# HG changeset patch # User bgruening # Date 1669054107 0 # Node ID 178b22349b79f1511318011c02c58109b053b77b planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/statistics commit 7c5002672919ca1e5eacacb835a4ce66ffa19656 diff -r 000000000000 -r 178b22349b79 macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,224 @@ + + + + numpy + scipy + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff -r 000000000000 -r 178b22349b79 readme.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/readme.rst Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,55 @@ +Galaxy wrapper for statistical hypothesis testing with scipy +============================================================ + +Computes a large number of probability distributions as well as a statistical functions of any kind. +For more informations have a look at the `SciPy site`_. + +.. _`SciPy site`: http://docs.scipy.org/doc/scipy/reference/stats.html + + +============ +Installation +============ + +Should be done via the Galaxy `Tool Shed`_. +Install the following repository: https://toolshed.g2.bx.psu.edu/view/bgruening/statistical_hypothesis_testing + +.. _`Tool Shed`: http://wiki.galaxyproject.org/Tool%20Shed + + +======= +History +======= + + - v0.1: no release yet + - v0.2: add a lot more statistics + + + + +Wrapper Licence (MIT/BSD style) +=============================== + +Copyright (c) 2013-2015 + + * Björn Gruening (bjoern dot gruening gmail dot com) + * Hui Li (lihui900116 gmail dot com) + +Permission to use, copy, modify, and distribute this software and its +documentation with or without modifications and for any purpose and +without fee is hereby granted, provided that any copyright notices +appear in all copies and that both those copyright notices and this +permission notice appear in supporting documentation, and that the +names of the contributors or copyright holders not be used in +advertising or publicity pertaining to distribution of the software +without specific prior permission. + +THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DISCLAIM ALL +WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL THE +CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY SPECIAL, INDIRECT +OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS +OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE +OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE +OR PERFORMANCE OF THIS SOFTWARE. + diff -r 000000000000 -r 178b22349b79 statistical_hypothesis_testing.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/statistical_hypothesis_testing.py Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,774 @@ +#!/usr/bin/env python + +""" + +""" +import argparse + +import numpy as np +from scipy import stats + + +def columns_to_values(args, line): + # here you go over every list + samples = [] + for i in args: + cols = line.split("\t") + sample_list = [] + for row in i: + sample_list.append(cols[row - 1]) + samples.append(list(map(int, sample_list))) + return samples + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("-i", "--infile", required=True, help="Tabular file.") + parser.add_argument( + "-o", "--outfile", required=True, help="Path to the output file." + ) + parser.add_argument("--sample_one_cols", help="Input format, like smi, sdf, inchi") + parser.add_argument("--sample_two_cols", help="Input format, like smi, sdf, inchi") + parser.add_argument( + "--sample_cols", + help="Input format, like smi, sdf, inchi,separate arrays using ;", + ) + parser.add_argument("--test_id", help="statistical test method") + parser.add_argument( + "--mwu_use_continuity", + action="store_true", + default=False, + help="Whether a continuity correction (1/2.) should be taken into account.", + ) + parser.add_argument( + "--equal_var", + action="store_true", + default=False, + help="If set perform a standard independent 2 sample test that assumes equal population variances. If not set, perform Welch's t-test, which does not assume equal population variance.", + ) + parser.add_argument( + "--reta", + action="store_true", + default=False, + help="Whether or not to return the internally computed a values.", + ) + parser.add_argument( + "--fisher", + action="store_true", + default=False, + help="if true then Fisher definition is used", + ) + parser.add_argument( + "--bias", + action="store_true", + default=False, + help="if false,then the calculations are corrected for statistical bias", + ) + parser.add_argument( + "--inclusive1", + action="store_true", + default=False, + help="if false,lower_limit will be ignored", + ) + parser.add_argument( + "--inclusive2", + action="store_true", + default=False, + help="if false,higher_limit will be ignored", + ) + parser.add_argument( + "--inclusive", + action="store_true", + default=False, + help="if false,limit will be ignored", + ) + parser.add_argument( + "--printextras", + action="store_true", + default=False, + help="If True, if there are extra points a warning is raised saying how many of those points there are", + ) + parser.add_argument( + "--initial_lexsort", + action="store_true", + default="False", + help="Whether to use lexsort or quicksort as the sorting method for the initial sort of the inputs.", + ) + parser.add_argument( + "--correction", + action="store_true", + default=False, + help="continuity correction ", + ) + parser.add_argument( + "--axis", + type=int, + default=0, + help="Axis can equal None (ravel array first), or an integer (the axis over which to operate on a and b)", + ) + parser.add_argument( + "--n", + type=int, + default=0, + help="the number of trials. This is ignored if x gives both the number of successes and failures", + ) + parser.add_argument( + "--b", type=int, default=0, help="The number of bins to use for the histogram" + ) + parser.add_argument( + "--N", type=int, default=0, help="Score that is compared to the elements in a." + ) + parser.add_argument( + "--ddof", type=int, default=0, help="Degrees of freedom correction" + ) + parser.add_argument( + "--score", + type=int, + default=0, + help="Score that is compared to the elements in a.", + ) + parser.add_argument("--m", type=float, default=0.0, help="limits") + parser.add_argument("--mf", type=float, default=2.0, help="lower limit") + parser.add_argument("--nf", type=float, default=99.9, help="higher_limit") + parser.add_argument( + "--p", + type=float, + default=0.5, + help="The hypothesized probability of success. 0 <= p <= 1. The default value is p = 0.5", + ) + parser.add_argument("--alpha", type=float, default=0.9, help="probability") + parser.add_argument( + "--new", + type=float, + default=0.0, + help="Value to put in place of values in a outside of bounds", + ) + parser.add_argument( + "--proportiontocut", + type=float, + default=0.0, + help="Proportion (in range 0-1) of total data set to trim of each end.", + ) + parser.add_argument( + "--lambda_", + type=float, + default=1.0, + help="lambda_ gives the power in the Cressie-Read power divergence statistic", + ) + parser.add_argument( + "--imbda", + type=float, + default=0, + help="If lmbda is not None, do the transformation for that value.If lmbda is None, find the lambda that maximizes the log-likelihood function and return it as the second output argument.", + ) + parser.add_argument( + "--base", + type=float, + default=1.6, + help="The logarithmic base to use, defaults to e", + ) + parser.add_argument("--dtype", help="dtype") + parser.add_argument("--med", help="med") + parser.add_argument("--cdf", help="cdf") + parser.add_argument("--zero_method", help="zero_method options") + parser.add_argument("--dist", help="dist options") + parser.add_argument("--ties", help="ties options") + parser.add_argument("--alternative", help="alternative options") + parser.add_argument("--mode", help="mode options") + parser.add_argument("--method", help="method options") + parser.add_argument("--md", help="md options") + parser.add_argument("--center", help="center options") + parser.add_argument("--kind", help="kind options") + parser.add_argument("--tail", help="tail options") + parser.add_argument("--interpolation", help="interpolation options") + parser.add_argument("--statistic", help="statistic options") + + args = parser.parse_args() + infile = args.infile + outfile = open(args.outfile, "w+") + test_id = args.test_id + nf = args.nf + mf = args.mf + imbda = args.imbda + inclusive1 = args.inclusive1 + inclusive2 = args.inclusive2 + sample0 = 0 + sample1 = 0 + sample2 = 0 + if args.sample_cols is not None: + sample0 = 1 + barlett_samples = [] + for sample in args.sample_cols.split(";"): + barlett_samples.append(list(map(int, sample.split(",")))) + if args.sample_one_cols is not None: + sample1 = 1 + sample_one_cols = args.sample_one_cols.split(",") + if args.sample_two_cols is not None: + sample_two_cols = args.sample_two_cols.split(",") + sample2 = 1 + for line in open(infile): + sample_one = [] + sample_two = [] + cols = line.strip().split("\t") + if sample0 == 1: + b_samples = columns_to_values(barlett_samples, line) + if sample1 == 1: + for index in sample_one_cols: + sample_one.append(cols[int(index) - 1]) + if sample2 == 1: + for index in sample_two_cols: + sample_two.append(cols[int(index) - 1]) + if test_id.strip() == "describe": + size, min_max, mean, uv, bs, bk = stats.describe( + list(map(float, sample_one)) + ) + cols.append(size) + cols.append(min_max) + cols.append(mean) + cols.append(uv) + cols.append(bs) + cols.append(bk) + elif test_id.strip() == "mode": + vals, counts = stats.mode(list(map(float, sample_one))) + cols.append(vals) + cols.append(counts) + elif test_id.strip() == "nanmean": + m = stats.nanmean(list(map(float, sample_one))) + cols.append(m) + elif test_id.strip() == "nanmedian": + m = stats.nanmedian(list(map(float, sample_one))) + cols.append(m) + elif