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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/
H A Dnonparametric.py49 nobs1 = len(x1)
59 rank1 = rank[:nobs1]
60 rank2 = rank[nobs1:]
459 nobs1 = len(x1)
461 nobs = nobs1 + nobs2
473 S1 /= nobs1 - 1
478 wbfn /= (nobs1 + nobs2) * np.sqrt(nobs1 * S1 + nobs2 * S2)
483 df_denom = np.power(nobs1 * S1, 2.0) / (nobs1 - 1)
502 nobs1=nobs1, nobs2=nobs2, nobs=nobs,
578 vn1 = var1 * nobs2 * nobs1 / (nobs1 - ddof)
[all …]
H A Dpower.py667 nobs2 = nobs1*ratio
670 df = (nobs1 - 1 + nobs2 - 1)
672 nobs = 1./ (1. / nobs1 + 1. / nobs2)
730 nobs1=nobs1,
747 def power(self, effect_size, nobs1, alpha, ratio=1, argument
786 nobs2 = nobs1*ratio
790 nobs = nobs1 - ddof
851 nobs1=nobs1,
1203 nobs2 = nobs1*ratio
1205 nobs = 1./ (1. / nobs1 + 1. / nobs2)
[all …]
H A Dproportion.py1148 p1 = count1 / nobs1
1276 nobs_row = np.array([nobs1, nobs2])
1277 nobs = nobs1 + nobs2
1342 nobs = nobs1 + nobs2
1344 p1 = count1 / nobs1
1389 var = (prop1 * (1 - prop1) / nobs1 +
1416 var = (1 / (prop1 * (1 - prop1) * nobs1) +
1572 p1 = count1 / nobs1
1915 nobs1=nobs1,
1916 nobs2=ratio * nobs1,
[all …]
H A Dmultivariate_tools.py166 nobs1, k1 = x1.shape
173 value = nobs1 * cc2[-1]
174 w_value = nobs1 * (cc2[-1] / (1. - cc2[-1]))
179 values = nobs1 * cc2[::-1].cumsum()
180 w_values = nobs1 * (cc2 / (1. - cc2))[::-1].cumsum()
214 nobs1, k1 = x1.shape # endogenous ?
222 df_resid = k1 * (nobs1 - k2 - demean)
H A Dmeta_analysis.py327 def effectsize_smd(mean1, sd1, nobs1, mean2, sd2, nobs2): argument
381 var_diff = (sd1**2 * (nobs1 - 1) +
382 sd2**2 * (nobs2 - 1)) / (nobs1 + nobs2 - 2)
384 nobs = nobs1 + nobs2
388 var_smdbc = nobs / nobs1 / nobs2 + smd_bc**2 / 2 / (nobs - 3.94)
392 def effectsize_2proportions(count1, nobs1, count2, nobs2, statistic="diff", argument
454 nobs_t = nobs1 + nobs2
456 cc2 = nobs1 / nobs_t
467 zero_mask1 = (count1 == 0) | (count1 == nobs1)
470 n1 = nobs1 + (cc1 + cc2) * zmask
H A Dweightstats.py1542 nobs1 = x1.shape[0]
1550 var_pooled = nobs1 * x1_var + nobs2 * x2_var
1551 var_pooled /= nobs1 + nobs2 - 2 * ddof
1552 var_pooled *= 1.0 / nobs1 + 1.0 / nobs2
1554 var_pooled = x1_var / (nobs1 - ddof)
1615 nobs1 = x1.shape[0]
1623 var_pooled = nobs1 * x1_var + nobs2 * x2_var
1624 var_pooled /= nobs1 + nobs2 - 2 * ddof
1625 var_pooled *= 1.0 / nobs1 + 1.0 / nobs2
1627 var_pooled = x1_var / (nobs1 - ddof)
H A Dmultivariate.py85 nobs1, k_vars = x1.shape
94 nobs_t = nobs1 + nobs2
95 combined_cov = ((nobs1 - 1) * cov1 + (nobs2 - 1) * cov2) / (nobs_t - 2)
97 t2 = (nobs1 * nobs2) / nobs_t * diff @ np.linalg.solve(combined_cov, diff)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/tests/
H A Dtest_proportion.py599 count1, nobs1 = 7, 34
631 ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
636 ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
640 ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
644 ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
650 ci = confint_proportions_2indep(count1, nobs1, count2, nobs2,
686 count1, nobs1 = 7, 34
726 count1, nobs1 = 7, 34
755 count1, nobs1 = 7, 34
806 count1, nobs1, count2, nobs2, value=low, compare=co,
[all …]
H A Dtest_power.py403 res3 = smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None)
699 pow_ = nip.solve_power(es0, nobs1=1600, alpha=0.01, power=None, ratio=1,
703 es = nip.solve_power(None, nobs1=1600, alpha=0.01, power=pow_, ratio=1,
712 es = nip.solve_power(None, nobs1=1600, alpha=0.01, power=pow_, ratio=1,
719 es = nip.solve_power(None, nobs1=1600, alpha=0.01, power=pow_, ratio=1,
726 es = nip.solve_power(nobs1=1600, alpha=0.01, effect_size=0, power=None)
732 assert_raises(ValueError, nip.solve_power, None, nobs1=1600, alpha=0.01,
