The tna Package.

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 qgraph and igraph for network plotting and 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/

• http://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()

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

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()

Coerce a Weight Matrix into an igraph Object.

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()

Coerce a tna Object into an igraph Object.

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()

Build and Visualize a Network with Edge Betweenness

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)

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

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)

Bootstrap Transition Networks from Sequence Data

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

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

Bootstrap Cliques of Transition Networks from Sequence Data

Description

Bootstrap Cliques of Transition Networks from Sequence Data

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

Build a Transition Network Analysis Model

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,
  type = "relative",
  scaling = character(0L),
  cols = tidyselect::everything(),
  params = list(),
  inits,
  begin_state,
  end_state
)

## Default S3 method:
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  cols = tidyselect::everything(),
  params = list(),
  inits,
  begin_state,
  end_state
)

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

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

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

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

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

## S3 method for class 'tsn_ews'
build_model(
  x,
  type = "relative",
  scaling = character(0L),
  cols = tidyselect::everything(),
  params = list(),
  inits,
  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)

Calculate Centrality Measures for a Transition Matrix

Description

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

Usage

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

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

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

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

Identify Cliques in a Transition Network

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)

Cluster Sequences via Dissimilarity Matrix based on String Distances

Description

Performs clustering on sequence data using specified dissimilarity measures and clustering methods. The sequences are first converted to strings and compared using the stringdist package.

Usage

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

## S3 method for class 'tna_clustering'
print(x, ...)

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)
print(result)

Community Detection for Transition Networks

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)

Compare Two Matrices or TNA Models with Comprehensive Metrics

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", ...)

## S3 method for class 'matrix'
compare(x, y, scaling = "none", ...)

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)

Compare Grouped TNA Models with Comprehensive Metrics

Description

Compare Grouped TNA Models with Comprehensive Metrics

Usage

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

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)

Compare Sequences Between Groups

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. For two groups, the proportions of the patterns between the groups are compared with a z-test for proportions. For more than two groups, the Chi-squared test is applied. Both tests are applied within patterns of the same length.

Usage

compare_sequences(x, ...)

## Default S3 method:
compare_sequences(x, group, sub, min_freq = 5L, correction = "bonferroni", ...)

## S3 method for class 'group_tna'
compare_sequences(x, sub, min_freq = 5L, correction = "bonferroni", ...)

Arguments

Value

A tna_sequence_comparison object, which is a data.frame with columns giving the names of the pattern, pattern frequencies, pattern proportions (within patterns of the same length), test statistic values 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

group <- c(rep("High", 1000), rep("Low", 1000))
comp <- compare_sequences(group_regulation, group)

Restore a Pruned Transition Network Analysis Model

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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

Example Data on Student Engagement

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

Example Mixed Markov Model Fitted to the engagement Data

Description

Example Mixed Markov Model Fitted to the engagement Data

Usage

engagement_mmm

Format

A mhmm object.

Source

The data was generated via mixed_markov_model.R in https://github.com/sonsoleslp/tna/tree/main/data-raw/

See Also

Datasets engagement, group_regulation, group_regulation_long

Estimate Centrality Stability

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,
  measures,
  iter,
  method,
  drop_prop,
  threshold,
  certainty,
  progressbar
)

estimate_centrality_stability(
  x,
  loops,
  normalize,
  measures,
  iter,
  method,
  drop_prop,
  threshold,
  certainty,
  progressbar
)

## S3 method for class 'tna'
estimate_cs(
  x,
  loops = FALSE,
  normalize = FALSE,
  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
)

## S3 method for class 'tna'
estimate_centrality_stability(
  x,
  loops = FALSE,
  normalize = FALSE,
  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
)

## S3 method for class 'group_tna'
estimate_cs(
  x,
  loops = FALSE,
  normalize = FALSE,
  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
)

## S3 method for class 'group_tna'
estimate_centrality_stability(
  x,
  loops = FALSE,
  normalize = FALSE,
  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
)

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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
)

Build a Grouped Transition Network Analysis Model

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(),
  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))

Example Wide Data on Group Regulation

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

Example Long Data on Group Regulation

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

Plot a Histogram of Edge Weights for a group_tna Object.

