- egg : Egg data object
The data to be analyzed
- subjgroup : list of strings or ints
String/int variables indicating how to group over subjects. Must be
the length of the number of subjects
- subjname : string
Name of the subject grouping variable
- listgroup : list of strings or ints
String/int variables indicating how to group over list. Must be
the length of the number of lists
- listname : string
Name of the list grouping variable
- analysis : string
This is the analysis you want to run. Can be accuracy, spc, pfr,
temporal or fingerprint
- position : int
Optional argument for pnr analysis. Defines encoding position of item
to run pnr. Default is 0, and it is zero indexed
- permute : bool
Optional argument for fingerprint/temporal cluster analyses. Determines
whether to correct clustering scores by shuffling recall order for each list
to create a distribution of clustering scores (for each feature). The
“corrected” clustering score is the proportion of clustering scores in
that random distribution that were lower than the clustering score for
the observed recall sequence. Default is False.
- n_perms : int
Optional argument for fingerprint/temporal cluster analyses. Number of
permutations to run for “corrected” clustering scores. Default is 1000 (
per recall list).
- parallel : bool
Option to use multiprocessing (this can help speed up the permutations
tests in the clustering calculations)
- match : str (exact, best or smooth)
Matching approach to compute recall matrix. If exact, the presented and
recalled items must be identical (default). If best, the recalled item
that is most similar to the presented items will be selected. If smooth,
a weighted average of all presented items will be used, where the
weights are derived from the similarity between the recalled item and
each presented item.
- distance : str
The distance function used to compare presented and recalled items.
Applies only to ‘best’ and ‘smooth’ matching approaches. Can be any
distance function supported by numpy.spatial.distance.cdist.