737 nip.solve_power(nobs1=None, effect_size=0, alpha=0.01,
753 val = nip.solve_power(0.1, nobs1=None, alpha=0.01, power=pow_, ratio=1,
764 assert_warns(ConvergenceWarning, nip.solve_power, 0.1, nobs1=None,
[all …]
H A Dtest_meta.py56 cls.count1, cls.nobs1, cls.count2, cls.nobs2 = df_12y.values.T
60 dta = (self.count1, self.nobs1, self.count2, self.nobs2)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/
H A Dtry_power2.py48 nobs_p2 = ttind_solve_power(effect_size=effect_size, nobs1=None, alpha=alpha, power=power)
50 …print('effect', ttind_solve_power(effect_size=None, nobs1=nobs_p2, alpha=alpha, power=power), effe…
51 …print('alpha ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=None, power=power)…
52 …print('power ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=None…
53 …print('ratio ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, power=powe…
56 …print('smaller power', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, powe…
57 …print('larger power ', ttind_solve_power(effect_size=effect_size, nobs1=nobs_p2, alpha=alpha, powe…
H A Dtry_power.py18 print(smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None))
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/
H A Dmetaanalysis1.py75 mean2, sd2, nobs2, mean1, sd1, nobs1 = np.asarray( variable
80 np.array(nobs1 + nobs2)
84 eff, var_eff = effectsize_smd(mean2, sd2, nobs2, mean1, sd1, nobs1)
105 res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))
126 res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))
209 count1, nobs1, count2, nobs2 = df_12y.values.T variable
/dports/finance/R-cran-lmtest/lmtest/R/
H A Dlrtest.R28 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs functionVar
30 rval <- try(nobs1(x, ...), silent = TRUE)
H A Dwaldtest.R32 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs functionVar
34 rval <- try(nobs1(x, ...), silent = TRUE)
/dports/math/R-cran-sandwich/sandwich/R/
H A Dauxiliary.R2 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else stats::nobs functionVar
4 rval <- try(nobs1(x, ...), silent = TRUE)
/dports/devel/z88dk/z88dk/libsrc/gfx/narrow/
H A Dputc4x6.asm54 jr c,nobs1
56 .nobs1
/dports/devel/z88dk/z88dk/libsrc/gfx/wide/
H A Dw_putc4x6.asm58 jr c,nobs1
60 .nobs1
/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/
H A Dmetaanalysis1.ipynb94 "mean2, sd2, nobs2, mean1, sd1, nobs1 = np.asarray(\n",
107 "np.array(nobs1 + nobs2)"
123 "eff, var_eff = effectsize_smd(mean2, sd2, nobs2, mean1, sd1, nobs1)"
146 "res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))\n",
189 "res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))\n",
328 "count1, nobs1, count2, nobs2 = df_12y.values.T\n",
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/multivariate/
H A Dmultivariate_ols.py90 nobs1, k_exog= x.shape
91 if nobs != nobs1:
93 'rows!' % (nobs1, nobs))
/dports/finance/R-cran-strucchange/strucchange/R/
H A Dgefp.R183 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else stats::nobs functionVar
185 rval <- try(nobs1(x, ...), silent = TRUE)
/dports/math/gretl/gretl-2021d/lib/src/
H A Dtsls.c623 int nobs1; in tsls_hausman_test() local
656 nobs1 = hmod.nobs; in tsls_hausman_test()
702 pprintf(dbgprn, "smpl1 = %d, smpl2 = %d\n", nobs1, hmod.nobs); in tsls_hausman_test()
/dports/science/py-scipy/scipy-1.7.1/scipy/stats/
H A Dstats.py5712 def ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2, argument
5827 df, denom = _equal_var_ttest_denom(std1**2, nobs1, std2**2, nobs2)
5829 df, denom = _unequal_var_ttest_denom(std1**2, nobs1,
/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/
H A Dtest_stats.py4619 nobs1, nobs2 = np.array([130, 140]), np.array([100, 150])
4621 stats.ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)