psifr.fr.category_crp#

psifr.fr.category_crp(df, category_key, item_query=None, test_key=None, test=None)#

Conditional response probability of within-category transitions.

Parameters:

  • df (pandas.DataFrame) – Merged study and recall data. See merge_lists. List length is assumed to be the same for all lists within each subject. Must have fields: subject, list, input, output, recalled.

  • category_key (str) – Name of column with category labels.

  • 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.

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:

pandas.DataFrame

Examples

>>> from psifr import fr
>>> raw = fr.sample_data('Morton2013')
>>> data = fr.merge_free_recall(raw, study_keys=['category'])
>>> cat_crp = fr.category_crp(data, 'category')
>>> cat_crp.head()
   subject      prob  actual  possible
0        1  0.801147     419       523
1        2  0.733456     399       544
2        3  0.763158     377       494
3        4  0.814882     449       551
4        5  0.877273     579       660