psifr.stats.count_lags_compound#
- psifr.stats.count_lags_compound(list_length, pool_items, recall_items, pool_label=None, recall_label=None, pool_test=None, recall_test=None, test=None, count_unique=False)#
Count lags conditional on the lag of the previous transition.
- Parameters:
list_length (int) – Number of items in each list.
pool_items (list) – List of the serial positions available for recall in each list. Must match the serial position codes used in recall_items.
recall_items (list) – List indicating the serial position of each recall in output order (NaN for intrusions).
pool_label (list, optional) – List of the positions to use for calculating lag. Default is to use pool_items.
recall_label (list, optional) – List of position labels in recall order. Default is to use recall_items.
pool_test (list, optional) – List of some test value for each item in the pool.
recall_test (list, optional) – List of some test value for each recall attempt by output position.
test (callable) – Callable that evaluates each transition between items n and n+1. Must take test values for items n and n+1 and return True if a given transition should be included.
count_unique (bool, optional) – If true, only unique values will be counted toward the possible transitions. If multiple items are avilable for recall for a given transition and a given bin, that bin will only be incremented once. If false, all possible transitions will add to the count.
- Returns:
actual (pandas.Series) – Count of actual lags that occurred in the recall sequence.
possible (pandas.Series) – Count of possible lags.
See also
count_lags
Count of individual transitions.
Examples
>>> from psifr import stats >>> pool_items = [[1, 2, 3]] >>> recall_items = [[3, 1, 2]] >>> actual, possible = stats.count_lags_compound(3, pool_items, recall_items) >>> (actual == possible).all() True >>> actual previous current -2 -2 0 -1 0 0 0 1 1 2 0 -1 -2 0 -1 0 0 0 1 0 2 0 0 -2 0 -1 0 0 0 1 0 2 0 1 -2 0 -1 0 0 0 1 0 2 0 2 -2 0 -1 0 0 0 1 0 2 0 dtype: int64