test_id.strip() == "kurtosistest": + z_value, p_value = stats.kurtosistest(list(map(float, sample_one))) + cols.append(z_value) + cols.append(p_value) + elif test_id.strip() == "variation": + ra = stats.variation(list(map(float, sample_one))) + cols.append(ra) + elif test_id.strip() == "itemfreq": + freq = np.unique(list(map(float, sample_one)), return_counts=True) + for i in freq: + elements = ",".join(list(map(str, i))) + cols.append(elements) + elif test_id.strip() == "nanmedian": + m = stats.nanmedian(list(map(float, sample_one))) + cols.append(m) + elif test_id.strip() == "variation": + ra = stats.variation(list(map(float, sample_one))) + cols.append(ra) + elif test_id.strip() == "boxcox_llf": + IIf = stats.boxcox_llf(imbda, list(map(float, sample_one))) + cols.append(IIf) + elif test_id.strip() == "tiecorrect": + fa = stats.tiecorrect(list(map(float, sample_one))) + cols.append(fa) + elif test_id.strip() == "rankdata": + r = stats.rankdata(list(map(float, sample_one)), method=args.md) + cols.append(r) + elif test_id.strip() == "nanstd": + s = stats.nanstd(list(map(float, sample_one)), bias=args.bias) + cols.append(s) + elif test_id.strip() == "anderson": + A2, critical, sig = stats.anderson( + list(map(float, sample_one)), dist=args.dist + ) + cols.append(A2) + for i in critical: + cols.append(i) + cols.append(",") + for i in sig: + cols.append(i) + elif test_id.strip() == "binom_test": + p_value = stats.binom_test(list(map(float, sample_one)), n=args.n, p=args.p) + cols.append(p_value) + elif test_id.strip() == "gmean": + gm = stats.gmean(list(map(float, sample_one)), dtype=args.dtype) + cols.append(gm) + elif test_id.strip() == "hmean": + hm = stats.hmean(list(map(float, sample_one)), dtype=args.dtype) + cols.append(hm) + elif test_id.strip() == "kurtosis": + k = stats.kurtosis( + list(map(float, sample_one)), + axis=args.axis, + fisher=args.fisher, + bias=args.bias, + ) + cols.append(k) + elif test_id.strip() == "moment": + n_moment = stats.moment(list(map(float, sample_one)), n=args.n) + cols.append(n_moment) + elif test_id.strip() == "normaltest": + k2, p_value = stats.normaltest(list(map(float, sample_one))) + cols.append(k2) + cols.append(p_value) + elif test_id.strip() == "skew": + skewness = stats.skew(list(map(float, sample_one)), bias=args.bias) + cols.append(skewness) + elif test_id.strip() == "skewtest": + z_value, p_value = stats.skewtest(list(map(float, sample_one))) + cols.append(z_value) + cols.append(p_value) + elif test_id.strip() == "sem": + s = stats.sem(list(map(float, sample_one)), ddof=args.ddof) + cols.append(s) + elif test_id.strip() == "zscore": + z = stats.zscore(list(map(float, sample_one)), ddof=args.ddof) + for i in z: + cols.append(i) + elif test_id.strip() == "signaltonoise": + s2n = stats.signaltonoise(list(map(float, sample_one)), ddof=args.ddof) + cols.append(s2n) + elif test_id.strip() == "percentileofscore": + p = stats.percentileofscore( + list(map(float, sample_one)), score=args.score, kind=args.kind + ) + cols.append(p) + elif test_id.strip() == "bayes_mvs": + c_mean, c_var, c_std = stats.bayes_mvs( + list(map(float, sample_one)), alpha=args.alpha + ) + cols.append(c_mean) + cols.append(c_var) + cols.append(c_std) + elif test_id.strip() == "sigmaclip": + c, c_low, c_up = stats.sigmaclip( + list(map(float, sample_one)), low=args.m, high=args.n + ) + cols.append(c) + cols.append(c_low) + cols.append(c_up) + elif test_id.strip() == "kstest": + d, p_value = stats.kstest( + list(map(float, sample_one)), + cdf=args.cdf, + N=args.N, + alternative=args.alternative, + mode=args.mode, + ) + cols.append(d) + cols.append(p_value) + elif test_id.strip() == "chi2_contingency": + chi2, p, dof, ex = stats.chi2_contingency( + list(map(float, sample_one)), + correction=args.correction, + lambda_=args.lambda_, + ) + cols.append(chi2) + cols.append(p) + cols.append(dof) + cols.append(ex) + elif test_id.strip() == "tmean": + if nf == 0 and mf == 0: + mean = stats.tmean(list(map(float, sample_one))) + else: + mean = stats.tmean( + list(map(float, sample_one)), (mf, nf), (inclusive1, inclusive2) + ) + cols.append(mean) + elif test_id.strip() == "tmin": + if mf == 0: + min = stats.tmin(list(map(float, sample_one))) + else: + min = stats.tmin( + list(map(float, sample_one)), + lowerlimit=mf, + inclusive=args.inclusive, + ) + cols.append(min) + elif test_id.strip() == "tmax": + if nf == 0: + max = stats.tmax(list(map(float, sample_one))) + else: + max = stats.tmax( + list(map(float, sample_one)), + upperlimit=nf, + inclusive=args.inclusive, + ) + cols.append(max) + elif test_id.strip() == "tvar": + if nf == 0 and mf == 0: + var = stats.tvar(list(map(float, sample_one))) + else: + var = stats.tvar( + list(map(float, sample_one)), (mf, nf), (inclusive1, inclusive2) + ) + cols.append(var) + elif test_id.strip() == "tstd": + if nf == 0 and mf == 0: + std = stats.tstd(list(map(float, sample_one))) + else: + std = stats.tstd( + list(map(float, sample_one)), (mf, nf), (inclusive1, inclusive2) + ) + cols.append(std) + elif test_id.strip() == "tsem": + if nf == 0 and mf == 0: + s = stats.tsem(list(map(float, sample_one))) + else: + s = stats.tsem( + list(map(float, sample_one)), (mf, nf), (inclusive1, inclusive2) + ) + cols.append(s) + elif test_id.strip() == "scoreatpercentile": + if nf == 0 and mf == 0: + s = stats.scoreatpercentile( + list(map(float, sample_one)), + list(map(float, sample_two)), + interpolation_method=args.interpolation, + ) + else: + s = stats.scoreatpercentile( + list(map(float, sample_one)), + list(map(float, sample_two)), + (mf, nf), + interpolation_method=args.interpolation, + ) + for i in s: + cols.append(i) + elif test_id.strip() == "relfreq": + if nf == 0 and mf == 0: + rel, low_range, binsize, ex = stats.relfreq( + list(map(float, sample_one)), args.b + ) + else: + rel, low_range, binsize, ex = stats.relfreq( + list(map(float, sample_one)), args.b, (mf, nf) + ) + for i in rel: + cols.append(i) + cols.append(low_range) + cols.append(binsize) + cols.append(ex) + elif test_id.strip() == "binned_statistic": + if nf == 0 and mf == 0: + st, b_edge, b_n = stats.binned_statistic( + list(map(float, sample_one)), + list(map(float, sample_two)), + statistic=args.statistic, + bins=args.b, + ) + else: + st, b_edge, b_n = stats.binned_statistic( + list(map(float, sample_one)), + list(map(float, sample_two)), + statistic=args.statistic, + bins=args.b, + range=(mf, nf), + ) + cols.append(st) + cols.append(b_edge) + cols.append(b_n) + elif test_id.strip() == "threshold": + if nf == 0 and mf == 0: + o = stats.threshold(list(map(float, sample_one)), newval=args.new) + else: + o = stats.threshold( + list(map(float, sample_one)), mf, nf, newval=args.new + ) + for i in o: + cols.append(i) + elif test_id.strip() == "trimboth": + o = stats.trimboth( + list(map(float, sample_one)), proportiontocut=args.proportiontocut + ) + for i in o: + cols.append(i) + elif test_id.strip() == "trim1": + t1 = stats.trim1( + list(map(float, sample_one)), + proportiontocut=args.proportiontocut, + tail=args.tail, + ) + for i in t1: + cols.append(i) + elif test_id.strip() == "histogram": + if nf == 0 and mf == 0: + hi, low_range, binsize, ex = stats.histogram( + list(map(float, sample_one)), args.b + ) + else: + hi, low_range, binsize, ex = stats.histogram( + list(map(float, sample_one)), args.b, (mf, nf) + ) + cols.append(hi) + cols.append(low_range) + cols.append(binsize) + cols.append(ex) + elif test_id.strip() == "cumfreq": + if nf == 0 and mf == 0: + cum, low_range, binsize, ex = stats.cumfreq( + list(map(float, sample_one)), args.b + ) + else: + cum, low_range, binsize, ex = stats.cumfreq( + list(map(float, sample_one)), args.b, (mf, nf) + ) + cols.append(cum) + cols.append(low_range) + cols.append(binsize) + cols.append(ex) + elif test_id.strip() == "boxcox_normmax": + if nf == 0 and mf == 0: + ma = stats.boxcox_normmax(list(map(float, sample_one))) + else: + ma = stats.boxcox_normmax( + list(map(float, sample_one)), (mf, nf), method=args.method + ) + cols.append(ma) + elif test_id.strip() == "boxcox": + if imbda == 0: + box, ma, ci = stats.boxcox( + list(map(float, sample_one)), alpha=args.alpha + ) + cols.append(box) + cols.append(ma) + cols.append(ci) + else: + box = stats.boxcox( + list(map(float, sample_one)), imbda, alpha=args.alpha + ) + cols.append(box) + elif test_id.strip() == "histogram2": + h2 = stats.histogram2( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + for i in h2: + cols.append(i) + elif test_id.strip() == "ranksums": + z_statistic, p_value = stats.ranksums( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + cols.append(z_statistic) + cols.append(p_value) + elif test_id.strip() == "ttest_1samp": + t, prob = stats.ttest_1samp(map(float, sample_one), map(float, sample_two)) + for i in t: + cols.append(i) + for i in prob: + cols.append(i) + elif test_id.strip() == "ansari": + AB, p_value = stats.ansari( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + cols.append(AB) + cols.append(p_value) + elif test_id.strip() == "linregress": + slope, intercept, r_value, p_value, stderr = stats.linregress( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + cols.append(slope) + cols.append(intercept) + cols.append(r_value) + cols.append(p_value) + cols.append(stderr) + elif test_id.strip() == "pearsonr": + cor, p_value = stats.pearsonr( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + cols.append(cor) + cols.append(p_value) + elif test_id.strip() == "pointbiserialr": + r, p_value = stats.pointbiserialr( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + cols.append(r) + cols.append(p_value) + elif test_id.strip() == "ks_2samp": + d, p_value = stats.ks_2samp( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + cols.append(d) + cols.append(p_value) + elif