds a network from a transition matrix in a tna object and computes edge betweenness for the network.

Usage

betweenness_network(x, directed = TRUE, invert = TRUE)

## S3 method for class 'tna'
betweenness_network(x, directed = TRUE, invert = TRUE)

Arguments

Value

A tna object where the edge weights are edge betweenness values.

See Also

Centrality measure functions centralities(), plot.group_tna_centralities(), plot.tna_centralities(), print.group_tna_centralities(), print.tna_centralities()

Examples

model <- tna(group_regulation)
betweenness_network(model)

Description

Perform bootstrapping on transition networks created from sequence data stored in a tna object. Bootstrapped estimates of edge weights are returned with confidence intervals and significance testing.

Usage

bootstrap(x, iter, level, method, threshold, consistency_range)

## S3 method for class 'tna'
bootstrap(
  x,
  iter = 1000,
  level = 0.05,
  method = "stability",
  threshold,
  consistency_range = c(0.75, 1.25)
)

## S3 method for class 'group_tna'
bootstrap(
  x,
  iter = 1000,
  level = 0.05,
  method = "stability",
  threshold,
  consistency_range = c(0.75, 1.25)
)

Arguments

Details

The function first computes the original edge weights for the specified cluster from the tna object. It then performs bootstrapping by resampling the sequence data and recalculating the edge weights for each bootstrap sample. The mean and standard deviation of the transitions are computed, and confidence intervals are derived. The function also estimates p-values for each edge and identifies significant edges based on the specified significance level. A matrix of significant edges (those with estimated p-values below the significance level) is generated. Additional statistics on removed edges (those not considered significant) are provided.

All results, including the original transition matrix, bootstrapped estimates, and summary statistics for removed edges, are returned in a structured list.

Value

A tna_bootstrap object which is a list containing the following elements:

• weights_orig: The original edge weight matrix.

• weights_sig: The matrix of significant transitions (those with estimated p-values below the significance level).

• weights_mean: The mean weight matrix from the bootstrap samples.

• weights_sd: The standard deviation matrix from the bootstrap samples.

• cr_lower: The lower bound matrix of the consistency range for the edge weights.

• cr_upper: The upper bound matrix of the consistency range for the edge weights.

• ci_lower: The lower bound matrix of the bootstrap confidence intervals for the edge weights.

• ci_upper: The upper bound matrix of the bootstrap confidence intervals for the edge weights.

• p_values: The matrix of estimated p-values for the edge weights.

• summary: A data.frame summarizing the edges, their weights, p-values, statistical significance, consistency ranges, and confidence intervals.

If x is a group_tna object, the output is a group_tna_bootstrap object, which is a list of tna_bootstrap objects.

See Also

Validation functions deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
# Small number of iterations for CRAN
bootstrap(model, iter = 10)

Description

Bootstrap the edge weights of all cliques of a given size in a tna model, producing per-clique mean weights, p-values, confidence intervals, and consistency-range bounds.

Usage

bootstrap_cliques(x, size, threshold, iter, level, consistency_range)

## S3 method for class 'tna'
bootstrap_cliques(
  x,
  size = 2L,
  threshold = 0,
  iter = 1000,
  level = 0.05,
  consistency_range = c(0.75, 1.25)
)

Arguments

Value

A data.frame (also of class tna_bootstrap_cliques) with one row per clique and the columns clique, mean_weight, p_values, sig, cr_lower, cr_upper, ci_lower, ci_upper.

Examples

model <- tna(group_regulation)
# Small number of iterations for CRAN
boot_cliq <- bootstrap_cliques(model, size = 2, iter = 10)

Description

Construct a transition network analysis (TNA) model from sequence data. The function takes a data set of sequence of events or states as input and builds a TNA model. It extracts the edge weights and initial probabilities from the data along with the state labels. The function also accepts weight matrices and initial state probabilities directly.

Usage

build_model(x, ...)

## Default S3 method:
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  params = list(),
  inits,
  ...
)

## S3 method for class 'matrix'
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  params = list(),
  inits,
  ...
)

## S3 method for class 'stslist'
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  cols = tidyselect::everything(),
  params = list(),
  concat = 1L,
  begin_state,
  end_state,
  ...
)

## S3 method for class 'data.frame'
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  cols = tidyselect::everything(),
  concat = 1L,
  params = list(),
  begin_state,
  end_state,
  ...
)

## S3 method for class 'tna_data'
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  params = list(),
  concat = 1L,
  begin_state,
  end_state,
  ...
)

## S3 method for class 'tsn'
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  params = list(),
  concat = 1L,
  begin_state,
  end_state,
  ...
)

tna(x, ...)

ftna(x, ...)

ctna(x, ...)

atna(x, ...)

tsn(x, ...)

Arguments

Value

An object of class tna which is a list containing the following elements:

• weights: An adjacency matrix of the model (weight matrix).

• inits: A numeric vector of initial values for each state. For matrix type x, this element will be NULL if inits is not directly provided

• labels: A character vector of the state labels, or NULL if there are no labels.

• data: The original sequence data that has been converted to an internal format used by the package when x is a stslist or a data.frame object. Otherwise NULL.

See Also

Basic functions hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- build_model(group_regulation)
print(model)

model <- tna(group_regulation)

model <- ftna(group_regulation)

model <- ctna(group_regulation)

model <- atna(group_regulation)

Description

Calculates several centrality measures. See 'Details' for information about the measures.

