Compute z-statistic for Sign test (ZScoreForSignTest)

class cerebunit.statistics.stat_scores.zSignScore.ZScoreForSignTest(*args, **kwargs)

Compute z-statistic for Sign Test.

Definitions	Interpretation
\(\eta_0\)	some specified value \(^{\dagger}\)
\(S^{+}\)	number of values in sample \(> \eta_0\)
\(S^{-}\)	number of values in sample \(< \eta_0\)
\(n_u\)	number of values in sample \(\neq \eta_0\), i.e., \(S^{+} + S^{-}\)
z-statistic, z	z = \(\frac{ S^{+} - \frac{n_u}{2} }{ \sqrt{\frac{n_u}{4}} }\)

\(^{\dagger} \eta_0\), null value is

• the model prediction for one sample testing
• 0 for testing with paired data (observation - prediction)

Use Case:

x = ZScoreForSignTest.compute( observation, prediction )
score = ZScoreForSignTest(x)

Note: As part of the SciUnit framework this custom TScore should have the following methods,

• compute() (class method)
• sort_key() (property)
• __str__()

classmethod compute(observation, prediction)

Argument	Value type
first argument	dictionary; observation/experimental data
second argument	floating number or array

Note:

• observation must have the key “raw_data” whose value is the list of numbers