/dports/math/gretl/gretl-2021d/share/scripts/ramanathan/ |
H A D | ps_descriptions | 9 "ps3-2","OLS, change of units","house prices" 12 "ps3-5","OLS, coefficents","house prices" 13 "ps3-6","OLS, forecast errors","patents" 14 "ps4-1","OLS, F-test","house prices" 16 "ps4-3","OLS, omit and add","housing starts" 17 "ps4-4","OLS, omit","bus travel" 22 "ps5-4","OLS","car maintenance" 24 "ps6-1","OLS and logs","house prices" 25 "ps6-2","OLS, squared terms","cost function" 29 "ps6-6","OLS, lagged variables","patents" [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/iolib/tests/ |
H A D | test_summary2.py | 10 from statsmodels.regression.linear_model import OLS 41 reg1 = OLS(y1, x).fit() 42 reg2 = OLS(y2, x).fit() 67 reg1 = OLS(y1, x).fit() 68 reg2 = OLS(y2, x).fit() 85 reg1 = OLS(y1, x).fit() 86 reg2 = OLS(y2, x2).fit() 103 reg1 = OLS(y1, x2).fit() 104 reg2 = OLS(y2, x2).fit() 125 reg1 = OLS(y1, x).fit() [all …]
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H A D | test_summary.py | 10 from statsmodels.regression.linear_model import OLS 18 mod = OLS.from_formula('CPI_ ~ 1 + np.log(realgdp)', data=data) 27 res = OLS(y, x).fit()
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/dports/finance/R-cran-strucchange/strucchange/tests/ |
H A D | strucchange-tests.Rout.save | 33 > Nile.ocus <- efp(Nile ~ 1, type = "OLS-CUSUM") 47 OLS-based CUSUM test 54 OLS-based CUSUM test with alternative boundaries 89 OLS-based MOSUM test 96 OLS-based MOSUM test with range norm 173 OLS-based CUSUM test 215 OLS-based MOSUM test 222 OLS-based MOSUM test with range norm 287 OLS-based CUSUM test 353 Monitoring with OLS-based CUSUM test [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/regression/tests/ |
H A D | test_regression.py | 25 OLS, 234 res1 = OLS(endog, exog).fit() 242 model_qr = OLS(endog, exog) 319 results = OLS(x, y).fit() 343 res = OLS(y, y).fit() 1114 res = OLS(y, x).fit() 1128 res = OLS(y, x).fit() 1196 for cls in OLS, WLS, GLS: 1428 model1 = OLS(yvec, xmat) 1479 model1 = OLS(yvec - 1, xmat) [all …]
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H A D | test_theil.py | 16 from statsmodels.regression.linear_model import OLS, GLS 115 res_ols = OLS(self.res1.model.endog, self.res1.model.exog).fit() 179 cls.res2 = OLS(y, x[:, :3]).fit() 189 cls.res2 = OLS(y, x).fit() 205 cls.res2 = OLS(y, x).fit() 233 cls.res2 = OLS(y, x2).fit() 254 cls.res2 = OLS(y, x2).fit() 288 cls.res_ols = OLS(y, x).fit() 346 res_ols0 = OLS(ys[:n05], xs[:n05, :k]).fit() 347 res_ols1 = OLS(ys[n05:], xs[n05:, :k]).fit() [all …]
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/dports/biology/ncbi-cxx-toolkit/ncbi_cxx--25_2_0/src/algo/phy_tree/fastme/ |
H A D | fastme.cpp | 138 *wtype = OLS; in chooseSettings() 159 *btype = OLS; in chooseSettings() 187 *ntype = OLS; in chooseSettings() 313 case OLS: in fastme_run() 324 case OLS: in fastme_run() 333 case OLS: in fastme_run() 334 if (OLS != btype) in fastme_run() 349 case OLS: in fastme_run() 350 if (OLS != btype) in fastme_run()
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/tests/ |
H A D | test_distributed_estimation.py | 45 mod = OLS(y, X) 62 mod = OLS(y, X) 87 mod = OLS(y, X) 130 mod = OLS(y, X) 151 mod = OLS(y, X) 172 mod = OLS(y, X) 201 mod = OLS(y, X) 338 ols_mod = OLS(y, X) 349 ols_mod = OLS(y, X) 395 ols_mod = OLS(y, X) [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/ |
H A D | ex_regressionplots.py | 38 res = sm.OLS(y, exog0).fit() 86 res1a = sm.OLS(y, exog0[:,[0,2]]).fit() 87 res1b = sm.OLS(x1, exog0[:,[0,2]]).fit() 89 res1c = sm.OLS(res1a.resid, res1b.resid).fit() 94 res2a = sm.OLS(y, exog0[:,[0,1]]).fit() 95 res2b = sm.OLS(x2, exog0[:,[0,1]]).fit() 97 res2c = sm.OLS(res2a.resid, res2b.resid).fit()
