mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-11-10 06:54:44 +00:00
170 lines
6.5 KiB
Python
170 lines
6.5 KiB
Python
# Only included for backwards compatibility! Do not update, StatisticalTest is preferred and supported.
|
|
|
|
import scipy
|
|
from scipy import stats
|
|
|
|
class StatisticalTest:
|
|
|
|
def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'):
|
|
|
|
results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
|
|
return {"t-value": results[0], "p-value": results[1]}
|
|
|
|
def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'):
|
|
|
|
results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
|
|
return {"t-value": results[0], "p-value": results[1]}
|
|
|
|
def ttest_statistic(self, o1, o2, equal = True):
|
|
|
|
results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
|
|
return {"t-value": results[0], "p-value": results[1]}
|
|
|
|
def ttest_related(self, a, b, axis = 0, nan_policy='propagate'):
|
|
|
|
results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
|
|
return {"t-value": results[0], "p-value": results[1]}
|
|
|
|
def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
|
|
|
|
results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
|
|
return {"ks-value": results[0], "p-value": results[1]}
|
|
|
|
def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0):
|
|
|
|
results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
|
|
return {"chisquared-value": results[0], "p-value": results[1]}
|
|
|
|
def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
|
|
|
|
results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
|
|
return {"powerdivergence-value": results[0], "p-value": results[1]}
|
|
|
|
def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'):
|
|
|
|
results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
|
|
return {"ks-value": results[0], "p-value": results[1]}
|
|
|
|
def es_twosample(self, x, y, t = (0.4, 0.8)):
|
|
|
|
results = scipy.stats.epps_singleton_2samp(x, y, t = t)
|
|
return {"es-value": results[0], "p-value": results[1]}
|
|
|
|
def mw_rank(self, x, y, use_continuity = True, alternative = None):
|
|
|
|
results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
|
|
return {"u-value": results[0], "p-value": results[1]}
|
|
|
|
def mw_tiecorrection(self, rank_values):
|
|
|
|
results = scipy.stats.tiecorrect(rank_values)
|
|
return {"correction-factor": results}
|
|
|
|
def rankdata(self, a, method = 'average'):
|
|
|
|
results = scipy.stats.rankdata(a, method = method)
|
|
return results
|
|
|
|
def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
|
|
|
|
results = scipy.stats.ranksums(a, b)
|
|
return {"u-value": results[0], "p-value": results[1]}
|
|
|
|
def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
|
|
|
|
results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
|
|
return {"t-value": results[0], "p-value": results[1]}
|
|
|
|
def kw_htest(self, *args, nan_policy = 'propagate'):
|
|
|
|
results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
|
|
return {"h-value": results[0], "p-value": results[1]}
|
|
|
|
def friedman_chisquare(self, *args):
|
|
|
|
results = scipy.stats.friedmanchisquare(*args)
|
|
return {"chisquared-value": results[0], "p-value": results[1]}
|
|
|
|
def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
|
|
|
|
results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
|
|
return {"w-value": results[0], "p-value": results[1]}
|
|
|
|
def combine_pvalues(self, pvalues, method = 'fisher', weights = None):
|
|
|
|
results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
|
|
return {"combined-statistic": results[0], "p-value": results[1]}
|
|
|
|
def jb_fitness(self, x):
|
|
|
|
results = scipy.stats.jarque_bera(x)
|
|
return {"jb-value": results[0], "p-value": results[1]}
|
|
|
|
def ab_equality(self, x, y):
|
|
|
|
results = scipy.stats.ansari(x, y)
|
|
return {"ab-value": results[0], "p-value": results[1]}
|
|
|
|
def bartlett_variance(self, *args):
|
|
|
|
results = scipy.stats.bartlett(*args)
|
|
return {"t-value": results[0], "p-value": results[1]}
|
|
|
|
def levene_variance(self, *args, center = 'median', proportiontocut = 0.05):
|
|
|
|
results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
|
|
return {"w-value": results[0], "p-value": results[1]}
|
|
|
|
def sw_normality(self, x):
|
|
|
|
results = scipy.stats.shapiro(x)
|
|
return {"w-value": results[0], "p-value": results[1]}
|
|
|
|
def shapiro(self, x):
|
|
|
|
return "destroyed by facts and logic"
|
|
|
|
def ad_onesample(self, x, dist = 'norm'):
|
|
|
|
results = scipy.stats.anderson(x, dist = dist)
|
|
return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
|
|
|
|
def ad_ksample(self, samples, midrank = True):
|
|
|
|
results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
|
|
return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
|
|
|
|
def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'):
|
|
|
|
results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
|
|
return {"p-value": results}
|
|
|
|
def fk_variance(self, *args, center = 'median', proportiontocut = 0.05):
|
|
|
|
results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
|
|
return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
|
|
|
|
def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
|
|
|
|
results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
|
|
return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
|
|
|
|
def mood_equalscale(self, x, y, axis = 0):
|
|
|
|
results = scipy.stats.mood(x, y, axis = axis)
|
|
return {"z-score": results[0], "p-value": results[1]}
|
|
|
|
def skewtest(self, a, axis = 0, nan_policy = 'propogate'):
|
|
|
|
results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
|
|
return {"z-score": results[0], "p-value": results[1]}
|
|
|
|
def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'):
|
|
|
|
results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
|
|
return {"z-score": results[0], "p-value": results[1]}
|
|
|
|
def normaltest(self, a, axis = 0, nan_policy = 'propogate'):
|
|
|
|
results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
|
|
return {"z-score": results[0], "p-value": results[1]} |