arkouda.scipy¶

Submodules¶

Classes¶

Power_divergenceResult

The results of a power divergence statistical test.

Functions¶

`chisquare`(f_obs[, f_exp, ddof])	Computes the chi square statistic and p-value.
`power_divergence`(f_obs[, f_exp, ddof, lambda_])	Computes the power divergence statistic and p-value.

Module Contents¶

class arkouda.scipy.Power_divergenceResult[source]¶

Bases: Power_divergenceResult

The results of a power divergence statistical test.

statistic¶

Type:: numpy.float64

pvalue¶

Type:: numpy.float64

arkouda.scipy.chisquare(f_obs, f_exp=None, ddof=0)[source]¶

Computes the chi square statistic and p-value.

Parameters:

f_obs (pdarray) – The observed frequency.
f_exp (pdarray, default = None) – The expected frequency.
ddof (int) – The delta degrees of freedom.

Return type:

arkouda.akstats.Power_divergenceResult

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda.stats import chisquare
>>> chisquare(ak.array([10, 20, 30, 10]), ak.array([10, 30, 20, 10]))
Power_divergenceResult(statistic=8.333333333333334, pvalue=0.03960235520756414)

See also

scipy.stats.chisquare, arkouda.akstats.power_divergence

References

[1] “Chi-squared test”, https://en.wikipedia.org/wiki/Chi-squared_test

[2] “scipy.stats.chisquare”, https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html

arkouda.scipy.power_divergence(f_obs, f_exp=None, ddof=0, lambda_=None)[source]¶

Computes the power divergence statistic and p-value.

Parameters:

f_obs (pdarray) – The observed frequency.
f_exp (pdarray, default = None) – The expected frequency.
ddof (int) – The delta degrees of freedom.
lambda (string, default = "pearson") –
The power in the Cressie-Read power divergence statistic. Allowed values: “pearson”, “log-likelihood”, “freeman-tukey”, “mod-log-likelihood”, “neyman”, “cressie-read”

Powers correspond as follows:

”pearson”: 1

”log-likelihood”: 0

”freeman-tukey”: -0.5

”mod-log-likelihood”: -1

”neyman”: -2

”cressie-read”: 2 / 3

Return type:

arkouda.akstats.Power_divergenceResult

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda.stats import power_divergence
>>> x = ak.array([10, 20, 30, 10])
>>> y = ak.array([10, 30, 20, 10])
>>> power_divergence(x, y, lambda_="pearson")
Power_divergenceResult(statistic=8.333333333333334, pvalue=0.03960235520756414)
>>> power_divergence(x, y, lambda_="log-likelihood")
Power_divergenceResult(statistic=8.109302162163285, pvalue=0.04380595350226197)