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)

Plot a Histogram of Edge Weights in the Network

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)

Import Wide Format Sequence Data as Long Format Sequence Data

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(), prepare_data(), print.tna_data(), 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
)

Import One-Hot Data and Create a Co-Occurrence Network Model

Description

Import One-Hot Data and Create a Co-Occurrence Network Model

Usage

import_onehot(data, cols, window = 1L)

Arguments

Value

A tna object for the co-occurrence model.

See Also

Other data: import_data(), prepare_data(), print.tna_data(), simulate.tna()

Examples

d <- data.frame(
  window = gl(100, 5),
  feature1 = rbinom(500, 1, prob = 0.33),
  feature2 = rbinom(500, 1, prob = 0.25),
  feature3 = rbinom(500, 1, prob = 0.50)
)
model <- import_onehot(d, feature1:feature3, window = "window")

Retrieve Statistics from a Mixture Markov Model (MMM)

Description

Retrieve Statistics from a Mixture Markov Model (MMM)

Usage

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)

Compare Two Networks from Sequence Data using a Permutation Test

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Compare Networks using a Permutation Test

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Plot a Grouped Transition Network Analysis Model

Description

Plots a transition network of each cluster using qgraph.

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)

Plot a Bootstrapped Grouped Transition Network Analysis Model

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Plot Centrality Measures

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)

Plot Found Cliques

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 qgraph 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)

Plot Detected Communities

Description

Plot Detected Communities

Usage

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

Arguments

Value

A list (invisibly) of qgraph 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)

Plot Permutation Test Results

Description

Plot Permutation Test Results

Usage

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

Arguments

Value

A list (invisibly) of qgraph 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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Plot Centrality Stability Results

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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Plot a Transition Network Analysis Model

Description

This function plots a transition network analysis (TNA) model using the qgraph 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.

Usage

## S3 method for class 'tna'
plot(
  x,
  labels,
  colors,
  pie,
  cut,
  show_pruned = TRUE,
  pruned_edge_color = "pink",
  edge.color = NA,
  edge.labels = TRUE,
  edge.label.position = 0.65,
  layout = "circle",
  layout_args = list(),
  scale_nodes,
  scaling_factor = 0.5,
  mar = rep(5, 4),
  theme = "colorblind",
  ...
)

Arguments

Value

A qgraph 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)

Plot a Bootstrapped Transition Network Analysis 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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Plot Centrality Measures

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)

Plot Cliques of a TNA Network

Description

Plot Cliques of a TNA Network

Usage

## S3 method for class 'tna_cliques'
plot(
  x,
  n = 6,
  first = 1,
  show_loops = FALSE,
  edge.labels = TRUE,
  edge.label.position = 0.65,
  minimum = 1e-05,
  mar = rep(5, 4),
  layout = "circle",
  layout_args = list(),
  cut = 0.01,
  normalize = TRUE,
  ask = TRUE,
  colors,
  theme = "colorblind",
  ...
)

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)

Plot Communities

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 = "spinglass", ...)

Arguments

Value

A qgraph 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")

Plot the Comparison of Two TNA Models or Matrices

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, Vol. 35 No. 6, pp. 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)

Plot the Significant Differences from a Permutation Test

Description

Plot the Significant Differences from a Permutation Test

Usage

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

Arguments

Value

A qgraph 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_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_permutation(), print.tna_stability(), prune(), pruning_details(), 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)

Plot a Sequence Comparison

Description

Plot a Sequence Comparison

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

group <- c(rep("High", 1000), rep("Low", 1000))
comp <- compare_sequences(group_regulation, group)
plot(comp)

Plot Centrality Stability Results

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(), 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_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

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

Plot an Association Network

Description

Plot an Association Network

Usage

plot_associations(x, ...)

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

Arguments

Value

A qgraph plot of the network.

Examples

model <- ftna(group_regulation)
plot_associations(model)

Plot the Difference Network Between Two Models

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,
  theme = NULL,
  palette = "colorblind",
  posCol = "#009900",
  negCol = "red",
  ...
)

Arguments

Value

A qgraph 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)

Plot the Difference Network Between Two Groups

Description

Plot the Difference Network Between Two Groups

Usage

## S3 method for class 'group_tna'
plot_compare(x, i = 1L, j = 2L, ...)

Arguments

Value

A qgraph 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)

Plot the Frequency Distribution of States

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")

Plot the Frequency Distribution of States

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)

Plot a Transition Network Model from a Matrix of Edge Weights

Description

Plot a Transition Network Model from a Matrix of Edge Weights

Usage

plot_model(
  x,
  labels,
  colors,
  cut,
  edge.labels = TRUE,
  edge.label.position = 0.65,
  layout = "circle",
  layout_args = list(),
  mar = rep(5, 4),
  theme = "colorblind",
  ...
)

Arguments

Value

See plot.tna().