test_id.strip() == "mannwhitneyu": + mw_stats_u, p_value = stats.mannwhitneyu( + list(map(float, sample_one)), + list(map(float, sample_two)), + use_continuity=args.mwu_use_continuity, + ) + cols.append(mw_stats_u) + cols.append(p_value) + elif test_id.strip() == "zmap": + z = stats.zmap( + list(map(float, sample_one)), + list(map(float, sample_two)), + ddof=args.ddof, + ) + for i in z: + cols.append(i) + elif test_id.strip() == "ttest_ind": + mw_stats_u, p_value = stats.ttest_ind( + list(map(float, sample_one)), + list(map(float, sample_two)), + equal_var=args.equal_var, + ) + cols.append(mw_stats_u) + cols.append(p_value) + elif test_id.strip() == "ttest_rel": + t, prob = stats.ttest_rel( + list(map(float, sample_one)), + list(map(float, sample_two)), + axis=args.axis, + ) + cols.append(t) + cols.append(prob) + elif test_id.strip() == "mood": + z, p_value = stats.mood( + list(map(float, sample_one)), + list(map(float, sample_two)), + axis=args.axis, + ) + cols.append(z) + cols.append(p_value) + elif test_id.strip() == "shapiro": + W, p_value = stats.shapiro(list(map(float, sample_one))) + cols.append(W) + cols.append(p_value) + elif test_id.strip() == "kendalltau": + k, p_value = stats.kendalltau( + list(map(float, sample_one)), + list(map(float, sample_two)), + initial_lexsort=args.initial_lexsort, + ) + cols.append(k) + cols.append(p_value) + elif test_id.strip() == "entropy": + s = stats.entropy( + list(map(float, sample_one)), + list(map(float, sample_two)), + base=args.base, + ) + cols.append(s) + elif test_id.strip() == "spearmanr": + if sample2 == 1: + rho, p_value = stats.spearmanr( + list(map(float, sample_one)), list(map(float, sample_two)) + ) + else: + rho, p_value = stats.spearmanr(list(map(float, sample_one))) + cols.append(rho) + cols.append(p_value) + elif test_id.strip() == "wilcoxon": + if sample2 == 1: + T, p_value = stats.wilcoxon( + list(map(float, sample_one)), + list(map(float, sample_two)), + zero_method=args.zero_method, + correction=args.correction, + ) + else: + T, p_value = stats.wilcoxon( + list(map(float, sample_one)), + zero_method=args.zero_method, + correction=args.correction, + ) + cols.append(T) + cols.append(p_value) + elif test_id.strip() == "chisquare": + if sample2 == 1: + rho, p_value = stats.chisquare( + list(map(float, sample_one)), + list(map(float, sample_two)), + ddof=args.ddof, + ) + else: + rho, p_value = stats.chisquare( + list(map(float, sample_one)), ddof=args.ddof + ) + cols.append(rho) + cols.append(p_value) + elif test_id.strip() == "power_divergence": + if sample2 == 1: + stat, p_value = stats.power_divergence( + list(map(float, sample_one)), + list(map(float, sample_two)), + ddof=args.ddof, + lambda_=args.lambda_, + ) + else: + stat, p_value = stats.power_divergence( + list(map(float, sample_one)), ddof=args.ddof, lambda_=args.lambda_ + ) + cols.append(stat) + cols.append(p_value) + elif test_id.strip() == "theilslopes": + if sample2 == 1: + mpe, met, lo, up = stats.theilslopes( + list(map(float, sample_one)), + list(map(float, sample_two)), + alpha=args.alpha, + ) + else: + mpe, met, lo, up = stats.theilslopes( + list(map(float, sample_one)), alpha=args.alpha + ) + cols.append(mpe) + cols.append(met) + cols.append(lo) + cols.append(up) + elif test_id.strip() == "combine_pvalues": + if sample2 == 1: + stat, p_value = stats.combine_pvalues( + list(map(float, sample_one)), + method=args.med, + weights=list(map(float, sample_two)), + ) + else: + stat, p_value = stats.combine_pvalues( + list(map(float, sample_one)), method=args.med + ) + cols.append(stat) + cols.append(p_value) + elif test_id.strip() == "obrientransform": + ob = stats.obrientransform(*b_samples) + for i in ob: + elements = ",".join(list(map(str, i))) + cols.append(elements) + elif test_id.strip() == "f_oneway": + f_value, p_value = stats.f_oneway(*b_samples) + cols.append(f_value) + cols.append(p_value) + elif test_id.strip() == "kruskal": + h, p_value = stats.kruskal(*b_samples) + cols.append(h) + cols.append(p_value) + elif test_id.strip() == "friedmanchisquare": + fr, p_value = stats.friedmanchisquare(*b_samples) + cols.append(fr) + cols.append(p_value) + elif test_id.strip() == "fligner": + xsq, p_value = stats.fligner( + center=args.center, proportiontocut=args.proportiontocut, *b_samples + ) + cols.append(xsq) + cols.append(p_value) + elif test_id.strip() == "bartlett": + T, p_value = stats.bartlett(*b_samples) + cols.append(T) + cols.append(p_value) + elif test_id.strip() == "levene": + w, p_value = stats.levene( + center=args.center, proportiontocut=args.proportiontocut, *b_samples + ) + cols.append(w) + cols.append(p_value) + elif test_id.strip() == "median_test": + stat, p_value, m, table = stats.median_test( + ties=args.ties, + correction=args.correction, + lambda_=args.lambda_, + *b_samples + ) + cols.append(stat) + cols.append(p_value) + cols.append(m) + cols.append(table) + for i in table: + elements = ",".join(list(map(str, i))) + cols.append(elements) + outfile.write("%s\n" % "\t".join(list(map(str, cols)))) + outfile.close() + + +if __name__ == "__main__": + main() diff -r 000000000000 -r 178b22349b79 statistical_hypothesis_testing.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/statistical_hypothesis_testing.xml Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,2648 @@ + + computes several descriptive statistics + + macros.xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +.. class:: warningmark + + +Computes a large number of probability distributions as well as a statistical functions of any kind. +For more informations have a look at the `SciPy site`_. + +.. _`SciPy site`: http://docs.scipy.org/doc/scipy/reference/stats.html + + +----- + +======== +Describe +======== + +Computes several descriptive statistics for samples x + +----- + +**The output are:** + +size of the data : int + + length of data along axis + +(min, max): tuple of ndarrays or floats + + minimum and maximum value of data array + +arithmetic mean : ndarray or float + + mean of data along axis + +unbiased variance : ndarray or float + + variance of the data along axis, denominator is number of observations minus one. + +biased skewness : ndarray or float + + skewness, based on moment calculations with denominator equal to the number of observations, i.e. no degrees of freedom correction + +biased kurtosis : ndarray or float + + kurtosis (Fisher), the kurtosis is normalized so that it is zero for the normal distribution. No degrees of freedom or bias correction is used. + +**example**: + +describe([4,417,8,3]) the result is (4,(3.0, 417.0),108.0,42440.6666667 ,1.15432044278, -0.666961688151) + + +===== +Gmean +===== + +Compute the geometric mean along the specified axis. + +Returns the geometric average of the array elements. That is: n-th root of (x1 * x2 * ... * xn) + +----- + +**The output are:** + +gmean : ndarray + + see dtype parameter above + +**example**: + +stats.gmean([4,17,8,3],dtype='float64') the result is (6.35594365562) + +===== +Hmean +===== + +py.stats.hmean(a, axis=0, dtype=None)[source] +Calculates the harmonic mean along the specified axis. + +That is: n / (1/x1 + 1/x2 + ... + 1/xn) + +**The output are:** + +hmean : ndarray + + see dtype parameter above + + +**example**: + +stats.hmean([4,17,8,3],dtype='float64')the result is (5.21405750799) + +======== +Kurtosis +======== + +Computes the kurtosis (Fisher or Pearson) of a dataset. + +Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution. + +If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators + +----- + +Computes the kurtosis for samples x . + +**The output are:** + +kurtosis : array + + The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s definition and 0 for Pearson’s definition. + +**example**: + +kurtosis([4,417,8,3],0,true,true) the result is (-0.666961688151) + +============= +Kurtosis Test +============= + +Tests whether a dataset has normal kurtosis + +This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution: kurtosis = 3(n-1)/(n+1). + +----- + +Computes the Z-value and p-value about samples x. + +kurtosistest only valid for n>=20. + +**The output are:** + +z-score : float + + The computed z-score for this test + +p-value : float + + The 2-sided p-value for the hypothesis test + + +**example**: + +kurtosistest([4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3]) the result is (0.29775013081425117, 0.7658938788569033) + +==== +Mode +==== + +Returns an array of the modal value in the passed array. + +If there is more than one such value, only the first is returned. The bin-count for the modal bins is also returned. + +----- + +Computes the most common value for samples x . + +**The output are:** + +vals : ndarray + + Array of modal values. + +counts : ndarray + + Array of counts for each mode. + + +**example**: + +mode([4,417,8,3]) the result is ([ 3.], [ 1.]) + +====== +Moment +====== + +Calculates the nth moment about the mean for a sample. + +Generally used to calculate coefficients of skewness and kurtosis. + +----- + +Computes the nth moment about the mean for samples x . + +**The output are:** + +n-th central moment : ndarray or float + + The appropriate moment along the given axis or over all values if axis is None. The denominator for the moment calculation is the number of observations, no degrees of freedom correction is done. + + +**example**: + +mode([4,417,8,3],moment=2) the result is (31830.5) + + +=========== +Normal Test +=========== + +Tests whether a sample differs from a normal distribution. + +This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D’Agostino and Pearson’s test that combines skew and kurtosis to produce an omnibus test of normality. + +----- + +Computes the k2 and p-value for samples x. + +skewtest is not valid with less than 8 samples.kurtosistest only valid