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Prune a Transition Network based on Transition Probabilities

Source: R/pruning.R
prune.Rd

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

x

An object of class tna or group_tna

...

Arguments passed to bootstrap() when using method = "bootstrap" and when a tna_bootstrap is not supplied.

method

A character string describing the pruning method. The available options are "threshold", "lowest", "bootstrap" and "disparity", corresponding to the methods listed in Details. The default is "threshold".

threshold

A numeric value specifying the edge weight threshold. Edges with weights below or equal to this threshold will be considered for removal.

lowest

A numeric value specifying the lowest percentage of non-zero edges. This percentage of edges with the lowest weights will be considered for removal. The default is 0.05.

level

A numeric value representing the significance level for the disparity filter. Defaults to 0.5.

boot

A tna_bootstrap object to be used for pruning with method "boot". The method argument is ignored if this argument is supplied.

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)