Examples

m <- matrix(rexp(25), 5, 5)
plot_model(m)

Create a Mosaic Plot of Transitions or Events

Description

Create a Mosaic Plot of Transitions or Events

Usage

plot_mosaic(x, ...)

## S3 method for class 'tna'
plot_mosaic(x, digits = 1, ...)

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)

Plot State Frequencies as a Mosaic Between Two Groups

Description

Plot State Frequencies as a Mosaic Between Two Groups

Usage

## S3 method for class 'group_tna'
plot_mosaic(x, label, digits = 1L, ...)

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_mmm)
plot_mosaic(model)

Plot State Frequencies as a Mosaic Between Two Groups

Description

Plot State Frequencies as a Mosaic Between Two Groups

Usage

## S3 method for class 'tna_data'
plot_mosaic(x, group, label = "Group", digits = 1, ...)

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")

Create a Sequence Index Plot or a Distribution Plot

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

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",
)

Compute User Sessions from Event Data

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

Value

A tna_data object, which is a list with the following elements:

• long_data: The processed data in long format.

• sequence_data: The processed data on the sequences in wide format, with actions/events as different variables structured with sequences.

• meta_data: Other variables from the original data in wide format.

• statistics: A list containing summary statistics: total sessions, total actions, unique users, time range (if applicable), and top sessions and user by activities.

See Also

Other data: import_data(), import_onehot(), print.tna_data(), simulate.tna()

Examples

results <- prepare_data(
  group_regulation_long, actor = "Actor", time = "Time", action = "Action"
)
print(results$sequence_data)
print(results$meta_data)
print(results$statistics)

# Custom order column
data_ordered <- tibble::tibble(
   user = c("A", "A", "A", "B", "B", "C", "C", "C"),
   order = c(1, 2, 3, 1, 2, 1, 2, 3),
   action = c(
     "view", "click", "add_cart", "view",
     "checkout", "view", "click", "share"
   )
)
results_ordered <- prepare_data(
  data_ordered, actor = "user", order = "order", action = "action"
)
print(results_ordered$sequence_data)
print(results_ordered$meta_data)
print(results_ordered$statistics)

# No actor scenario leading to a single session
data_single_session <- tibble::tibble(
  action = c(
    "view", "click", "add_cart", "view",
    "checkout", "view", "click", "share"
   )
)
results_single <- prepare_data(data_single_session, action = "action")
print(results_single$sequence_data)
print(results_single$meta_data)
print(results_single$statistics)

# Multiple actors
data_multi_actor <- tibble::tibble(
  user = c("A", "A", "A", "A", "B", "B", "B", "B"),
  session = c(1, 1, 2, 2, 1, 1, 2, 2),
  action = c(
    "view", "click", "add_cart", "view",
    "checkout", "view", "click", "share"
  )
)
results_multi_actor <- prepare_data(
  data_multi_actor, actor = c("user", "session"), action = "action"
)
print(results_multi_actor$sequence_data)
print(results_multi_actor$meta_data)
print(results_multi_actor$statistics)

Print a group_tna Object

Description

Print a group_tna Object

Usage

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

Arguments

Value

x (invisibly).

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(), plot_mosaic.tna_data(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- group_model(engagement_mmm)
print(model)

Print group_tna Bootstrap Results

Description

Print group_tna Bootstrap Results

Usage

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

Arguments

Value

x (invisibly).

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_permutation(), print.group_tna_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- group_model(engagement_mmm)
# Low number of iteration for CRAN
boot <- bootstrap(model, iter = 10)
print(boot)

Print Centrality Measures

Description

Print Centrality Measures

Usage

## S3 method for class 'group_tna_centralities'
print(x, ...)

Arguments

Value

x (invisibly).

See Also

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

Examples

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

Print Found Cliques

Description

Print Found Cliques

Usage

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

Arguments

Value

x (invisibly).