Usage

centralities(x, loops = FALSE, normalize = FALSE, invert = TRUE, measures)

## S3 method for class 'tna'
centralities(x, loops = FALSE, normalize = FALSE, invert = TRUE, measures)

## S3 method for class 'matrix'
centralities(x, loops = FALSE, normalize = FALSE, invert = TRUE, measures)

## S3 method for class 'group_tna'
centralities(x, loops = FALSE, normalize = FALSE, invert = TRUE, measures)

Arguments

Details

The following measures are provided:

• OutStrength: Outgoing strength centrality, calculated using igraph::strength() with mode = "out". It measures the total weight of the outgoing edges from each node.

• InStrength: Incoming strength centrality, calculated using igraph::strength() with mode = "in". It measures the total weight of the incoming edges to each node.

• ClosenessIn: Closeness centrality (incoming), calculated using igraph::closeness() with mode = "in". It measures how close a node is to all other nodes based on the incoming paths.

• ClosenessOut: Closeness centrality (outgoing), calculated using igraph::closeness() with mode = "out". It measures how close a node is to all other nodes based on the outgoing paths.

• Closeness: Closeness centrality (overall), calculated using igraph::closeness() with mode = "all". It measures how close a node is to all other nodes based on both incoming and outgoing paths.

• Betweenness: Betweenness centrality defined by the number of geodesics calculated using igraph::betweenness().

• BetweennessRSP: Betweenness centrality based on randomized shortest paths (Kivimäki et al. 2016). It measures the extent to which a node lies on the shortest paths between other nodes.

• Diffusion: Diffusion centrality of Banerjee et.al. (2014). It measures the influence of a node in spreading information through the network.

• Clustering: Signed clustering coefficient of Zhang and Horvath (2005) based on the symmetric adjacency matrix (sum of the adjacency matrix and its transpose). It measures the degree to which nodes tend to cluster together.

Value

A tna_centralities object which is a tibble (tbl_df). containing centrality measures for each state.

See Also

Centrality measure functions betweenness_network(), plot.group_tna_centralities(), plot.tna_centralities(), print.group_tna_centralities(), print.tna_centralities()

Examples

model <- tna(group_regulation)

# Centrality measures including loops in the network
centralities(model)

# Centrality measures excluding loops in the network
centralities(model, loops = FALSE)

# Centrality measures normalized
centralities(model, normalize = TRUE)

Description

This function identifies cliques of a specified size in a transition network. It searches for cliques, i.e., complete subgraphs where every pair of nodes is connected, of size n in the transition matrix for the specified cluster in the tna object.

Usage

cliques(x, ...)

## S3 method for class 'tna'
cliques(x, size = 2, threshold = 0, sum_weights = FALSE, ...)

## S3 method for class 'group_tna'
cliques(x, size = 2, threshold = 0, sum_weights = FALSE, ...)

Arguments

Value

A tna_cliques object which is a list of two elements:

• weights is a matrix of the edge weights in the clique.

• inits is a numeric vector of initial weights for the clique.

If x is a group_tna object, a group_tna_cliques object is returned instead, which is a list or tna_cliques objects.

See Also

Clique-related functions plot.group_tna_cliques(), plot.tna_cliques(), print.group_tna_cliques(), print.tna_cliques()

Examples

model <- tna(group_regulation)

# Find  2-cliques (dyads)
cliq <- cliques(model, size = 2)

model <- group_tna(engagement_mmm)
cliques(model)

Description

Performs clustering using specified dissimilarity measures and clustering methods. The rows of the data are first converted to strings and compared using the dissimilarity measures available in the stringdist package.

Usage

cluster_data(
  data,
  k,
  dissimilarity = "hamming",
  method = "pam",
  na_syms = c("*", "%"),
  weighted = FALSE,
  lambda = 1,
  ...
)

cluster_sequences(
  data,
  k,
  dissimilarity = "hamming",
  method = "pam",
  na_syms = c("*", "%"),
  weighted = FALSE,
  lambda = 1,
  ...
)

Arguments

Value

A tna_clustering object which is a list containing:

• data: The original data.

• k: The number of clusters.

• assignments: An integer vector of cluster assignments.

• silhouette: Silhouette score measuring clustering quality.

• sizes: An integer vector of cluster sizes.

• method: The clustering method used.

• distance: The distance matrix.

Examples

data <- data.frame(
  T1 = c("A", "B", "A", "C", "A", "B"),
  T2 = c("B", "A", "B", "A", "C", "A"),
  T3 = c("C", "C", "A", "B", "B", "C")
)

# PAM clustering with optimal string alignment (default)
result <- cluster_sequences(data, k = 2)

Description

This function detects communities within the transition networks (represented by the tna object). It uses various algorithms to find communities in the graph representation of transitions and returns a list of communities for each cluster or a specified cluster. If multiple transition matrices exist, the function iterates over each cluster in the tna object to find communities using different algorithms. The function uses the igraph package to convert the transition matrices into graphs and then applies community detection algorithms (e.g., Walktrap, Fast Greedy, Label Propagation, Infomap, Edge Betweenness, Leading Eigenvector, and Spin Glass).

