netfacs  R Documentation 
The netfacs
function underlies most other functions in this
package.
It takes the data set and reports the observed and expected
probabilities that elements and combinations of elements occur in this data
set, and whether this differs from a null condition.
netfacs( data, condition = NULL, test.condition = NULL, null.condition = NULL, duration = NULL, ran.trials = 1000, control = NULL, random.level = NULL, combination.size = 2, 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 compared across conditions; if NULL, all events will be tested against a random null condition based on permutations 
test.condition 
level of 'condition' that is supposed to be tested 
null.condition 
level of 'condition' that is used to create the null distribution of values; if NULL, all levels that are not the test condition will be used 
duration 
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 
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. 
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.
Function returns a Result data frame that includes the combination name, how many elements it consisted of, how often it was observed, the probability it was observed under this condition, the expected probability under null condition (based on the permutation or bootstrap), effect size (difference between observed probability and expected probability), pvalue (how many randomisations were more extreme), and for direct comparisons of contexts the specificity (probability that the condition is in fact the test condition if that combination is known) and probability increase (the factor by which the probability of the element is higher in the test than null condition)
'event.size.information' contains information about the observed and expected size of combination or elements per event based on the randomisations
Alex Mielke, Alan V. Rincon
### how do angry facial expressions differ from nonangry ones? data(emotions_set) angry.face < netfacs( data = emotions_set[[1]], condition = emotions_set[[2]]$emotion, test.condition = "anger", null.condition = NULL, duration = NULL, ran.trials = 100, control = NULL, random.level = NULL, combination.size = 5, tail = "upper.tail", use_parallel = TRUE, n_cores = 2 ) head(angry.face$result, 20) angry.face$event.size.information
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