Compute z-statistic (ZScoreStandard
)¶
-
class
cerebunit.statistics.stat_scores.zScore.
ZScoreStandard
(*args, **kwargs)¶ Compute t-statistic as the standardized statistic as
Definitions Interpretation sample_mean, \(\bar{x}\) observation[“mean”] null_value, \(\mu_0\) model prediction standard_deviation, sd observation[“standard_deviation”] z-statistic, z z = \(\frac{\bar{x} - \mu_0}{sd}\) Note: se = \(\frac{s}{\sqrt{n}}\), where n is the sample size and s is the standard deviation.
Use Case
x = ZScoreStandard.compute( observation, prediction ) score = ZScoreStandard(x)
Note: As part of the SciUnit framework this custom
ZScoreStandard
should have the following methods,compute()
(class method)sort_key()
(property)__str__()
-
classmethod
compute
(observation, prediction)¶ Note:
- observation (sample) is in dictionary form with keys mean and
- standard_error whose value has magnitude and python quantity
- the populations parameter is the predicted value