Usage

communities(x, methods, gamma)

## S3 method for class 'tna'
communities(x, methods, gamma = 1)

## S3 method for class 'group_tna'
communities(x, methods, gamma = 1)

Arguments

Value

An object of class tna_communities which is a list with an element for each cluster containing:

• counts: A list with the number of communities found by each algorithm.

• assignments: A data.frame where each row corresponds to a node and each column to a community detection algorithm, with color-coded community assignments.

If x is a group_tna object, a group_tna_communities object is returned instead, which is a list of tna_communities objects.

See Also

Community detection functions plot.group_tna_communities(), plot.tna_communities(), print.group_tna_communities(), print.tna_communities()

Cluster-related functions group_model(), mmm_stats(), rename_groups()

Examples

model <- tna(group_regulation)
comm <- communities(model)

Description

Various distances, measures of dissimilarity and similarity, correlations and other metrics are computed to compare the models. Optionally, the weight matrices of the models can be scaled before comparison. The resulting object can be used to produce heatmap plots and scatterplots to further illustrate the differences.

Usage

compare(x, ...)

## S3 method for class 'tna'
compare(x, y, scaling = "none", measures = character(0), network = TRUE, ...)

## S3 method for class 'matrix'
compare(x, y, scaling = "none", measures = character(0), network = TRUE, ...)

Arguments

Value

A tna_comparison object, which is a list containing the following elements:

• matrices: A list containing the scaled matrices of the input tna objects or the scaled inputs themselves in the case of matrices.

• difference_matrix: A matrix of differences x - y.

• edge_metrics: A data.frame of edge-level metrics about the differences.

• summary_metrics: A data.frame of summary metrics of the differences across all edges.

• network_metrics: A data.frame of network metrics for both x and y.

• centrality_differences: A data.frame of differences in centrality measures computes from x and y.

• centrality_correlations: A numeric vector of correlations of the centrality measures between x and y.

See Also

Model comparison functions compare.group_tna(), compare_sequences(), plot.tna_comparison(), plot.tna_sequence_comparison(), plot_compare(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

# Comparing TNA models
model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
comp1 <- compare(model_x, model_y)

# Comparing matrices
mat_x <- model_x$weights
mat_y <- model_y$weights
comp2 <- compare(mat_x, mat_y)

# Comparing a matrix to a TNA model
comp3 <- compare(mat_x, model_y)

Description

Performs comprehensive sequence comparison analysis between groups. All patterns of the sequences (subsequences of specific length) are extracted from all sequences in each group. The pattern frequencies are compared between the groups using a permutation test. The reported effect size is the difference between the observed test statistic (sum of squared differences between the observed and expected counts) and the mean value over the permutation samples divided by their standard deviation times square root of the number of observations.

Usage

compare_sequences(x, ...)

## Default S3 method:
compare_sequences(
  x,
  group,
  sub,
  min_freq = 5L,
  test = TRUE,
  iter = 1000L,
  adjust = "bonferroni",
  ...
)

## S3 method for class 'group_tna'
compare_sequences(
  x,
  sub,
  min_freq = 5L,
  test = TRUE,
  iter = 1000L,
  adjust = "bonferroni",
  ...
)

Arguments

Value

A tna_sequence_comparison object, which is a data.frame with columns giving the names of the patterns, pattern frequencies, pattern proportions (within patterns of the same length), effect sizes, and p-values of the tests.

See Also

Model comparison functions compare(), compare.group_tna(), plot.tna_comparison(), plot.tna_sequence_comparison(), plot_compare(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

# Use a subset to keep the example fast.
idx <- c(1:500, 1001:1500)
group <- c(rep("High", 500), rep("Low", 500))
comp <- compare_sequences(group_regulation[idx, ], group)

# With permutation test (small number of iterations for CRAN)
comp_test <- compare_sequences(
  group_regulation[idx, ],
  group,
  test = TRUE,
  iter = 5
)

Description

Compare Grouped TNA Models with Comprehensive Metrics

Usage

## S3 method for class 'group_tna'
compare(
  x,
  i = 1L,
  j = 2L,
  scaling = "none",
  measures = character(0),
  network = TRUE,
  ...
)

Arguments

Value

A tna_comparison object. See compare.tna() for details.

See Also

Model comparison functions compare(), compare_sequences(), plot.tna_comparison(), plot.tna_sequence_comparison(), plot_compare(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

model <- group_model(engagement_mmm)
compare(model, i = 1, j = 2)

Description

Restore a Pruned Transition Network Analysis Model

Usage

deprune(x, ...)

## S3 method for class 'tna'
deprune(x, ...)

## S3 method for class 'tna'
reprune(x, ...)

## S3 method for class 'group_tna'
deprune(x, ...)

Arguments

Value

A tna or group_tna object that has not been pruned.

See Also

Validation functions bootstrap(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
pruned_model <- prune(model, method = "threshold", threshold = 0.1)
depruned_model <- deprune(pruned_model) # restore original model

Description

Students' engagement states (Active / Average / Disengaged) throughout a whole study program. The data was generated synthetically based on the article "The longitudinal association between engagement and achievement varies by time, students' profiles, and achievement state: A full program study"

Usage

engagement

Format

A stslist object (sequence data).

Source

doi:10.1016/j.compedu.2023.104787

See Also

Datasets engagement_mmm, group_regulation, group_regulation_long

Description

A tna_mmm object structurally equivalent to a fitted seqHMM mixture Markov model, used by the package's examples for group_model() and mmm_stats(). Generated deterministically by random_tna_mmm() with engagement-flavored state labels and three clusters; carries no foreign class references so it is safe to bundle.

