/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/ |
H A D | nonparametric.py | 49 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 …]
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H A D | power.py | 667 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 …]
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H A D | proportion.py | 1148 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 …]
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H A D | multivariate_tools.py | 166 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)
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H A D | meta_analysis.py | 327 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
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H A D | weightstats.py | 1542 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)
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H A D | multivariate.py | 85 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)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/tests/ |
H A D | test_proportion.py | 599 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 …]
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H A D | test_power.py | 403 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 …]
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H A D | test_meta.py | 56 cls.count1, cls.nobs1, cls.count2, cls.nobs2 = df_12y.values.T 60 dta = (self.count1, self.nobs1, self.count2, self.nobs2)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/ |
H A D | try_power2.py | 48 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…
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H A D | try_power.py | 18 print(smp.NormalIndPower().solve_power(effect_size=0.3, nobs1=80, alpha=0.05, power=None))
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
H A D | metaanalysis1.py | 75 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
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/dports/finance/R-cran-lmtest/lmtest/R/ |
H A D | lrtest.R | 28 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs functionVar 30 rval <- try(nobs1(x, ...), silent = TRUE)
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H A D | waldtest.R | 32 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs functionVar 34 rval <- try(nobs1(x, ...), silent = TRUE)
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/dports/math/R-cran-sandwich/sandwich/R/ |
H A D | auxiliary.R | 2 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else stats::nobs functionVar 4 rval <- try(nobs1(x, ...), silent = TRUE)
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/dports/devel/z88dk/z88dk/libsrc/gfx/narrow/ |
H A D | putc4x6.asm | 54 jr c,nobs1 56 .nobs1
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/dports/devel/z88dk/z88dk/libsrc/gfx/wide/ |
H A D | w_putc4x6.asm | 58 jr c,nobs1 60 .nobs1
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/ |
H A D | metaanalysis1.ipynb | 94 "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",
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/multivariate/ |
H A D | multivariate_ols.py | 90 nobs1, k_exog= x.shape 91 if nobs != nobs1: 93 'rows!' % (nobs1, nobs))
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/dports/finance/R-cran-strucchange/strucchange/R/ |
H A D | gefp.R | 183 nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else stats::nobs functionVar 185 rval <- try(nobs1(x, ...), silent = TRUE)
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/dports/math/gretl/gretl-2021d/lib/src/ |
H A D | tsls.c | 623 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()
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/dports/science/py-scipy/scipy-1.7.1/scipy/stats/ |
H A D | stats.py | 5712 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,
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/dports/science/py-scipy/scipy-1.7.1/scipy/stats/tests/ |
H A D | test_stats.py | 4619 nobs1, nobs2 = np.array([130, 140]), np.array([100, 150]) 4621 stats.ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
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