`scipy.stats._hypotests:_get_binomial_log_p_value_with_nuisance_param`

source: /scipy/stats/_hypotests.py :1469

Signature

   def     _get_binomial_log_p_value_with_nuisance_param (    nuisance_param    ,    x1_sum_x2    ,    x1_sum_x2_log_comb    ,    index_arr      )

Summary

Compute the log pvalue in respect of a nuisance parameter considering a 2x2 sample space.

Parameters

nuisance_param : float: nuisance parameter used in the computation of the maximisation of the p-value. Must be between 0 and 1
x1_sum_x2 : ndarray: Sum of x1 and x2 inside barnard_exact
x1_sum_x2_log_comb : ndarray: sum of the log combination of x1 and x2
index_arr : ndarray of boolean

Returns

p_value : float: Return the maximum p-value considering every nuisance parameter between 0 and 1

Notes

Both Barnard's test and Boschloo's test iterate over a nuisance parameter $π \in [0, 1]$ to find the maximum p-value. To search this maxima, this function return the negative log pvalue with respect to the nuisance parameter passed in params. This negative log p-value is then used in shgo to find the minimum negative pvalue which is our maximum pvalue.

Also, to compute the different combination used in the p-values' computation formula, this function uses gammaln which is more tolerant for large value than scipy.special.comb. gammaln gives a log combination. For the little precision loss, performances are improved a lot.

Aliases

scipy.stats._hypotests._get_binomial_log_p_value_with_nuisance_param