Usage

engagement_mmm

Format

A tna_mmm object containing three clusters over the states "Engaged", "Moderate", "Disengaged".

Details

Re-generate via data-raw/engagement_mmm.R whenever the simulator changes.

Source

Generated by data-raw/engagement_mmm.R using random_tna_mmm().

See Also

Datasets engagement, group_regulation, group_regulation_long

Description

Estimates the stability of centrality measures in a network using subset sampling without replacement. It allows for dropping varying proportions of cases and calculates correlations between the original centralities and those computed using sampled subsets.

Usage

estimate_cs(
  x,
  loops,
  normalize,
  invert,
  measures,
  iter,
  method,
  drop_prop,
  threshold,
  certainty,
  progressbar = NULL,
  detailed = NULL
)

estimate_centrality_stability(
  x,
  loops,
  normalize,
  invert,
  measures,
  iter,
  method,
  drop_prop,
  threshold,
  certainty,
  progressbar = NULL,
  detailed = NULL
)

## S3 method for class 'tna'
estimate_cs(
  x,
  loops = FALSE,
  normalize = FALSE,
  invert = TRUE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE,
  detailed = NULL
)

## S3 method for class 'tna'
estimate_centrality_stability(
  x,
  loops = FALSE,
  normalize = FALSE,
  invert = TRUE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE,
  detailed = NULL
)

## S3 method for class 'group_tna'
estimate_cs(
  x,
  loops = FALSE,
  normalize = FALSE,
  invert = TRUE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE,
  detailed = NULL
)

## S3 method for class 'group_tna'
estimate_centrality_stability(
  x,
  loops = FALSE,
  normalize = FALSE,
  invert = TRUE,
  measures = c("InStrength", "OutStrength", "Betweenness"),
  iter = 1000,
  method = "pearson",
  drop_prop = seq(0.1, 0.9, by = 0.1),
  threshold = 0.7,
  certainty = 0.95,
  progressbar = FALSE,
  detailed = NULL
)

Arguments

Details

The function works by repeatedly resampling the data, dropping varying proportions of cases, and calculating centrality measures on the subsets. The correlation between the original centralities and the resampled centralities is calculated for each drop proportion. The stability of each centrality measure is then summarized using a centrality stability (CS) coefficient, which represents the proportion of dropped cases at which the correlations drop below a given threshold (default 0.7).

The results can be visualized by plotting the output object showing the stability of the centrality measures across different drop proportions, along with confidence intervals. The CS-coefficients are displayed in the subtitle.

Value

A tna_stability object which is a list with an element for each measure with the following elements:

• cs_coefficient: The centrality stability (CS) coefficient of the measure.

• correlations: A matrix of correlations between the original centrality and the resampled centralities for each drop proportion.

If x is a group_tna object, a group_tna_stability object is returned instead, which is a list of tna_stability objects.

See Also

Validation functions bootstrap(), deprune(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
# Small number of iterations and drop proportions for CRAN
estimate_cs(
  model,
  drop_prop = seq(0.3, 0.9, by = 0.2),
  measures = c("InStrength", "OutStrength"),
  iter = 10
)

Description

This function constructs a transition network analysis (TNA) model for each group from a given sequence, wide-format dataframe or a mixture Markov model.

Usage

group_model(x, ...)

## Default S3 method:
group_model(
  x,
  group,
  type = "relative",
  scaling = character(0L),
  groupwise = FALSE,
  cols = tidyselect::everything(),
  params = list(),
  concat = 1L,
  na.rm = TRUE,
  ...
)

## S3 method for class 'tna_mmm'
group_model(
  x,
  type = "relative",
  scaling = character(0L),
  groupwise = FALSE,
  params = list(),
  na.rm = TRUE,
  ...
)

## S3 method for class 'mhmm'
group_model(
  x,
  type = "relative",
  scaling = character(0L),
  groupwise = FALSE,
  params = list(),
  na.rm = TRUE,
  ...
)

## S3 method for class 'tna_clustering'
group_model(
  x,
  type = "relative",
  scaling = character(0L),
  groupwise = FALSE,
  params = list(),
  na.rm = TRUE,
  ...
)

group_tna(x, ...)

group_ftna(x, ...)

group_ctna(x, ...)

group_atna(x, ...)

Arguments

Value

An object of class group_tna which is a list containing one element per cluster. Each element is a tna object.

See Also

Cluster-related functions communities(), mmm_stats(), rename_groups()

Examples

# Manually specified groups
group <- c(rep("High", 1000), rep("Low", 1000))
model <- group_model(group_regulation, group = group)

# Groups defined by a mixed Markov model
model <- group_model(engagement_mmm)

model <- group_tna(group_regulation, group = gl(2, 1000))

model <- group_ftna(group_regulation, group = gl(2, 1000))

model <- group_ctna(group_regulation, group = gl(2, 1000))

model <- group_atna(group_regulation, group = gl(2, 1000))

Description

Students' regulation during collaborative learning. Students' interactions were coded as: "adapt", "cohesion", "consensus", "coregulate", "discuss", "emotion", "monitor", "plan", "synthesis"

Usage

group_regulation

Format

A data.frame object.

Source

The data was generated synthetically.