for n>=20. + +**The output are:** + +k2 : float or array + + s^2 + k^2, where s is the z-score returned by skewtest and k is the z-score returned by kurtosistest. + +p-value : float or array + + A 2-sided chi squared probability for the hypothesis test. + + +**example**: + +normaltest([4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3]) the result is (5.8877986151838, 0.052659990380181286) + +==== +Skew +==== + +Computes the skewness of a data set. + +For normally distributed data, the skewness should be about 0. A skewness value > 0 means that there is more weight in the left tail of the distribution. The function skewtest can be used to determine if the skewness value is close enough to 0, statistically speaking. + +----- + +Computes the skewness from samples x. + + +**The output are:** + +skewness : ndarray + + The skewness of values along an axis, returning 0 where all values are equal. + + +**example**: + +kurtosistest([4,417,8,3]) the result is (1.1543204427775307) + + +========= +Skew Test +========= + +Tests whether the skew is different from the normal distribution. + +This function tests the null hypothesis that the skewness of the population that the sample was drawn from is the same as that of a corresponding normal distribution. + +----- + +Computes the z-value and p-value from samples x. + +skewtest is not valid with less than 8 samples + +**The output are:** + +z-score : float + + The computed z-score for this test. + +p-value : float + + a 2-sided p-value for the hypothesis test + +**example**: + +skewtest([4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3]) the result is (2.40814108282,0.0160339834731) + +====== +tmean +====== + +Compute the trimmed mean. + +This function finds the arithmetic mean of given values, ignoring values outside the given limits. + +----- + +Computes the mean of samples x,considering the lower and higher limits. + +Values in the input array less than the lower limit or greater than the upper limit will be ignored + +for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True) + +**The output are:** + +tmean : float + + The computed mean for this test. + + +**example**: + +tmean([4,17,8,3],(0,20),(true,true)) the result is (8.0) + +===== +tvar +===== + +Compute the trimmed variance + +This function computes the sample variance of an array of values, while ignoring values which are outside of given limits + +----- + +Computes the variance of samples x,considering the lower and higher limits. + +Values in the input array less than the lower limit or greater than the upper limit will be ignored + +for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True) + +**The output are:** + +tvar : float + + The computed variance for this test. + + +**example**: + +tvar([4,17,8,3],(0,99999),(true,true)) the result is (40.6666666667) + +===== +tmin +===== + +Compute the trimmed minimum. + +This function finds the arithmetic minimum of given values, ignoring values outside the given limits. + +----- + +Compute the trimmed minimum + +This function finds the miminum value of an array a along the specified axis, but only considering values greater than a specified lower limit. + +**The output are:** + +tmin : float + + The computed min for this test. + + +**example**: + +stats.tmin([4,17,8,3],2,0,'true') the result is (3.0) + +============ +tmax +============ + +Compute the trimmed maximum. + +This function finds the arithmetic maximum of given values, ignoring values outside the given limits. + +This function computes the maximum value of an array along a given axis, while ignoring values larger than a specified upper limit. + +**The output are:** + +tmax : float + + The computed max for this test. + + +**example**: + +stats.tmax([4,17,8,3],50,0,'true') the result is (17.0) + +============ +tstd +============ + +Compute the trimmed sample standard deviation + +This function finds the sample standard deviation of given values, ignoring values outside the given limits. + +----- + +Computes the deviation of samples x,considering the lower and higher limits. + +Values in the input array less than the lower limit or greater than the upper limit will be ignored + +for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True) + +**The output are:** + +tstd : float + + The computed deviation for this test. + + +**example**: + +tstd([4,17,8,3],(0,99999),(true,true)) the result is (6.37704215657) + + +============ +tsem +============ + +Compute the trimmed standard error of the mean. + +This function finds the standard error of the mean for given values, ignoring values outside the given limits. + +----- + +Computes the standard error of mean for samples x,considering the lower and higher limits. + +Values in the input array less than the lower limit or greater than the upper limit will be ignored + +for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True) + +**The output are:** + +tsem : float + + The computed the standard error of mean for this test. + + +**example**: + +tsem([4,17,8,3],(0,99999),(true,true)) the result is (3.18852107828) + +======== +nanmean +======== + +Compute the mean over the given axis ignoring nans + +----- + +Computes the mean for samples x without considering nans + +**The output are:** + +m : float + + The computed the mean for this test. + + +**example**: + +tsem([4,17,8,3]) the result is (8.0) + +======= +nanstd +======= + +Compute the standard deviation over the given axis, ignoring nans. + +----- + +Computes the deviation for samples x without considering nans + +**The output are:** + +s : float + + The computed the standard deviation for this test. + + +**example**: + +nanstd([4,17,8,3],0,'false') the result is (5.52268050859) + + +============ +nanmedian +============ + +Computes the median for samples x without considering nans + +**The output are:** + +m : float + + The computed the median for this test. + + +**example**: + +nanmedian([4,17,8,3]) the result is (6.0) + + +============ +variation +============ + +Computes the coefficient of variation, the ratio of the biased standard deviation to the mean for samples x + +**The output are:** + +ratio: float + + The ratio of the biased standard deviation to the mean for this test. + + +**example**: + +variation([4,17,8,3]) the result is (0.690335063574) + +============ +cumfreq +============ + +Returns a cumulative frequency histogram, using the histogram function. + +**The output are:** + +cumfreq : ndarray + + Binned values of cumulative frequency. + +lowerreallimit : float + + Lower real limit + +binsize : float + + Width of each bin. + +extrapoints : int + + Extra points. + + +**example**: + +cumfreq([4,17,8,3],defaultreallimits=(2.0,3.5)) the result is ([ 0. 0. 0. 0. 0. 0. 1. 1. 1. 1.],2.0,0.15,3) + +========== +histogram2 +========== + +Compute histogram using divisions in bins. + +Count the number of times values from array a fall into numerical ranges defined by bins. + +samples should at least have two numbers. + +**The output are:** + +histogram2 : ndarray of rank 1 + + Each value represents the occurrences for a given bin (range) of values. + + +**example**: + +stats.histogram2([4,17,8,3], [30,45,5,3]) the result is (array([ 0, -2, -2, 4])) + +============ +histogram +============ + +Separates the range into several bins and returns the number of instances in each bin + +**The output are:** + +histogram : ndarray + + Number of points (or sum of weights) in each bin. + +low_range : float + + Lowest value of histogram, the lower limit of the first bin. + +binsize : float + + The size of the bins (all bins have the same size). + +extrapoints : int + + The number of points outside the range of the histogram. + + +**example**: + +histogram([4,17,8,3],defaultlimits=(2.0,3.4)) the result is ([ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.],2.0,0.14,3) + + +============ +itemfreq +============ + +Computes the frequencies for numbers + +**The output are:** + +temfreq : (K, 2) ndarray + A 2-D frequency table. Column 1 contains sorted, unique values from a, column 2 contains their respective counts. + + +**example**: + +variation([4,17,8,3]) the result is array([[ 3, 1], [ 4, 1],[ 8, 1],[17, 1]]) + +=== +Sem +=== + +Calculates the standard error of the mean (or standard error of measurement) of the values in the input array. + + +**The output are:** + +s : ndarray or float + The standard error of the mean in the sample(s), along the input axis. + + +**example**: + +variation([4,17,8,3],ddof=1) the result is(3.18852107828) + +===== +Z Map +===== + +Calculates the relative z-scores. + +Returns an array of z-scores, i.e., scores that are standardized to zero mean and unit variance, where mean and variance are calculated from the comparison array. + + +**The output are:** + +zscore : array_like + + Z-scores, in the same shape as scores. + +**example**: + +stats.zmap([4,17,8,3],[30,45,5,3],ddof=1)the result is[-0.82496302 -0.18469321 -0.62795692 -0.87421454] + +======= +Z Score +======= + +Calculates the z score of each value in the sample, relative to the sample mean and standard deviation + + +**The output are:** + +zscore : array_like + The z-scores, standardized by mean and standard deviation of input array a. + + +**example**: + +variation([4,17,8,3],ddof=0) the result is ([-0.72428597 1.62964343 0. -0.90535746]) + +=============== +Signal to noise +=============== + +The signal-to-noise ratio of the input data. + +Returns the signal-to-noise ratio of a, here defined as the mean divided by the standard deviation. + + +**The output are:** + +s2n : ndarray + The mean to standard deviation ratio(s) along axis, or 0 where the standard deviation is 0. + + +**example**: + +variation([4,17,8,3],ddof=0) the result is (1.44857193668) + +=================== +Percentile of score +=================== + +The percentile rank of a score relative to a list of scores. + +A percentileofscore of, for example, 80% means that 80% of the scores in a are below the given score. In the case of gaps or ties, the exact definition depends on the optional keyword, kind. + +**The output are:** + +pcos : float + Percentile-position of score (0-100) relative to a. + + +**example**: + +percentileofscore([4,17,8,3],score=3,kind='rank') the result is(25.0) + +=================== +Score at percentile +=================== + +Calculate the score at a given percentile of the input sequence. + +For example, the score at per=50 is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of interpolation. If the parameter limit is provided, it should be a tuple (lower, upper) of two values. + +The second simple should be in range [0,100]. + +**The output are:** + +score : float or ndarray + Score at percentile(s). + + +**example**: + +stats.scoreatpercentile([4,17,8,3],[8,3],(0,100),'fraction') the result is array([ 3.24, 3.09]) + +======= +relfreq +======= + +Returns a relative frequency histogram, using the histogram function + +numbins are the number of bins to use for the histogram. + +**The output are:** + +relfreq : ndarray + + Binned values of relative frequency. + +lowerreallimit : float + + Lower real limit + +binsize : float + + Width of each bin. + +extrapoints : int + + Extra points. + + +**example**: + +stats.relfreq([4,17,8,3],10,(0,100)) the result is (array([ 0.75, 0.25, 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]), 0, 10.0, 0) + +================ +Binned statistic +================ + +Compute a binned statistic for a set of data. + +This is a generalization of a histogram function. A histogram divides the space into bins, and returns the count of the number of points in each bin. This function allows the computation of the sum, mean, median, or other statistic of the values within each bin. + +Y must be the same shape as X + +**The output are:** + +statistic : array + + The values of the selected statistic in each bin. + +bin_edges : array of dtype float + + Return the bin edges (length(statistic)+1). + +binnumber : 1-D ndarray of ints + + This assigns to each observation an integer that represents the bin in which this observation falls. Array has the same length as values. + + +**example**: + + stats.binned_statistic([4,17,8,3],[30,45,5,3],'sum',10,(0,100)) the result is ([ 38. 45. 0. 0. 0. 0. 0. 0. 0. 0.],[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.],[1 2 1 1]) + +================ +obrientransform +================ + +Computes the O’Brien transform on input data (any number of arrays). + +Used to test for homogeneity of variance prior to running one-way stats. + +It has to have at least two samples. + +**The output are:** + +obrientransform : ndarray + + Transformed data for use in an ANOVA. The first dimension of the result corresponds to the sequence of transformed arrays. If the arrays given are all 1-D of the same length, the return value is a 2-D array; otherwise it is a 1-D array of type object, with each element being an ndarray. + + +**example**: + +stats.obrientransformcenter([4,17,8,3], [30,45,5,3]) the result is (array([[ 16.5 , 124.83333333, -10.16666667, 31.5 ],[ 39.54166667, 877.04166667, 310.375 , 422.04166667]])) + +========= +bayes mvs +========= + +Bayesian confidence intervals for the mean, var, and std.alpha should be larger than 0,smaller than 1. + + +**The output are:** + +mean_cntr, var_cntr, std_cntr : tuple + +The three results are for the mean, variance and standard deviation, respectively. Each result is a tuple of the form: + +(center, (lower, upper)) + +with center the mean of the conditional pdf of the value given the data, and (lower, upper) a confidence interval, centered on the median, containing the estimate to a probability alpha. + +**example**: + +stats.bayes_mvs([4,17,8,3],0.8) the result is (8.0, (0.49625108326958145, 15.503748916730416));(122.0, (15.611548029617781, 346.74229584218108));(8.8129230241075476, (3.9511451542075475, 18.621017583423871)) + +========= +sigmaclip +========= + +Iterative sigma-clipping of array elements. + +The output array contains only those elements of the input array c that satisfy the conditions + +**The output are:** + +c : ndarray + Input array with clipped elements removed. +critlower : float + Lower threshold value use for clipping. +critlupper : float + Upper threshold value use for clipping. + + +**example**: + +sigmaclip([4,17,8,3]) the result is [ 4. 17. 8. 3.],-14.0907220344,30.0907220344） + +========= +threshold +========= + +Clip array to a given value. + +Similar to numpy.clip(), except that values less than threshmin or greater than threshmax are replaced by newval, instead of by threshmin and threshmax respectively. + + +**The output are:** + +out : ndarray + The clipped input array, with values less than threshmin or greater than threshmax replaced with newval. + +**example**: + +stats.threshold([4,17,8,3],2,8,0)the result is array([4, 17, 8, 3]) + +======== +trimboth +======== + +Slices off a proportion of items from both ends of an array. + +Slices off the passed proportion of items from both ends of the passed array (i.e., with proportiontocut = 0.1, slices leftmost 10% and rightmost 10% of scores). You must pre-sort the array if you want ‘proper’ trimming. Slices off less if proportion results in a non-integer slice index (i.e., conservatively slices off proportiontocut). + + +**The output are:** + +out : ndarray + Trimmed version of array a. + +**example**: + +stats.trimboth([4,17,8,3],0.1)the result is array([ 4, 17, 8, 3]) + +===== +trim1 +===== + +Slices off a proportion of items from ONE end of the passed array distribution. + +If proportiontocut = 0.1, slices off ‘leftmost’ or ‘rightmost’ 10% of scores. Slices off LESS if proportion results in a non-integer slice index (i.e., conservatively slices off proportiontocut ). + +**The output are:** + +trim1 : ndarray + + Trimmed version of array a + +**example**: + +stats.trim1([4,17,8,3],0.5,'left')the result is array([8, 3]) + +========= +spearmanr +========= + +Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation. + +The Spearman correlation is a nonparametric measure of the monotonicity of the relationship between two datasets. Unlike the Pearson correlation, the Spearman correlation does not assume that both datasets are normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact monotonic relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases. + +**The output are:** + +rho : float or ndarray (2-D square) + + Spearman correlation matrix or correlation coefficient (if only 2 variables are given as parameters. Correlation matrix is square with length equal to total number of variables (columns or rows) in a and b combined. + +p-value : float + + The two-sided p-value for a hypothesis test whose null hypothesis is that two sets of data are uncorrelated, has same dimension as rho. + +**example**: + +stats.spearmanr([4,17,8,3,30,45,5,3],[5,3,4,17,8,3,30,45])the result is (-0.722891566265, 0.0427539458876) + +======== +f oneway +======== + +Performs a 1-way ANOVA. + +The one-way ANOVA tests the null hypothesis that two or more groups have the same population mean. The test is applied to samples from two or more groups, possibly with differing sizes. + +**The output are:** + +F-value : float + + The computed F-value of the test. + +p-value : float + + The associated p-value from the F-distribution. + +**example**: + +stats. f_oneway([4,17,8,3], [30,45,5,3]) the result is (1.43569457222,0.276015080537) + +================= +Mann-Whitney rank +================= + +Compute the Wilcoxon rank-sum statistic for two samples. + +The Wilcoxon rank-sum test tests the null hypothesis that two sets of measurements are drawn from the same distribution. The alternative hypothesis is that values in one sample are more likely to be larger than the values in the other sample. + +This test should be used to compare two samples from continuous distributions. It does not handle ties between measurements in x and y. For tie-handling and an optional continuity correction use mannwhitneyu. + +----- + +Computes the Mann-Whitney rank test on samples x and y. + +u : float + + The Mann-Whitney statistics. + +prob : float + + One-sided p-value assuming a asymptotic normal distribution. + +=================== +Ansari-Bradley test +=================== + +Perform the Ansari-Bradley test for equal scale parameters + +The Ansari-Bradley test is a non-parametric test for the equality of the scale parameter of the distributions from which two samples were drawn. + +The p-value given is exact when the sample sizes are both less than 55 and there are no ties, otherwise a normal approximation for the p-value is used. + +----- + +Computes the Ansari-Bradley test for samples x and y. + +**The output are:** + +AB : float + + The Ansari-Bradley test statistic + +p-value : float + + The p-value of the hypothesis test + +**example**: + +ansari([1,2,3,4],[15,5,20,8,10,12]) the result is (10.0, 0.53333333333333333) + +======== +bartlett +======== + +Perform Bartlett’s test for equal variances + +Bartlett’s test tests the null hypothesis that all input samples are from populations with equal variances. + +It has to have at least two samples. + +**The output are:** + +T : float + + The test statistic. + +p-value : float + + The p-value of the test. + + +**example**: + +stats.bartlett([4,17,8,3], [30,45,5,3]) the result is (2.87507113948,0.0899609995242) + +====== +levene +====== + +Perform Levene test for equal variances. + +The Levene test tests the null hypothesis that all input samples are from populations with equal variances. + +It has to have at least two samples. + +**The output