See Also

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

Examples

model <- group_model(engagement_mmm)
cliq <- cliques(model, size = 2)
print(cliq)

Print Detected Communities

Description

Print Detected Communities

Usage

## S3 method for class 'group_tna_communities'
print(x, ...)

Arguments

Value

x (invisibly).

See Also

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

Examples

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

Print Permutation Test Results

Description

Print Permutation Test Results

Usage

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

Arguments

Value

x (invisibly).

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_stability(), print.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

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

Print Centrality Stability Results

Description

Print Centrality Stability Results

Usage

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

Arguments

Value

x (invisibly).

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.summary.group_tna_bootstrap(), print.summary.tna_bootstrap(), print.tna_bootstrap(), print.tna_permutation(), print.tna_stability(), prune(), pruning_details(), 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
)
print(stability)

Print a Summary of a Grouped Transition Network Analysis Model

Description

Print a Summary of a Grouped Transition Network Analysis Model

Usage

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

Arguments

Value

x (invisibly).

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(), plot_mosaic.tna_data(), print.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- group_model(engagement_mmm)
print(summary(model))

Print a Bootstrap Summary for a Grouped Transition Network Model

Description

Print a Bootstrap Summary for a Grouped Transition Network Model

Usage

## S3 method for class 'summary.group_tna_bootstrap'
print(x, ...)

Arguments

Value

x (invisibly).

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.tna_bootstrap(), print.tna_bootstrap(), print.tna_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- group_model(engagement_mmm)
# Low number of iteration for CRAN
boot <- bootstrap(model, iter = 10)
print(summary(boot))

Print a TNA Summary

Description

Print a TNA Summary

Usage

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

Arguments

Value

A summary.tna object (invisibly) containing the TNA model network metrics and values.

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(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- tna(group_regulation)
print(summary(model))

Print a Bootstrap Summary

Description

Print a Bootstrap Summary

Usage

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

Arguments

Value

A summary.tna_bootstrap object (invisibly) containing the weight, estimated p-value and confidence interval of each edge.

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.tna_bootstrap(), print.tna_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

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

Print a tna Object

Description

Print a tna Object

Usage

## S3 method for class 'tna'
print(x, generic = FALSE, ...)

Arguments

Value

The tna object passed as argument x (invisibly).

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(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), summary.group_tna(), summary.tna(), tna-package

Examples

model <- tna(group_regulation)
print(model)

Print Bootstrap Results

Description

Print Bootstrap Results

Usage

## S3 method for class 'tna_bootstrap'
print(x, digits = getOption("digits"), type = "both", ...)

Arguments

Value

x (invisibly).

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_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

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

Print Centrality Measures

Description

Print Centrality Measures

Usage

## S3 method for class 'tna_centralities'
print(x, ...)

Arguments

Value

x (invisibly).

See Also

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

Examples

model <- tna(group_regulation)
cm <- centralities(model)
print(cm)

Print Found Cliques of a TNA Network

Description

Print Found Cliques of a TNA Network

Usage

## S3 method for class 'tna_cliques'
print(x, n = 6, first = 1, digits = getOption("digits"), ...)

Arguments

Value

x (invisibly).

See Also

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

Examples

model <- tna(group_regulation)
cliq <- cliques(model, size = 2)
print(cliq)

Print Detected Communities

Description

Print Detected Communities

Usage

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

Arguments

Value

x (invisibly).

See Also

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

Examples

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

Print Comparison Results

Description

Print Comparison Results

Usage

## S3 method for class 'tna_comparison'
print(x, ...)

Arguments

Value

x (invisibly).

See Also

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

Print a TNA Data Object

Description

Print a TNA Data Object

Usage

## S3 method for class 'tna_data'
print(x, data = "sequence", ...)

Arguments

Value

x (invisibly).

See Also

Other data: import_data(), import_onehot(), prepare_data(), simulate.tna()

Examples

res <- prepare_data(group_regulation_long, action = "Action", actor = "Actor",
time = "Time")
print(res, which = "sequence")
print(res, which = "meta")
print(res, which = "long")

Print Permutation Test Results

Description

Print Permutation Test Results

Usage

## S3 method for class 'tna_permutation'
print(x, ...)

Arguments

Value

x (invisibly).