See Also

Datasets engagement, engagement_mmm, group_regulation_long

Description

Students' regulation during collaborative learning. This is the same dataset as group_regulation but in long format. In addition to students' actions (Action), it contains the student identifier (Actor), timestamp (Time), Course name, and collaboration Group. It also includes a column (Achiever) indicating whether the student is a high or low achiever.

Usage

group_regulation_long

Format

A data.frame object.

Source

The data was generated synthetically from group_regulation

See Also

Datasets engagement, engagement_mmm, group_regulation

Description

Plot a Histogram of Edge Weights for a group_tna Object.

Usage

## S3 method for class 'group_tna'
hist(x, ...)

Arguments

Value

A list (invisibly) of histogram objects of the edge weights of each cluster.

See Also

Basic functions build_model(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- group_model(engagement_mmm)
hist(model)

Description

Plot a Histogram of Edge Weights in the Network

Usage

## S3 method for class 'tna'
hist(x, breaks, col = "lightblue", main, xlab, border = "white", ...)

Arguments

Value

A histogram object of edge weights.

See Also

Basic functions build_model(), hist.group_tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- tna(group_regulation)
hist(model)

Description

This function transforms wide format data where features are in separate columns into a long format suitable for sequence analysis. It creates windows of data based on row order and generates sequence order within these windows.

Usage

import_data(data, cols, id_cols, window_size = 1, replace_zeros = TRUE)

Arguments

Value

A data.frame in long format with added columns for window and sequence order.

See Also

Other data: import_onehot(), list_random_state_pools(), prepare_data(), print.tna_data(), random_group_tna(), random_tna(), random_tna_mmm(), simulate.group_tna(), simulate.tna()

Examples

data <- data.frame(
  ID = c("A", "A", "B", "B"),
  Time = c(1, 2, 1, 2),
  feature1 = c(10, 0, 15, 20),
  feature2 = c(5, 8, 0, 12),
  feature3 = c(2, 4, 6, 8),
  other_col = c("X", "Y", "Z", "W")
)

# Using a vector
long_data1 <- import_data(
  data = data,
  cols = c(feature1, feature2),
  id_cols = c("ID", "Time"),
  window_size = 2,
  replace_zeros = TRUE
)

# Using a column range
long_data2 <- import_data(
  data = data,
  cols = feature1:feature3,
  id_cols = c("ID", "Time"),
  window_size = 2,
  replace_zeros = TRUE
)

Description

Import One-Hot Data

Usage

import_onehot(
  data,
  cols,
  actor,
  session,
  interval,
  window_size = 1L,
  window_type = "tumbling",
  aggregate = FALSE
)

Arguments

Value

The processed data as a data.frame.

See Also

Other data: import_data(), list_random_state_pools(), prepare_data(), print.tna_data(), random_group_tna(), random_tna(), random_tna_mmm(), simulate.group_tna(), simulate.tna()

Examples

d <- data.frame(
  actor = gl(100, 5),
  session = gl(10, 50),
  feature1 = rbinom(500, 1, prob = 0.33),
  feature2 = rbinom(500, 1, prob = 0.25),
  feature3 = rbinom(500, 1, prob = 0.50)
)
onehot1 <- import_onehot(d, feature1:feature3)
onehot2 <- import_onehot(d, feature1:feature3, "actor", "session")

List Built-in Label Pools for random_tna()

Description

Return the names of the curated state-label pools available to random_tna() and random_group_tna(), with their sizes.

Usage

list_random_state_pools()

Value

A named integer vector mapping pool names to pool sizes.

See Also

Other data: import_data(), import_onehot(), prepare_data(), print.tna_data(), random_group_tna(), random_tna(), random_tna_mmm(), simulate.group_tna(), simulate.tna()

Examples

list_random_state_pools()

Description

Retrieve Statistics from a Mixture Markov Model (MMM)

Usage

mmm_stats(x, level = 0.05)

## S3 method for class 'tna_mmm'
mmm_stats(x, level = 0.05)

## S3 method for class 'mhmm'
mmm_stats(x, level = 0.05)

Arguments

Value

A data.frame object.

See Also

Cluster-related functions communities(), group_model(), rename_groups()

Examples

mmm_stats(engagement_mmm)

Description

This function compares two networks built from sequence data using permutation tests. The function builds Markov models for two sequence objects, computes the transition probabilities, and compares them by performing permutation tests. It returns the differences in transition probabilities, effect sizes, estimated p-values, and confidence intervals.

Usage

permutation_test(x, ...)

## S3 method for class 'tna'
permutation_test(
  x,
  y,
  adjust = "none",
  iter = 1000,
  paired = FALSE,
  level = 0.05,
  measures = character(0),
  ...
)

Arguments

Value

A tna_permutation object which is a list with two elements: edges and centralities, both containing the following elements:

• stats: A data.frame of original differences, effect sizes, and estimated p-values for each edge or centrality measure. The effect size is computed as the observed difference divided by the standard deviation of the differences of the permuted samples.

• diffs_true: A matrix of differences in the data.

• diffs_sig: A matrix showing the significant differences.

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
# Small number of iterations for CRAN
permutation_test(model_x, model_y, iter = 20)

Description

Test edge weight differences between all pairs or a subset of pairs of a group_tna object. See permutation_test.tna() for more details.