are:** + +W : float + + The test statistic. + +p-value : float + + The p-value for the test. + + +**example**: + +stats.levene(center='mean',proportiontocut=0.01,[4,17,8,3], [30,45,5,3]) the result is (11.5803858521,0.014442549362) + +======= +fligner +======= + +Perform Fligner’s test for equal variances. + +Fligner’s test tests the null hypothesis that all input samples are from populations with equal variances. Fligner’s test is non-parametric in contrast to Bartlett’s test bartlett and Levene’s test levene. + +**The output are:** + +Xsq : float + + The test statistic. + +p-value : float + + The p-value for the hypothesis test. + + +========== +linregress +========== + +Calculate a regression line + +This computes a least-squares regression for two sets of measurements. + +----- + +Computes the least-squares regression for samples x and y. + +**The output are:** + +slope : float + + slope of the regression line + +intercept : float + + intercept of the regression line + +r-value : float + + correlation coefficient + +p-value : float + + two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero. + +stderr : float + + Standard error of the estimate + +**example**: + +linregress([4,417,8,3],[30,45,5,3]) the result is (0.0783053989099, 12.2930169177, 0.794515680443,0.205484319557,0.0423191764713) + +=========== +ttest 1samp +=========== + +Calculates the T-test for the mean of ONE group of scores. + +This is a two-sided test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean, popmean. + +**The output are:** + +t : float or array + + The calculated t-statistic. + +prob : float or array + + The two-tailed p-value. + +**example**: + +stats.ttest_1samp([4,17,8,3],[30,45,5,3])the result is (array([ -6.89975053, -11.60412589, 0.94087507, 1.56812512]), array([ 0.00623831, 0.00137449, 0.41617971, 0.21485306])) + +========= +ttest ind +========= + +Calculates the T-test for the means of TWO INDEPENDENT samples of scores. + +This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the populations have identical variances. + +The independent samples t-test is used when two separate sets of independent and identically distributed samples are obtained, one from each of the two populations +being compared. +----- +Computes the T-test for the means of independent samples x and y. + +**The output are:** + +t : float or array + + The calculated t-statistic. + +prob : float or array + + The two-tailed p-value. + +**example**: + +ttest_ind([4,417,8,3],[30,45,5,3]) the result is (0.842956644207,0.431566932748) + +========= +ttest rel +========= + +Calculates the T-test on TWO RELATED samples of scores, a and b. + +This is a two-sided test for the null hypothesis that 2 related or repeated samples have identical average (expected) values. + +related samples t-tests typically consist of a sample of matched pairs of similar units, or one group of units that has been tested twice (a "repeated measures" t-test) + +----- + +Computes the T-test for the means of related samples x and y. + +**The output are:** + +t : float or array + + t-statistic + +prob : float or array + + two-tailed p-value + +**example**: + +ttest_rel([4,417,8,3],[30,45,5,3]) the result is (0.917072474241,0.426732624361) + +========= +chisquare +========= + +Calculates a one-way chi square test. + +The chi square test tests the null hypothesis that the categorical data has the given frequencies. + +**The output are:** + +chisq : float or ndarray + + The chi-squared test statistic. The value is a float if axis is None or f_obs and f_exp are 1-D. + +p : float or ndarray + + The p-value of the test. The value is a float if ddof and the return value chisq are scalars. + +**example**: + +stats.chisquare([4,17,8,3],[30,45,5,3],ddof=1)the result is (41.7555555556,8.5683326078e-10) + +================ +power divergence +================ + +Cressie-Read power divergence statistic and goodness of fit test. + +This function tests the null hypothesis that the categorical data has the given frequencies, using the Cressie-Read power divergence statistic. + +**The output are:** + +stat : float or ndarray + + The Cressie-Read power divergence test statistic. The value is a float if axis is None or if` f_obs and f_exp are 1-D. + +p : float or ndarray + + The p-value of the test. The value is a float if ddof and the return value stat are scalars. + +**example**: + +stats.power_divergence([4,17,8,3],[30,45,5,3],1,lambda=1)the result is (41.7555555556， 8.5683326078e-10) + +========== +tiecorrect +========== + +Tie correction factor for ties in the Mann-Whitney U and Kruskal-Wallis H tests. + +**The output are:** + +factor : float + + Correction factor for U or H. + +**example**: + +stats.tiecorrect([4,17,8,3,30,45,5,3])the result is (0.988095238095) + +======== +rankdata +======== + +Assign ranks to data, dealing with ties appropriately. + +Ranks begin at 1. The method argument controls how ranks are assigned to equal values. See [R308] for further discussion of ranking methods. + +**The output are:** + +ranks : ndarray + + An array of length equal to the size of a, containing rank scores. + +**example**: + +stats.rankdata([4,17,8,3],average)the result is ([ 2. 4. 3. 1.]) + +======= +kruskal +======= + +Compute the Kruskal-Wallis H-test for independent samples + +The Kruskal-Wallis H-test tests the null hypothesis that the population median of all of the groups are equal. It is a non-parametric version of ANOVA. + +The number of samples have to be more than one + +**The output are:** + +H-statistic : float + + The Kruskal-Wallis H statistic, corrected for ties + +p-value : float + + The p-value for the test using the assumption that H has a chi square distribution + + +**example**: + +stats. kruskal([4,17,8,3], [30,45,5,3]) the result is (0.527108433735,0.467825077285) + +================== +friedmanchisquare +================== + +Computes the Friedman test for repeated measurements + +The Friedman test tests the null hypothesis that repeated measurements of the same individuals have the same distribution. It is often used to test for consistency among measurements obtained in different ways. + +The number of samples have to be more than two. + +**The output are:** + +friedman chi-square statistic : float + + the test statistic, correcting for ties + +p-value : float + + the associated p-value assuming that the test statistic has a chi squared distribution + + +**example**: + +stats.friedmanchisquare([4,17,8,3],[8,3,30,45],[30,45,5,3])the result is (0.933333333333,0.627089085273) + +===== +mood +===== + +Perform Mood’s test for equal scale parameters. + +Mood’s two-sample test for scale parameters is a non-parametric test for the null hypothesis that two samples are drawn from the same distribution with the same scale parameter. + +----- + +Computes the Mood’s test for equal scale samples x and y. + +**The output are:** + +z : scalar or ndarray + + The z-score for the hypothesis test. For 1-D inputs a scalar is returned; + +p-value : scalar ndarray + + The p-value for the hypothesis test. + +**example**: + +mood([4,417,8,3],[30,45,5,3]) the result is (0.396928310068,0.691420327045) + +=============== +combine_pvalues +=============== + +Methods for combining the p-values of independent tests bearing upon the same hypothesis. + + +**The output are:** + +statistic: float + + The statistic calculated by the specified method: - “fisher”: The chi-squared statistic - “stouffer”: The Z-score + +pval: float + + The combined p-value. + +**example**: + +stats.combine_pvalues([4,17,8,3],method='fisher',weights=[5,6,7,8]) the result is (-14.795123071,1.0) + +=========== +median test +=========== + +Mood’s median test. + +Test that two or more samples come from populations with the same median. + +**The output are:** + +stat : float + +The test statistic. The statistic that is returned is determined by lambda. The default is Pearson’s chi-squared statistic. + +p : float + +The p-value of the test. + +m : float + +The grand median. + +table : ndarray + +The contingency table. + + +**example**: + +stats.median_test(ties='below',correction=True ,lambda=1,*a)the result is ((0.0, 1.0, 6.5, array([[2, 2],[2, 2]]))) + +======== +shapiro +======== + +Perform the Shapiro-Wilk test for normality. + +The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution. + +----- + +Computes the Shapiro-Wilk test for samples x and y. + +If x has length n, then y must have length n/2. + +**The output are:** + +W : float + + The test statistic. + +p-value : float + + The p-value for the hypothesis test. + + +**example**: + +shapiro([4,417,8,3]) the result is (0.66630089283, 0.00436889193952) + +======== +anderson +======== + +Anderson-Darling test for data coming from a particular distribution + +The Anderson-Darling test is a modification of the Kolmogorov- Smirnov test kstest for the null hypothesis that a sample is drawn from a population that follows a particular distribution. For the Anderson-Darling test, the critical values depend on which distribution is being tested against. This function works for normal, exponential, logistic, or Gumbel (Extreme Value Type I) distributions. + +----- + +Computes the Anderson-Darling test for samples x which comes from a specific distribution.. + +**The output are:** + + +A2 : float + + The Anderson-Darling test statistic + +critical : list + + The critical values for this distribution + +sig : list + + The significance levels for the corresponding critical values in percents. The function returns critical values for a differing set of significance levels depending on the distribution that is being tested against. + +**example**: + +anderson([4,417,8,3],norm) the result is (0.806976419634,[ 1.317 1.499 1.799 2.098 2.496] ,[ 15. 10. 5. 