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_stability(), prune(), pruning_details(), 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)
print(perm)

Print a Comparison of Sequences

Description

Print a Comparison of Sequences

Usage

## S3 method for class 'tna_sequence_comparison'
print(x, ...)

Arguments

Value

x (invisibly).

See Also

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

Examples

group <- c(rep("High", 1000), rep("Low", 1000))
comp <- compare_sequences(group_regulation, group)
print(comp)

Print Centrality Stability Results

Description

Print Centrality Stability Results

Usage

## S3 method for class 'tna_stability'
print(x, ...)

Arguments

Value

x (invisibly).

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_permutation(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

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

Prune a Transition Network based on Transition Probabilities

Description

Prunes a network represented by a tna object by removing edges based on a specified threshold, lowest percent of non-zero edge weights, or the disparity filter algorithm (Serrano et al., 2009). It ensures the network remains weakly connected.

Prunes a network represented by a tna object by removing edges based on a specified threshold, lowest percent of non-zero edge weights, or the disparity filter algorithm (Serrano et al., 2009). It ensures the network remains weakly connected.

Usage

prune(x, ...)

## S3 method for class 'tna'
prune(
  x,
  method = "threshold",
  threshold = 0.1,
  lowest = 0.05,
  level = 0.5,
  boot = NULL,
  ...
)

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

Arguments

Value

A pruned tna or group_tna object. Details on the pruning can be viewed with pruning_details(). The original model can be restored with deprune().

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_permutation(), print.tna_stability(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

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_permutation(), print.tna_stability(), pruning_details(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
pruned_threshold <- prune(model, method = "threshold", threshold = 0.1)
pruned_percentile <- prune(model, method = "lowest", lowest = 0.05)
pruned_disparity <- prune(model, method = "disparity", level = 0.5)

Print Detailed Information on the Pruning Results

Description

Print Detailed Information on the Pruning Results

Usage

pruning_details(x, ...)

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

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

Arguments

Value

A data.frame containing the removed edges if x is a tna object, or a list of data.frame objects in the case of group_tna object.

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_permutation(), print.tna_stability(), prune(), reprune(), summary.group_tna_bootstrap(), summary.tna_bootstrap()

Examples

model <- tna(group_regulation)
pruned_threshold <- prune(model, method = "threshold", threshold = 0.1)
pruning_details(pruned_threshold)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

igraph

as.igraph

stats

simulate

Rename Groups

Description

Rename Groups

Usage

rename_groups(x, new_names)

Arguments

Value

A renamed group_tna object.

See Also

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

Examples

model <- group_model(engagement_mmm)
model_renamed <- rename_groups(model, c("A", "B", "C"))

Restore Previous Pruning of a Transition Network Analysis Model

Description

Restore Previous Pruning of a Transition Network Analysis Model

Usage

reprune(x, ...)

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

Arguments

Value

A tna or group_tna object that has not been pruned. The previous pruning result can be reactivated with reprune().

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_permutation(), print.tna_stability(), prune(), pruning_details(), 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
repruned_model <- reprune(depruned_model) # reapply the previous pruning

Simulate Data from a Transition Network Analysis Model

Description

Simulate Data from a Transition Network Analysis Model

Usage

## S3 method for class 'tna'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  max_len = 100L,
  na_range = c(0L, 0L),
  ...
)

Arguments

Value

A data.frame of the simulated sequence data with nsim rows and max_len columns.

See Also

Other data: import_data(), import_onehot(), prepare_data(), print.tna_data()

Examples

model <- tna(group_regulation)
sim <- simulate(model, nsim = 10, max_len = 10)

Build a Social Network Analysis Model

Description

Build a Social Network Analysis Model

Usage

sna(x, aggregate = sum, ...)

Arguments

Value

A tna object representing the model.

Examples

set.seed(123)
d <- data.frame(
  from = sample(LETTERS[1:4], 100, replace = TRUE),
  to = sample(LETTERS[1:4], 100, replace = TRUE),
  weight = rexp(100)
)
model <- sna(d)

Calculate Summary of Network Metrics for a grouped Transition Network

Description

This function calculates a variety of network metrics for a tna object. It computes key metrics such as node and edge counts, network density, mean distance, strength measures, degree centrality, and reciprocity.