Usage

## S3 method for class 'group_tna'
permutation_test(
  x,
  groups,
  adjust = "none",
  iter = 1000,
  paired = FALSE,
  level = 0.05,
  measures = character(0),
  consecutive = FALSE,
  ...
)

Arguments

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- group_model(engagement_mmm)
# Small number of iterations for CRAN
permutation_test(model, iter = 20)

Description

Plot an Association Network

Usage

plot_associations(x, ...)

## S3 method for class 'tna'
plot_associations(x, edge_color, ...)

Arguments

Value

A cograph_network object.

Examples

model <- ftna(group_regulation)
plot_associations(model)

Description

Plots the difference network between model x and model y. The edges are computed from subtracting the two models. The pie chart is the difference in initial probabilities between model x and model y. Green color indicates that xis greater than yand red indicates otherwise.

Usage

plot_compare(x, ...)

## S3 method for class 'tna'
plot_compare(x, y, posCol = "#009900", negCol = "red", ...)

Arguments

Value

A cograph_network object displaying the difference network between the two models.

See Also

Model comparison functions compare(), compare.group_tna(), compare_sequences(), plot.tna_comparison(), plot.tna_sequence_comparison(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

model_x <- tna(group_regulation[group_regulation[, 1] == "plan", ])
model_y <- tna(group_regulation[group_regulation[, 1] != "plan", ])
plot_compare(model_x, model_y)

Description

Plot the Difference Network Between Two Groups

Usage

## S3 method for class 'group_tna'
plot_compare(x, i = NULL, j = NULL, ...)

Arguments

Value

A cograph_network object displaying the difference network between the two clusters

See Also

Model comparison functions compare(), compare.group_tna(), compare_sequences(), plot.tna_comparison(), plot.tna_sequence_comparison(), plot_compare(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

model <- group_model(engagement_mmm)
plot_compare(model)

Description

Plot the Frequency Distribution of States

Usage

plot_frequencies(x, ...)

## S3 method for class 'tna'
plot_frequencies(x, width = 0.7, hjust = 1.2, show_label = TRUE, colors, ...)

Arguments

Value

A ggplot object.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- tna(group_regulation)
plot_frequencies(model)
plot_frequencies(model, width =  0.5, colors = "pink")

Description

Plot the Frequency Distribution of States

Usage

## S3 method for class 'group_tna'
plot_frequencies(
  x,
  label,
  colors,
  width = 0.7,
  palette = "Set2",
  show_label = TRUE,
  position = "dodge",
  hjust = 1.2,
  ...
)

Arguments

Value

A ggplot object.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- group_model(engagement_mmm)
# Default
plot_frequencies(model)
# Default labels outside and custom colors
plot_frequencies(
  model,
  width = 0.9,
  hjust = -0.3,
  colors = c("#218516", "#f9c22e", "#53b3cb")
)
# Stacked with no labels
plot_frequencies(model, position = "stack", show_label = FALSE)
# Fill
plot_frequencies(model, position = "fill", hjust = 1.1)

Description

Create a Mosaic Plot of Transitions or Events

Usage

plot_mosaic(x, ...)

## S3 method for class 'tna'
plot_mosaic(x, ...)

Arguments

Value

A ggplot object.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

ftna_model <- ftna(group_regulation)
plot_mosaic(ftna_model)

Description

Plot State Frequencies as a Mosaic Between Two Groups

Usage

## S3 method for class 'group_tna'
plot_mosaic(x, label, ...)

Arguments

Value

A ggplot object.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- group_model(engagement, group = rep(1:3, length.out = 1000))
plot_mosaic(model)

Description

Plot State Frequencies as a Mosaic Between Two Groups

Usage

## S3 method for class 'tna_data'
plot_mosaic(x, group, label = "Group", ...)

Arguments

Value

A ggplot object.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

d <- data.frame(
  time = rep(1:5, rep = 4),
  group = rep(1:4, each = 5),
  event = sample(LETTERS[1:3], 20, replace = TRUE)
)
sequence_data <- prepare_data(
  d,
  time = "time",
  actor = "group",
  action = "event"
)
plot_mosaic(sequence_data, group = "group")

Description

Create a Sequence Index Plot or a Distribution Plot

Usage

plot_sequences(x, ...)

## S3 method for class 'tna'
plot_sequences(
  x,
  group,
  type = "index",
  scale = "proportion",
  geom = "bar",
  include_na = FALSE,
  na_color = "white",
  sort_by,
  show_n = TRUE,
  border,
  title,
  legend_title,
  xlab,
  ylab,
  tick = 5,
  ncol = 2L,
  ...
)

## S3 method for class 'tna_data'
plot_sequences(
  x,
  group,
  type = "index",
  scale = "proportion",
  geom = "bar",
  include_na = FALSE,
  colors,
  na_color = "white",
  sort_by,
  show_n = TRUE,
  border,
  title,
  legend_title,
  xlab,
  ylab,
  tick = 5,
  ncol = 2L,
  ...
)

## Default S3 method:
plot_sequences(
  x,
  cols = tidyselect::everything(),
  group,
  type = "index",
  scale = "proportion",
  geom = "bar",
  include_na = FALSE,
  colors,
  na_color = "white",
  sort_by,
  show_n = TRUE,
  border,
  title,
  legend_title,
  xlab,
  ylab,
  tick = 5,
  ncol = 2L,
  ...
)

## S3 method for class 'group_tna'
plot_sequences(
  x,
  type = "index",
  scale = "proportion",
  geom = "bar",
  include_na = FALSE,
  na_color = "white",
  sort_by,
  show_n = TRUE,
  border,
  title,
  legend_title,
  xlab,
  ylab,
  tick = 1,
  ncol = 2L,
  ...
)

Arguments

Value

A ggplot object containing either a sequence index plot or a state distribution plot, faceted by group when supplied.