2.5 1. ]) + +========== +binom_test +========== + +Perform a test that the probability of success is p. + +This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is p. + +he binomial test is an exact test of the statistical significance of deviations from a theoretically expected distribution of observations into two categories. + +----- + +Computes the test for the probability of success is p . + +**The output are:** + +p-value : float + + The p-value of the hypothesis test + +**example**: + +binom_test([417,8],1,0.5) the result is (5.81382734132e-112) + +======== +pearsonr +======== + +Calculates a Pearson correlation coefficient and the p-value for testing non-correlation. + +The Pearson correlation coefficient measures the linear relationship between two datasets.The value of the correlation (i.e., correlation coefficient) does not depend on the specific measurement units used. + +**The output are:** + +Pearson’s correlation coefficient: float + +2-tailed p-value: float + + +**example**: + +pearsonr([4,17,8,3],[30,45,5,3]) the result is (0.695092958988,0.304907041012) + +======== +wilcoxon +======== + +Calculate the Wilcoxon signed-rank test. + +The Wilcoxon signed-rank test tests the null hypothesis that two related paired samples come from the same distribution. In particular, it tests whether the distribution of the differences x - y is symmetric about zero. It is a non-parametric version of the paired T-test. + +**The output are:** + +T : float + + The sum of the ranks of the differences above or below zero, whichever is smaller. + +p-value : float + + The two-sided p-value for the test. + + +**example**: + +stats.wilcoxon([3,6,23,70,20,55,4,19,3,6], +[23,70,20,55,4,19,3,6,23,70],zero_method='pratt',correction=True) the result is (23.0, 0.68309139830960874) + +============== +pointbiserialr +============== + +Calculates a Pearson correlation coefficient and the p-value for testing non-correlation. + +The Pearson correlation coefficient measures the linear relationship between two datasets.The value of the correlation (i.e., correlation coefficient) does not depend on the specific measurement units used. +**The output are:** + +r : float + + R value + +p-value : float + + 2-tailed p-value + + +**example**: + +pointbiserialr([0,0,0,1,1,1,1],[1,0,1,2,3,4,5]) the result is (0.84162541153017323, 0.017570710081214368) + +======== +ks_2samp +======== + +Computes the Kolmogorov-Smirnov statistic on 2 samples. + +This is a two-sided test for the null hypothesis that 2 independent samples are drawn from the same continuous distribution. + +If the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same. + +**The output are:** + +D : float + + KS statistic + +p-value : float + + two-tailed p-value + + +**example**: + +ks_2samp([4,17,8,3],[30,45,5,3]) the result is (0.5,0.534415719217) + +========== +kendalltau +========== + +Calculates Kendall’s tau, a correlation measure for sample x and sample y. + +sample x and sample y should be in the same size. + +Kendall’s tau is a measure of the correspondence between two rankings. Values close to 1 indicate strong agreement, values close to -1 indicate strong disagreement. This is the tau-b version of Kendall’s tau which accounts for ties. + + +**The output are:** + +Kendall’s tau : float + + The tau statistic. + +p-value : float + + The two-sided p-value for a hypothesis test whose null hypothesis is an absence of association, tau = 0. + + +**example**: + +kendalltau([4,17,8,3],[30,45,5,3])，the result is (0.666666666667,0.174231399708) + +================ +chi2_contingency +================ + +Chi-square test of independence of variables in a contingency table. + +This function computes the chi-square statistic and p-value for the hypothesis test of independence of the observed frequencies in the contingency table observed. + +**The output are:** + +chi2 : float + + The test statistic. + +p : float + + The p-value of the test + +dof : int + + Degrees of freedom + +expected : ndarray, same shape as observed + + The expected frequencies, based on the marginal sums of the table. + +**example**: + +stats.chi2_contingency([4,17,8,3],1)the result is (0.0, 1.0, 0, array([ 4., 17., 8., 3.])) + +====== +boxcox +====== + +Return a positive dataset transformed by a Box-Cox power transformation + +**The output are:** + +boxcox : ndarray + + Box-Cox power transformed array. + +maxlog : float, optional + + If the lmbda parameter is None, the second returned argument is the lambda that maximizes the log-likelihood function. + +(min_ci, max_ci) : tuple of float, optional + + If lmbda parameter is None and alpha is not None, this returned tuple of floats represents the minimum and maximum confidence limits given alpha. + + +**example**: + +stats.boxcox([4,17,8,3],0.9) the result is ([ 1.03301717 1.60587825 1.35353026 0.8679017 ],-0.447422166194,(-0.5699221654511225, -0.3259515659400082)) + +============== +boxcox normmax +============== + +Compute optimal Box-Cox transform parameter for input data + +**The output are:** + +maxlog : float or ndarray + + The optimal transform parameter found. An array instead of a scalar for method='all'. + + +**example**: + +stats.boxcox_normmax([4,17,8,3],(-2,2),'pearsonr')the result is (-0.702386238971) + +========== +boxcox llf +========== + +The boxcox log-likelihood function + +**The output are:** + +llf : float or ndarray + + Box-Cox log-likelihood of data given lmb. A float for 1-D data, an array otherwise. + +**example**: + +stats.boxcox_llf(1,[4,17,8,3]) the result is (-6.83545336723) + +======= +entropy +======= + +Calculate the entropy of a distribution for given probability values. + +If only probabilities pk are given, the entropy is calculated as S = -sum(pk * log(pk), axis=0). + +If qk is not None, then compute the Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=0). + +This routine will normalize pk and qk if they don’t sum to 1. + +**The output are:** + +S : float + + The calculated entropy. + + +**example**: + +stats.entropy([4,17,8,3],[30,45,5,3],1.6)the result is (0.641692653659) + +====== +kstest +====== + +Perform the Kolmogorov-Smirnov test for goodness of fit. + +**The output are:** + +D : float + + KS test statistic, either D, D+ or D-. + +p-value : float + + One-tailed or two-tailed p-value. + +**example**: + +stats.kstest([4,17,8,3],'norm',N=20,alternative='two-sided',mode='approx')the result is (0.998650101968,6.6409100441e-12) + +=========== +theilslopes +=========== + +Computes the Theil-Sen estimator for a set of points (x, y). + +theilslopes implements a method for robust linear regression. It computes the slope as the median of all slopes between paired values. + +**The output are:** + +medslope : float + + Theil slope. + +medintercept : float + + Intercept of the Theil line, as median(y) - medslope*median(x). + +lo_slope : float + + Lower bound of the confidence interval on medslope. + +up_slope : float + + Upper bound of the confidence interval on medslope. + +**example**: + +stats.theilslopes([4,17,8,3],[30,45,5,3],0.95)the result is (0.279166666667,1.11458333333,-0.16,2.5) + + + diff -r 000000000000 -r 178b22349b79 test-data/anderson.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/anderson.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 0.8395671270811338 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.3508336255833786 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.34860835455981043 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.40500235419538555 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.2796055138521272 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.596047080196433 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.4363855271379071 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.30419696306940924 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.9795874693762361 0.802 0.937 1.166 1.397 1.702 , 15.0 10.0 5.0 2.5 1.0 diff -r 000000000000 -r 178b22349b79 test-data/boxcox_normmax.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/boxcox_normmax.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 0.20351483127630257 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 -0.12078501691459378 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 -0.44742218359385444 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.8668717354666292 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.59563618553533 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.3631109785613174 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 -0.09467843209836545 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.21165837130330667 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 -0.0943832002581824 diff -r 000000000000 -r 178b22349b79 test-data/boxcox_normmax2.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/boxcox_normmax2.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 5.098476604777152 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 -0.18698374641392929 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 -0.7023862705588814 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.5488732779821681 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.8876859294780886 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 3.300879090532072 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.0163263831184912 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.2675982136667671 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 -0.1225406076896372 diff -r 000000000000 -r 178b22349b79 test-data/f_oneway.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/f_oneway.