multiple.netfacs {NetFACS}

(Deprecated) Applies the netfacs function across multiple levels of the condition and puts them in a list

Description

This function is deprecated. Please see netfacs_multiple instead

Usage

multiple.netfacs(
  data,
  condition = NULL,
  duration = NULL,
  ran.trials = 1000,
  control = NULL,
  random.level = NULL,
  combination.size = NULL,
  tail = "upper.tail",
  use_parallel = TRUE,
  n_cores = 2
)

Arguments

data

A binary matrix with one column per element, and one row per event, consisting of 1 (element was active during that event) and 0 (element was not active).

condition

character vector of same length as 'data' that contains information on the condition each event belongs to, so probabilities can be compared across conditions

duration

A numeric vector that contains information on the duration of each event; if NULL, all events are assumed to have equal duration.

ran.trials

Number of randomisations that will be performed to find the null distribution.

control

A list of vectors that are used as control variables. During bootstraps, the ratio of events in each level will be adapted. So, for example, if in the test distribution, there are three angry participants for each happy participant, the null distribution will maintain that ratio.

random.level

A character vector of the level on which the randomization should take place. If NULL, the randomization takes place on the event level (i.e., every row can either be selected or not); if a vector is provided, the randomization takes place on the levels of that vector rather than individual events.

combination.size

A positive integer, indicating the maximum combination size of element combinations. Higher numbers will increase computation time. Default is 2.

tail

Either 'upper.tail' (proportion of null probabilities that are larger than observed probabilities), or 'lower.tail' (proportion of null probabilities that are smaller than observed probabilities); default is 'upper.tail'.

use_parallel

Logical, indicating whether randomization or bootstrap should be parallelized (default is TRUE)

n_cores

Numeric, indicating the number cores to be used for parallelization. Default is 2.

Value

Function returns for each level of the condition a list equivalent to the results of the netfacs function; can be used to create multiple networks and graphs at the same time