Examples

# Sequence index plot (default)
plot_sequences(
  group_regulation,
  group = rep(1:2, each = 1000),
)
# State distribution plot
plot_sequences(
  group_regulation,
  group = rep(1:2, each = 1000),
  type = "distribution",
)

Description

Plots a transition network of each cluster using cograph.

Usage

## S3 method for class 'group_tna'
plot(x, title, which, ...)

Arguments

Value

NULL (invisibly).

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- group_model(engagement_mmm)
plot(model, which = 1)

Description

Plot a Bootstrapped Grouped Transition Network Analysis Model

Usage

## S3 method for class 'group_tna_bootstrap'
plot(x, title, ...)

Arguments

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- group_model(engagement_mmm)
# Small number of iterations for CRAN
boot <- bootstrap(model, iter = 10)
plot(boot)

Description

Plot Centrality Measures

Usage

## S3 method for class 'group_tna_centralities'
plot(
  x,
  reorder = TRUE,
  ncol = 3,
  scales = c("free_x", "fixed"),
  colors,
  palette = "Set2",
  labels = TRUE,
  ...
)

Arguments

Value

A ggplot object displaying a line chart for each centrality with one line per cluster.

See Also

Centrality measure functions betweenness_network(), centralities(), plot.tna_centralities(), print.group_tna_centralities(), print.tna_centralities()

Examples

model <- group_model(engagement_mmm)
cm <- centralities(model)
plot(cm)

Description

Plot Found Cliques

Usage

## S3 method for class 'group_tna_cliques'
plot(x, title, ...)

Arguments

Value

A list (invisibly) with one element per cluster. Each element contains a cograph_network plot when only one clique is present per cluster, otherwise the element is NULL.

See Also

Clique-related functions cliques(), plot.tna_cliques(), print.group_tna_cliques(), print.tna_cliques()

Examples

model <- group_model(engagement_mmm)
cliq <- cliques(model, size = 2)
plot(cliq, ask = FALSE)

Description

Plot Detected Communities

Usage

## S3 method for class 'group_tna_communities'
plot(x, title, colors, ...)

Arguments

Value

A list (invisibly) of cograph_network objects in which the nodes are colored by community for each cluster.

See Also

Community detection functions communities(), plot.tna_communities(), print.group_tna_communities(), print.tna_communities()

Examples

model <- group_model(engagement_mmm)
comm <- communities(model)
plot(comm)

Description

Plot Permutation Test Results

Usage

## S3 method for class 'group_tna_permutation'
plot(x, title, ...)

Arguments

Value

A list (invisibly) of cograph_network objects depicting the significant difference between each pair.

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- group_tna(engagement_mmm)
# Small number of iterations for CRAN
perm <- permutation_test(model, iter = 20)
plot(perm)

Description

Plot Centrality Stability Results

Usage

## S3 method for class 'group_tna_stability'
plot(x, ...)

Arguments

Value

A list (invisibly) of ggplot objects displaying the stability analysis plot.

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- group_model(engagement_mmm)
# Low number of iterations for CRAN
stability <- estimate_cs(
  model,
  drop_prop = c(0.3, 0.5, 0.7, 0.9),
  iter = 10
)
plot(stability)

Description

This function plots a transition network analysis (TNA) model using the cograph package. The nodes in the graph represent states, with node sizes corresponding to initial state probabilities. Edge labels represent the edge weights of the network. See cograph::splot() for details on how to further configure the plot.

Usage

## S3 method for class 'tna'
plot(
  x,
  node_list,
  use_list_order = TRUE,
  scale_nodes,
  scaling_factor = 0.5,
  ...
)

Arguments

Value

A cograph_network plot of the transition network.

See Also

Basic functions build_model(), hist.group_tna(), hist.tna(), plot.group_tna(), plot_frequencies(), plot_frequencies.group_tna(), plot_mosaic(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- tna(group_regulation)
plot(model)

Description

Plot a Bootstrapped Transition Network Analysis Model

Usage

## S3 method for class 'tna_bootstrap'
plot(x, ...)

Arguments

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_permutation(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
# Small number of iterations for CRAN
boot <- bootstrap(model, iter = 50)
plot(boot)

Description

Plots the centrality measures of a tna_centralities object as a lollipop chart. The resulting plot includes facets for each centrality measure, showing the values for each state. The returned plot is a ggplot2 object, so it can be easily modified and styled. See centralities() for details on the centrality measures.

Usage

## S3 method for class 'tna_centralities'
plot(
  x,
  reorder = TRUE,
  ncol = 3,
  scales = c("free_x", "fixed"),
  colors,
  labels = TRUE,
  ...
)

Arguments

Value

A ggplot object displaying the lollipop charts for each centrality measure.