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 0.4965060684075027 0.5074543604725761 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.0028076743097800653 0.9594620298343387 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.4356945722171113 0.27601508053693957 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.75 0.4197530864197529 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.392336087185797 0.28266963152034197 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.1439902379499696 0.3259397502675068 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.4533092826418759 0.5258374221666884 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.40814437760296157 0.5465114483065159 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0615521855486173 0.8123320667065559 diff -r 000000000000 -r 178b22349b79 test-data/input.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/input.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 \ No newline at end of file diff -r 000000000000 -r 178b22349b79 test-data/itemfreq.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/itemfreq.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1.0,3.0,6.0,10.0,55.0,60.0,67.0 2,1,2,1,1,1,2 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3.0,4.0,6.0,19.0,20.0,23.0,55.0,70.0 2,1,2,1,1,1,1,1 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 3.0,4.0,5.0,8.0,17.0,30.0,45.0 2,2,1,1,2,1,1 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2.0,4.0,5.0,8.0,17.0,22.0,23.0,40.0,41.0,47.0 1,1,1,1,1,1,1,1,1,1 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4.0,6.0,9.0,11.0,14.0,17.0,33.0,50.0,51.0,83.0 1,1,1,1,1,1,1,1,1,1 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.0,4.0,7.0,9.0,17.0,38.0,50.0,54.0,60.0 1,2,1,1,1,1,1,1,1 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2.0,4.0,5.0,7.0,8.0,17.0,51.0,70.0,83.0 1,1,1,1,2,1,1,1,1 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.0,4.0,5.0,8.0,9.0,17.0,33.0,67.0 1,2,1,1,1,2,1,1 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1.0,4.0,5.0,9.0,17.0,60.0,78.0 2,2,2,1,1,1,1 diff -r 000000000000 -r 178b22349b79 test-data/median_test_result1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/median_test_result1.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,18 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 0.0 1.0 8.0 [[2 2 2] + [2 2 2]] 2,2,2 2,2,2 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.0 1.0 19.5 [[2 2 2] + [2 2 2]] 2,2,2 2,2,2 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 1.0 6.5 [[2 2 2] + [2 2 2]] 2,2,2 2,2,2 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2.0 0.36787944117144245 12.5 [[3 2 1] + [1 2 3]] 3,2,1 1,2,3 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2.0 0.36787944117144245 12.5 [[3 2 1] + [1 2 3]] 3,2,1 1,2,3 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2.0 0.36787944117144245 8.5 [[3 2 1] + [1 2 3]] 3,2,1 1,2,3 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4.8 0.09071795328941248 8.0 [[4 1 2] + [0 3 2]] 4,1,2 0,3,2 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.6857142857142857 0.7097395956891255 8.0 [[3 2 2] + [1 2 2]] 3,2,2 1,2,2 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.6857142857142857 0.7097395956891255 5.0 [[3 2 2] + [1 2 2]] 3,2,2 1,2,2 diff -r 000000000000 -r 178b22349b79 test-data/normaltest.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/normaltest.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 19.151765858220887 6.938201116887135e-05 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 4.306712648280366 0.1160938545412872 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 5.8877986151838 0.052659990380181286 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4.692388738731004 0.09573279350518404 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 10.755118387226739 0.004619082467994047 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4.397330213057973 0.11095116754126409 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 11.414523481144135 0.003321755932387679 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 12.837704536044896 0.0016305265687451174 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 14.49099384379253 0.0007133795707588875 diff -r 000000000000 -r 178b22349b79 test-data/obrientransform.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/obrientransform.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1457.3333333333333,1567.3333333333333,957.3333333333334,867.3333333333334 -29.666666666666668,2070.3333333333335,250.33333333333334,110.33333333333333 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 604.3333333333334,394.3333333333333,-229.0,3061.0 -83.0,1433.6666666666667,583.6666666666666,-66.33333333333333 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 16.5,124.83333333333333,-10.166666666666666,31.5 39.541666666666664,877.0416666666666,310.375,422.0416666666667 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 870.0,450.0,150.0,-30.0 735.3333333333334,-44.666666666666664,95.33333333333333,355.3333333333333 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2051.5416666666665,-85.95833333333333,2372.375,-160.95833333333334 1307.1666666666667,72.16666666666667,145.5,-12.833333333333334 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1411.875,354.375,157.70833333333334,-112.29166666666667 2676.3333333333335,-43.666666666666664,316.3333333333333,156.33333333333334 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1117.6666666666667,3117.6666666666665,1117.6666666666667,-29.0 3733.8333333333335,58.833333333333336,333.8333333333333,158.83333333333334 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 -25.291666666666668,634.7083333333334,-11.125,181.375 2968.7083333333335,333.7083333333333,396.2083333333333,-156.29166666666666 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 489.375,4660.208333333333,219.375,2.7083333333333335 2809.0,102.33333333333333,59.0,252.33333333333334 diff -r 000000000000 -r 178b22349b79 test-data/percentileofscore1.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/percentileofscore1.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 25.0 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.0 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 25.0 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 25.0 diff -r 000000000000 -r 178b22349b79 test-data/percentileofscore2.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/percentileofscore2.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 25.0 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.0 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.0 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 25.0 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 25.0 diff -r 000000000000 -r 178b22349b79 test-data/power_divergence.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/power_divergence.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 499.718181818 3.07309201963e-109 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 285.249282297 1.14526335773e-62 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 41.7555555556 8.5683326078e-10 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 415.659090909 5.50522701843e-91 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 788.95015873 4.80451754204e-172 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 2961.81666667 0.0 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1321.25714286 1.23781813439e-287 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 226.067778753 8.128409517e-50 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 1491.01666667 0.0 diff -r 000000000000 -r 178b22349b79 test-data/scoreatpercentile.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/scoreatpercentile.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 6.0 6.0 6.0 6.0 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 6.0 6.0 6.0 6.0 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 8.0 8.0 8.0 8.0 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 5.0 5.0 5.0 23.0 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 6.0 6.0 6.0 6.0 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 7.0 7.0 7.0 7.0 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 8.0 8.0 8.0 8.0 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 8.0 8.0 8.0 8.0 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 5.0 5.0 5.0 5.0 diff -r 000000000000 -r 178b22349b79 test-data/shapiro.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/shapiro.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 0.8119358420372009 0.12540598213672638 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.8314339518547058 0.1715719997882843 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.8674042224884033 0.28762444853782654 +5 47 41 23 40 22 8 2 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.939820408821106 0.6532316207885742 +6 33 83 51 50 11 9 14 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.9979438781738281 0.9934430718421936 +7 54 50 38 60 9 1 4 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.8657336235046387 0.28127521276474 +8 83 8 51 70 7 2 5 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.8628528714179993 0.2705530524253845 +9 33 8 1 67 5 4 17 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.8396957516670227 0.19451719522476196 +1 78 5 9 60 4 5 1 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.7127071619033813 0.016164502128958702 diff -r 000000000000 -r 178b22349b79 test-data/shapiro2.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/shapiro2.tabular Mon Nov 21 18:08:27 2022 +0000 @@ -0,0 +1,9 @@ +1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 1 67 6 60 10 55 3 6 0.7177605628967285 0.009460396133363247 +3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 3 6 23 70 20 55 4 19 0.7570356130599976 0.023322617635130882 +4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 4 17 8 3 30 45 5 3 0.7241963744163513 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