Usage

## S3 method for class 'group_tna'
summary(object, combined = TRUE, ...)

Arguments

Details

The function extracts the igraph network for each cluster and computes the following network metrics:

• Node count: Total number of nodes in the network.

• Edge count: Total number of edges in the network.

• Network density: Proportion of possible edges that are present in the network.

• Mean distance: The average shortest path length between nodes.

• Mean and standard deviation of out-strength and in-strength: Measures of the total weight of outgoing and incoming edges for each node.

• Mean and standard deviation of out-degree: The number of outgoing edges from each node.

• Centralization of out-degree and in-degree: Measures of how centralized the network is based on the degrees of nodes.

• Reciprocity: The proportion of edges that are reciprocated (i.e., mutual edges between nodes).

Value

A summary.group_tna object which is a list of lists or a combined data.frame containing the following network metrics:

• node_count: The total number of nodes.

• edge_count: The total number of edges.

• network_Density: The density of the network.

• mean_distance: The mean shortest path length.

• mean_out_strength: The mean out-strength of nodes.

• sd_out_strength: The standard deviation of out-strength.

• mean_in_strength: The mean in-strength of nodes.

• sd_in_strength: The standard deviation of in-strength.

• mean_out_degree: The mean out-degree of nodes.

• sd_out_degree: The standard deviation of out-degree.

• centralization_out_degree: The centralization of out-degree.

• centralization_in_degree: The centralization of in-degree.

• reciprocity: The reciprocity of the network.

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(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.tna(), tna-package

Examples

group <- c(rep("High", 1000), rep("Low", 1000))
model <- group_model(group_regulation, group = group)
summary(model)

Summarize Bootstrap Results for a Grouped Transition Network

Description

Summarize Bootstrap Results for a Grouped Transition Network

Usage

## S3 method for class 'group_tna_bootstrap'
summary(object, ...)

Arguments

Value

A summary.group_tna_bootstrap object containing the weight, estimated p-value and confidence interval of each edge for each cluster.

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_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.tna_bootstrap()

Examples

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

Calculate Summary of Network Metrics for a Transition Network

Description

This function calculates a variety of network metrics for a tna object. It computes key metrics such as node and edge counts, network density, mean distance, strength measures, degree centrality, and reciprocity.

Usage

## S3 method for class 'tna'
summary(object, ...)

Arguments

Details

The function extracts the igraph network and computes the following network metrics:

• Node count: Total number of nodes in the network.

• Edge count: Total number of edges in the network.

• Network density: Proportion of possible edges that are present in the network.

• Mean distance: The average shortest path length between nodes.

• Mean and standard deviation of out-strength and in-strength: Measures of the total weight of outgoing and incoming edges for each node.

• Mean and standard deviation of out-degree: The number of outgoing edges from each node.

• Centralization of out-degree and in-degree: Measures of how centralized the network is based on the degrees of nodes.

• Reciprocity: The proportion of edges that are reciprocated (i.e., mutual edges between nodes).

A summary of the metrics is printed to the console.

Value

A named list containing the following network metrics (invisibly):

• node_count: The total number of nodes.

• edge_count: The total number of edges.

• network_Density: The density of the network.

• mean_distance: The mean shortest path length.

• mean_out_strength: The mean out-strength of nodes.

• sd_out_strength: The standard deviation of out-strength.

• mean_in_strength: The mean in-strength of nodes.

• sd_in_strength: The standard deviation of in-strength.

• mean_out_degree: The mean out-degree of nodes.

• sd_out_degree: The standard deviation of out-degree.

• centralization_out_degree: The centralization of out-degree.

• centralization_in_degree: The centralization of in-degree.

• reciprocity: The reciprocity of the network.

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(), plot_mosaic.tna_data(), print.group_tna(), print.summary.group_tna(), print.summary.tna(), print.tna(), summary.group_tna(), tna-package

Examples

model <- tna(group_regulation)
summary(model)

Summarize Bootstrap Results

Description

Summarize Bootstrap Results

Usage

## S3 method for class 'tna_bootstrap'
summary(object, ...)

Arguments

Value

A summary.tna_bootstrap object containing the weight, estimated p-value and confidence interval of each edge.

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_permutation(), print.tna_stability(), prune(), pruning_details(), reprune(), summary.group_tna_bootstrap()

Examples

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