View source: R/multiple_netfacs.R
multiple.netfacs  R Documentation 
Take dataset and report observed and expected likelihood that elements and combinations of elements occur in this dataset, and whether this differs from a null condition Expected values are based on bootstraps of null distribution, so the values represent distribution of element cooccurrence under null condition; or permutations of the observed distribution to test it against 'random'. The resulting object is the basis for most other functions in this package.
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 )
data 
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 compare across conditions; if NULL, all events will be tested against random 
duration 
numeric vector that contains information on the duration of each event; if NULL, all rows have the same value 
ran.trials 
Number of randomisations that will be performed to find the null distribution 
control 
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 
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 
if not all combinations of elements are of interest (e.g., if the question only concerns single elements or dyads of elements), this variable allows to reduce the results to those combinations, increasing speed 
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, should the bootstrap be parallelized (default is

n_cores 
numeric, the number cores to be used for parallelization. Default is the number of available cores minus 1. 
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
data(emotions_set) emo.faces < multiple.netfacs( data = emotions_set[[1]], condition = emotions_set[[2]]$emotion, ran.trials = 10, # only for example combination.size = 2 ) head(emo.faces$anger$result, 5) head(emo.faces$happy$result, 5)
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