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H A D | try_fit_constrained.py | 97 from statsmodels.regression.linear_model import OLS 98 res2 = OLS(endog, exog).fit() 102 res1 = OLS(endog, exog_st).fit() 104 res1 = OLS(endog - exog.dot(transf.constant.squeeze()), exog_st).fit() 111 res3_ols = OLS(endog - exog[:, -1], exog[:, :-2]).fit() 115 res3 = OLS(endog, exog3_st).fit() 117 res3 = OLS(endog - exog.dot(transf3.constant.squeeze()), exog3_st).fit()
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/tests/ |
H A D | test_diagnostic.py | 1151 res = OLS(endog, exog).fit() 1175 res = OLS(y, x).fit() 1182 res2 = OLS(y, x[:, 0]).fit() 1255 res1 = OLS(y, x).fit() 1719 res1 = OLS(y, x1).fit() 1720 res2 = OLS(y, z1).fit() 1754 res1 = OLS(y, x).fit() 1755 res2 = OLS(y, z).fit() 1826 res = OLS(y, x).fit() 1838 res = OLS(y, x).fit() [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/regression/ |
H A D | ols_anova_original.py | 13 from statsmodels.regression.linear_model import OLS 111 res_b0 = OLS(y_b0, X_b0).results 162 res2 = OLS(y_b0, X2).results 169 res3 = OLS(y_b0, X3).results 266 rest1 = OLS(y,X).results 283 rest1 = OLS(y,X).results 310 rest_b1 = OLS(y_b1, X_b1).results 325 rest_b1a = OLS(y_b1a, X_b1a).results 333 rest_b1a_ = OLS(y_b1a_, X_b1a_).results
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/dports/math/p5-Statistics-OLS/Statistics-OLS-0.07/ |
H A D | README | 1 Statistics::OLS 4 I wrote Statistics::OLS to perform Ordinary Least Squares (linear curve 7 designed for univariate analysis. It accomodates OLS, but somewhat 9 might make sense to fold OLS into that module or a supermodule someday. 11 Statistics::OLS computes the estimated slope and intercept of the 17 harder to do multiple regression, so Statistics::OLS is unlikely ever
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/notebooks/ |
H A D | robust_models_0.ipynb | 115 "## Comparing OLS and RLM\n", 130 "sig = 0.3 # smaller error variance makes OLS<->RLM contrast bigger\n", 143 "Note that the quadratic term in OLS regression will capture outlier effects. " 152 "res = sm.OLS(y2, X).fit()\n", 180 "Draw a plot to compare OLS estimates to the robust estimates:" 197 "ax.plot(x1, res.fittedvalues, \"r-\", label=\"OLS\")\n", 210 "Fit a new OLS model using only the linear term and the constant:" 220 "res2 = sm.OLS(y2, X2).fit()\n", 247 "Draw a plot to compare OLS estimates to the robust estimates:" 263 "ax.plot(x1, res2.fittedvalues, \"r-\", label=\"OLS\")\n",
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H A D | wls.ipynb | 93 "## OLS vs. WLS\n", 95 "Estimate an OLS model for comparison: " 104 "res_ols = sm.OLS(y, X).fit()\n", 113 "Compare the WLS standard errors to heteroscedasticity corrected OLS standard errors:" 136 "rownames = [\"WLS\", \"OLS\", \"OLS_HC0\", \"OLS_HC1\", \"OLS_HC3\", \"OLS_HC3\"]\n", 145 "Calculate OLS prediction interval:" 175 "Draw a plot to compare predicted values in WLS and OLS:" 191 "# OLS\n", 193 "ax.plot(x, iv_u_ols, \"r--\", label=\"OLS\")\n",
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H A D | ols.ipynb | 37 "## OLS estimation\n", 85 "model = sm.OLS(y, X)\n", 111 "## OLS non-linear curve but linear in parameters\n", 145 "res = sm.OLS(y, X).fit()\n", 188 "ax.plot(x, res.fittedvalues, \"r--.\", label=\"OLS\")\n", 198 "## OLS with dummy variables\n", 259 "res2 = sm.OLS(y, X).fit()\n", 267 "Draw a plot to compare the true relationship to OLS predictions:" 347 "res3 = sm.OLS(y, X).fit()" 403 "ols_model = sm.OLS(y, X)\n", [all …]
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/dports/finance/R-cran-strucchange/strucchange/man/ |
