metadpy.sdt.rates#

metadpy.sdt.rates(data: None, hits: None, misses: None, fas: None, crs: None, stimuli: Union[List[Any], Any], responses: Union[List[Any], Any], correction: bool = True) → Tuple[float, float][source]#

metadpy.sdt.rates(data: DataFrame, hits: None, misses: None, fas: None, crs: None, stimuli: str, responses: str, correction: bool = True) → Tuple[float, float]

metadpy.sdt.rates(data: None, hits: int, misses: int, fas: int, crs: int, stimuli: None, responses: None, correction: bool = True) → Tuple[float, float]

Compute hit and false alarm rates.

The values are automatically corrected to avoid d’ infinity (see below).

Parameters

data

Dataframe containing one stimuli and one response column.

stimuli

If a string is provided, should be the name of the column used as stimuli. If a list or an array is provided, should contain the boolean vectors for stimuli. If None and data is a pandas.DataFrame, will be set to Stimuli by default.

responses

If a string is provided, should be the name of the column used as responses. If a list or an array is provided, should contain the boolean vector for responses. If None and data is a pandas.DataFrame, will be set to Responses by default.

hits

Hits.

misses

Misses.

fas

False alarms.

crs

Correct rejections.

correction

Avoid d’ infinity by correcting false alarm and hit rate values if equal to 0 or 1 using half inverse or 1 - half inverse. Half inverses values are defined by:

half_hit = 0.5 / (hits + misses) half_fa = 0.5 / (fas + crs)

Default is set to True (use correction).

Returns

hit_rate:: Hit rate.
fa_rate: False alarm rate.

Raises

ValueError:: If no valid data are provided.