Criteria
- survivors.criteria.CRITERIA_DICT = {'logrank': <function logrank>, 'peto': <function peto>, 'tarone-ware': <function tarone_ware>, 'wilcoxon': <function wilcoxon>}
dict: Available criteria in library and its realization
- survivors.criteria.chi2_sf(x, df)
Probability value (1-tail) for the Chi^2 probability distribution.
Broadcasting rules apply.
Parameters
x : array_like or float > 0 df : array_like or float, probably int >= 1
Returns
- chisqprobndarray
The area from chisq to infinity under the Chi^2 probability distribution with degrees of freedom df.
- survivors.criteria.find_inverse_gamma(a, p, q)
In order to understand what’s going on here, you will need to refer to:
Computation of the Incomplete Gamma Function Ratios and their Inverse ARMIDO R. DIDONATO and ALFRED H. MORRIS, JR. ACM Transactions on Mathematical Software, Vol. 12, No. 4, December 1986, Pages 377-393.
- survivors.criteria.polyval(p, x)
Evaluate a polynomial by Horner’s scheme
- survivors.criteria.weight_lr_fast(dur_A, dur_B, cens_A=None, cens_B=None, weightings='')
Count weighted log-rank criteria
Parameters
- dur_Aarray-like
Time of occurred events from first sample.
- dur_Barray-like
Time of occurred events from second sample.
- cens_Aarray-like, optional
Indicate of occurred events from first sample. The default is None (all events occurred).
- cens_Barray-like, optional
Indicate of occurred events from second sample. The default is None (all events occurred).
- weightingsstr, optional
Weights of criteria. The default is “” (log-rank). Log-rank :math:’w = 1’ Wilcoxon :math:’w = N_j’ Tarone-ware :math:’w = sqrt(N_j)’ Peto-peto :math:’w = fraq{1 - O_j}{N_j + 1}’
Returns
- logrankfloat
Chi2 statistic value of weighted log-rank test