tna (version 0.4.0)

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

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 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.

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

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

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

• summary: A data.frame summarizing the edges, their weights, p-values, statistical significance 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.

Arguments

x

A tna or a group_tna object created from sequence data.

...

Ignored.

iter

An integer specifying the number of bootstrap samples to draw. Defaults to 1000.

level

A numeric value representing the significance level for hypothesis testing and confidence intervals. Defaults to 0.05.

method

A character string. This argument defines the bootstrap test statistic. The "stability" option (the default) compares edge weights against a range of "consistent" values defined by consistency_range. Weights that fall outside this range are considered insignificant. In other words, an edge is considered significant if its value is within the range in (1 - level) * 100% of the bootstrap samples. The "threshold" option instead compares the edge weights against a user-specified threshold value.

threshold

A numeric value to compare edge weights against. The default is the 10th percentile of the edge weights. Used only when method = "threshold".

consistency_range

A numeric vector of length 2. Determines how much the edge weights may deviate (multiplicatively) from their observed values (below and above) before they are considered insignificant. The default is c(0.75, 1.25) which corresponds to a symmetric 25% deviation range. Used only when method = "stability".

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 calculates p-values for each edge and identifies significant edges based on the specified significance level. A matrix of significant edges (those with 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.

See Also

Evaluation and validation functions permutation_test(), prune(), pruning_details()

Cluster-related functions centralities(), cliques(), communities(), deprune(), estimate_cs(), group_model(), hist.group_tna(), mmm_stats(), plot.group_tna(), plot.group_tna_centralities(), plot.group_tna_cliques(), plot.group_tna_communities(), plot.group_tna_stability(), plot_compare.group_tna(), plot_mosaic.group_tna(), plot_mosaic.tna_data(), print.group_tna(), print.group_tna_bootstrap(), print.group_tna_centralities(), print.group_tna_cliques(), print.group_tna_communities(), print.group_tna_stability(), print.summary.group_tna(), print.summary.group_tna_bootstrap(), prune(), pruning_details(), rename_groups(), reprune(), summary.group_tna(), summary.group_tna_bootstrap()

Examples

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