H A D | efp.Rd | 9 or OLS residuals, parameter estimates or ML scores (OLS first order 31 be used for the residuals? By default, the standard OLS variance is employed. 40 \details{If \code{type} is one of \code{"Rec-CUSUM"}, \code{"OLS-CUSUM"}, 41 \code{"Rec-MOSUM"} or \code{"OLS-MOSUM"} the function \code{efp} will return a 43 on recursive residuals or on OLS residuals and the process will contain 51 regressors in the model, as it is based on recursive OLS estimates of the 52 regression coefficients or moving OLS estimates respectively. The recursive 111 Ploberger W., Kr�mer W. (1992), The CUSUM test with OLS residuals, 143 ## compute OLS-based CUSUM process and plot 145 ocus.nile <- efp(Nile ~ 1, type = "OLS-CUSUM") [all …]
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/dports/math/cmlib/cmlib-3.0_8/doc/slrpack/ |
H A D | rgm | 36 3. The name geometric mean alludes to the fact that if B(OLS-y) 39 subject to error) and if B(OLS-x) denotes the converse, then 41 B(OLS-y) and B(OLS-x). This technique for estimating the 42 regression parameters is preferable to OLS-y and to OLS-x 43 for the above type of data because the OLS-y technique is 46 while OLS-x results have the opposite characteristics (Riggs
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/dports/math/py-statsmodels/statsmodels-0.13.1/examples/python/ |
H A D | ols.py | 37 model = sm.OLS(y, X) 63 res = sm.OLS(y, X).fit() 120 res2 = sm.OLS(y, X).fit() 164 res3 = sm.OLS(y, X).fit() 185 ols_model = sm.OLS(y, X) 214 ols_results2 = sm.OLS(y.iloc[:14], X.iloc[:14]).fit()
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tools/tests/ |
H A D | test_web.py | 4 from statsmodels.regression.linear_model import OLS 25 url = _generate_url(OLS, True) 29 url = _generate_url(OLS, False)
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/dports/finance/R-cran-vars/vars/man/ |
H A D | stability.Rd | 20 \method{stability}{default}(x, type = c("OLS-CUSUM", "Rec-CUSUM", 21 "Rec-MOSUM", "OLS-MOSUM", "RE", "ME", "Score-CUSUM", "Score-MOSUM", 23 \method{stability}{varest}(x, type = c("OLS-CUSUM", "Rec-CUSUM", 24 "Rec-MOSUM", "OLS-MOSUM", "RE", "ME", "Score-CUSUM", "Score-MOSUM", 32 computed, the default is \sQuote{\code{OLS-CUSUM}}. For details see: 80 var.2c.stabil <- stability(var.2c, type = "OLS-CUSUM")
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/panel/ |
H A D | panel_short.py | 26 from statsmodels.regression.linear_model import OLS, GLS 97 self.res_pooled = OLS(self.endog, self.exog).fit() 117 self.res1 = OLS(wendog, wexog).fit() 158 self.res_pooled = OLS(self.endog, self.exog).fit() 168 self.res1 = OLS(wendog, wexog).fit()
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/stats/ |
H A D | oaxaca.py | 51 from statsmodels.regression.linear_model import OLS 177 self._f_model = OLS(endog_f, exog_f).fit( 180 self._s_model = OLS(endog_s, exog_s).fit( 233 _f_model = OLS(endog_f, exog_f).fit( 236 _s_model = OLS(endog_s, exog_s).fit( 272 _t_model = OLS(endog, neumark).fit( 278 _t_model = OLS(endog, exog).fit( 439 self._t_model = OLS(self.endog, self.neumark).fit( 445 self._t_model = OLS(self.endog, self.exog).fit(
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H A D | _diagnostic_other.py | 167 from statsmodels.regression.linear_model import OLS 279 … res_ols1 = OLS(res.resid_response / std, np.column_stack((dm_incl, dm_excl)) / std[:, None]).fit() 295 res_ols3 = OLS(np.ones(nobs), pp.resid * resid_p[:,None]).fit() 382 stat2 = OLS(np.ones(nobs), moms_test).fit().ess 717 res_ols_nb2 = OLS(endog_v, fitted).fit(use_t=False) 723 res_ols_nb1 = OLS(endog_v, fitted).fit(use_t=False) 730 res_ols_nb2 = OLS(endog_v, fitted).fit(cov_type='HC1', use_t=False) 736 res_ols_nb1 = OLS(endog_v, np.ones(len(endog_v))).fit(cov_type='HC1', 791 res_ols = OLS(endog_v, ex).fit(cov_type=cov_type, cov_kwds=cov_kwds, 932 res = OLS(endog, ex).fit() [all …]
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/dports/math/gretl/gretl-2021d/share/scripts/greene/ |
H A D | greene11_3.inp | 4 # run initial OLS 11 # iterate OLS till the error sum of squares converges 17 # run OLS
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