See Also

Centrality measure functions betweenness_network(), centralities(), plot.group_tna_centralities(), print.group_tna_centralities(), print.tna_centralities()

Examples

tna_model <- tna(group_regulation)
cm <- centralities(tna_model)
plot(cm, ncol = 3, reorder = TRUE)

Description

Plot Cliques of a TNA Network

Usage

## S3 method for class 'tna_cliques'
plot(x, n = 6, first = 1, show_loops = FALSE, colors, ask = TRUE, ...)

Arguments

Value

NULL (invisibly).

See Also

Clique-related functions cliques(), plot.group_tna_cliques(), print.group_tna_cliques(), print.tna_cliques()

Examples

model <- tna(group_regulation)
cliq <- cliques(model, size = 2)
plot(cliq, n = 1, ask = FALSE)

Description

This function visualizes the communities detected within a tna object based on different community detection algorithms and their corresponding color mappings.

Usage

## S3 method for class 'tna_communities'
plot(x, colors, method, ...)

Arguments

Value

A cograph_network object in which the nodes are colored by community.

See Also

Community detection functions communities(), plot.group_tna_communities(), print.group_tna_communities(), print.tna_communities()

Examples

model <- tna(group_regulation)
comm <- communities(model)
plot(comm, method = "leading_eigen")

Description

Plot the Comparison of Two TNA Models or Matrices

Usage

## S3 method for class 'tna_comparison'
plot(
  x,
  type = "heatmap",
  population = "difference",
  method = "pearson",
  name_x = "x",
  name_y = "y",
  ...
)

Arguments

Value

A ggplot object.

References

Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007), Measuring and Testing Dependence by Correlation of Distances, Annals of Statistics, 35(6), 2769-2794. doi:10.1214/009053607000000505

See Also

Model comparison functions compare(), compare.group_tna(), compare_sequences(), plot.tna_sequence_comparison(), plot_compare(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
comp <- compare(model_x, model_y)
plot(comp)

Description

Plot the Significant Differences from a Permutation Test

Usage

## S3 method for class 'tna_permutation'
plot(x, colors, posCol = "#009900", negCol = "red", edge_labels = TRUE, ...)

Arguments

Value

A cograph_network object containing only the significant edges according to the permutation test.

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_reliability(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model_x <- tna(group_regulation[1:200, ])
model_y <- tna(group_regulation[1001:1200, ])
# Small number of iterations for CRAN
perm <- permutation_test(model_x, model_y, iter = 20)
plot(perm)

Description

Plot Reliability Analysis Results

Usage

## S3 method for class 'tna_reliability'
plot(x, type = "histogram", metric = "Median Abs. Diff.", ...)

Arguments

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_stability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

# Small number of iterations for CRAN
model <- tna(engagement)
rel <- reliability(model, iter = 20)
plot(rel)

Description

Visualize the differences in pattern counts by plotting the standardized residuals by pattern and group.

Usage

## S3 method for class 'tna_sequence_comparison'
plot(
  x,
  n = 10,
  legend = TRUE,
  cells = TRUE,
  text_color = "white",
  digits = 2L,
  ...
)

Arguments

Value

A ggplot object.

See Also

Model comparison functions compare(), compare.group_tna(), compare_sequences(), plot.tna_comparison(), plot_compare(), plot_compare.group_tna(), print.tna_comparison(), print.tna_sequence_comparison()

Examples

idx <- c(1:500, 1001:1500)
group <- c(rep("High", 500), rep("Low", 500))
comp <- compare_sequences(group_regulation[idx, ], group)
plot(comp)

Description

This function visualizes the centrality stability results produced by the estimate_centrality_stability function. It shows how different centrality measures' correlations change as varying proportions of cases are dropped, along with their confidence intervals (CIs).

Usage

## S3 method for class 'tna_stability'
plot(x, level = 0.05, ...)

Arguments

Details

The function aggregates the results for each centrality measure across multiple proportions of dropped cases (e.g., 0.1, 0.2, ..., 0.9) and calculates the mean and the desired quantiles for each proportion. The confidence intervals (CIs) are computed based on the quantiles and displayed in the plot.

If no valid data is available for a centrality measure (e.g., missing or NA values), the function skips that measure with a warning.

The plot includes:

• The mean correlation for each centrality measure as a function of the proportion of dropped cases.

• Shaded confidence intervals representing CIs for each centrality measure.

• A horizontal dashed line at the threshold value used for calculating the CS-coefficient.

• A subtitle listing the CS-coefficients for each centrality measure.

Value

A ggplot object displaying the stability analysis plot.

See Also

Validation functions bootstrap(), deprune(), estimate_cs(), permutation_test(), permutation_test.group_tna(), plot.group_tna_bootstrap(), plot.group_tna_permutation(), plot.group_tna_stability(), plot.tna_bootstrap(), plot.tna_permutation(), plot.tna_reliability(), print.group_tna_bootstrap(), print.group_tna_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_clustering(), print.tna_permutation(), print.tna_reliability(), print.tna_stability(), prune(), pruning_details(), reliability(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
cs <- estimate_cs(model, iter = 10)
plot(cs)

Description

Processes a dataset to create user sessions based on time gaps, ordering columns, or actor groupings. It supports different ways to understand order in user behavior and provides flexibility when widening the data.

Usage

prepare_data(
  data,
  actor,
  time,
  action,
  order,
  time_threshold = 900,
  custom_format = NULL,
  is_unix_time = FALSE,
  unix_time_unit = "seconds",
  unused_fn = dplyr::first
)

Arguments