psifr.fr.lag_crp#
- psifr.fr.lag_crp(df, lag_key='input', count_unique=False, item_query=None, test_key=None, test=None)#
Lag-CRP for multiple subjects.
- Parameters:
df (pandas.DataFrame) – Merged study and recall data. See merge_lists. List length is assumed to be the same for all lists. Must have fields: subject, list, input, output, recalled. Input position must be defined such that the first serial position is 1, not 0.
lag_key (str, optional) – Name of column to use when calculating lag between recalled items. Default is to calculate lag based on input position.
count_unique (bool, optional) – If true, possible transitions of the same lag will only be incremented once per transition.
item_query (str, optional) – Query string to select items to include in the pool of possible recalls to be examined. See pandas.DataFrame.query for allowed format.
test_key (str, optional) – Name of column with labels to use when testing transitions for inclusion.
test (callable, optional) – Callable that takes in previous and current item values and returns True for transitions that should be included.
- Returns:
results – Has fields:
- subjecthashable
Results are separated by each subject.
- lagint
Lag of input position between two adjacent recalls.
- probfloat
Probability of each lag transition.
- actualint
Total of actual made transitions at each lag.
- possibleint
Total of times each lag was possible, given the prior input position and the remaining items to be recalled.
- Return type:
See also
lag_rankRank of the absolute lags in recall sequences.
Examples
>>> from psifr import fr >>> raw = fr.sample_data('Morton2013') >>> data = fr.merge_free_recall(raw) >>> fr.lag_crp(data) subject lag prob actual possible 0 1 -23.0 0.020833 1 48 1 1 -22.0 0.035714 3 84 2 1 -21.0 0.026316 3 114 3 1 -20.0 0.024000 3 125 4 1 -19.0 0.014388 2 139 ... ... ... ... ... ... 1875 47 19.0 0.061224 3 49 1876 47 20.0 0.055556 2 36 1877 47 21.0 0.045455 1 22 1878 47 22.0 0.071429 1 14 1879 47 23.0 0.000000 0 6 [1880 rows x 5 columns]