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Python OLS.from_formula方法代码示例

本文整理汇总了Python中statsmodels.regression.linear_model.OLS.from_formula方法的典型用法代码示例。如果您正苦于以下问题:Python OLS.from_formula方法的具体用法?Python OLS.from_formula怎么用?Python OLS.from_formula使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在statsmodels.regression.linear_model.OLS的用法示例。


在下文中一共展示了OLS.from_formula方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_from_formula

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
def test_from_formula():
    mod = RecursiveLS.from_formula('cpi ~ m1', data=dta)
    res = mod.fit()

    # Test the RLS estimates against OLS estimates
    mod_ols = OLS.from_formula('cpi ~ m1', data=dta)
    res_ols = mod_ols.fit()
    assert_allclose(res.params, res_ols.params)
开发者ID:bashtage,项目名称:statsmodels,代码行数:10,代码来源:test_recursive_ls.py

示例2: test_from_formula

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
def test_from_formula():
    with pytest.warns(ValueWarning, match="No frequency information"):
        mod = RecursiveLS.from_formula('cpi ~ m1', data=dta)

    res = mod.fit()

    # Test the RLS estimates against OLS estimates
    mod_ols = OLS.from_formula('cpi ~ m1', data=dta)
    res_ols = mod_ols.fit()
    assert_allclose(res.params, res_ols.params)
开发者ID:kshedden,项目名称:statsmodels,代码行数:12,代码来源:test_recursive_ls.py

示例3: setup_class

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
    def setup_class(cls):
        from .test_diagnostic import get_duncan_data
        endog, exog, labels = get_duncan_data()
        data = pd.DataFrame(np.column_stack((endog, exog)),
                        columns='y const var1 var2'.split(),
                        index=labels)

        res0 = GLM.from_formula('y ~ const + var1 + var2 - 1', data).fit()
        res1 = OLS.from_formula('y ~ const + var1 + var2 - 1', data).fit()
        cls.infl1 = res1.get_influence()
        cls.infl0 = res0.get_influence()
开发者ID:haribharadwaj,项目名称:statsmodels,代码行数:13,代码来源:test_influence.py

示例4: setup_class

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
    def setup_class(cls):
        nobs = 30
        np.random.seed(987128)
        x = np.random.randn(nobs, 3)
        y = x.sum(1) + np.random.randn(nobs)
        index = ['obs%02d' % i for i in range(nobs)]
        # add one extra column to check that it doesn't matter
        cls.data = pd.DataFrame(np.round(np.column_stack((y, x)), 4),
                                columns='y var1 var2 var3'.split(),
                                index=index)

        cls.res = OLS.from_formula('y ~ var1 + var2', data=cls.data).fit()
开发者ID:ChadFulton,项目名称:statsmodels,代码行数:14,代码来源:test_predict.py

示例5: test_missing_formula_predict

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
def test_missing_formula_predict():
    # see 2171
    nsample = 30

    data = pandas.DataFrame({'x': np.linspace(0, 10, nsample)})
    null = pandas.DataFrame({'x': np.array([np.nan])})
    data = pandas.concat([data, null])
    beta = np.array([1, 0.1])
    e = np.random.normal(size=nsample+1)
    data['y'] = beta[0] + beta[1] * data['x'] + e
    model = OLS.from_formula('y ~ x', data=data)
    fit = model.fit()
    pred = fit.predict(exog=data[:-1])
开发者ID:NanoResearch,项目名称:statsmodels,代码行数:15,代码来源:test_regression.py

示例6: env_corr

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
    def env_corr(self, env_vars, coeff_plot=False, qq_plot=False):
        """
        Determine correlations with environmental/non-discretionary variables
        using a logit regression. Tobit will be implemented when available
        upstream in statsmodels.

        Takes:
            env_vars: A pandas dataframe of environmental variables

        Returns:
            corr_mod: the statsmodels' model instance containing the inputs
                      and results from the logit model.

        Note that there can be no spaces in the variables' names.
        """

        import matplotlib.pyplot as plt
        from statsmodels.regression.linear_model import OLS
        from statsmodels.graphics.gofplots import qqplot
        from seaborn import coefplot

        env_data = _to_dataframe(env_vars)
        corr_data = env_data.join(self['Efficiency'])
        corr_mod = OLS.from_formula(
            "Efficiency ~ " + " + ".join(env_vars.columns), corr_data)
        corr_res = corr_mod.fit()

        #plot coeffs
        if coeff_plot:
            coefplot("Efficiency ~ " + " + ".join(env_vars.columns),
                     data=corr_data)
            plt.xticks(rotation=45, ha='right')
            plt.title('Regression coefficients and standard errors')

        #plot qq of residuals
        if qq_plot:
            qqplot(corr_res.resid, line='s')
            plt.title('Distribution of residuals')

        print(corr_res.summary())

        return corr_res
开发者ID:jameszuccollo,项目名称:pyDEA,代码行数:44,代码来源:dea.py

示例7: test_outlier_test

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import from_formula [as 别名]
def test_outlier_test():
    endog, exog, labels = get_duncan_data()
    ndarray_mod = OLS(endog, exog).fit()
    rstudent =  [3.1345185839, -2.3970223990,  2.0438046359, -1.9309187757,
                 1.8870465798, -1.7604905300, -1.7040324156,  1.6024285876,
                 -1.4332485037, -1.1044851583,  1.0688582315,  1.0185271840,
                 -0.9024219332, -0.9023876471, -0.8830953936,  0.8265782334,
                 0.8089220547,  0.7682770197,  0.7319491074, -0.6665962829,
                 0.5227352794, -0.5135016547,  0.5083881518,  0.4999224372,
                 -0.4980818221, -0.4759717075, -0.4293565820, -0.4114056499,
                 -0.3779540862,  0.3556874030,  0.3409200462,  0.3062248646,
                 0.3038999429, -0.3030815773, -0.1873387893,  0.1738050251,
                 0.1424246593, -0.1292266025,  0.1272066463, -0.0798902878,
                 0.0788467222,  0.0722556991,  0.0505098280,  0.0233215136,
                 0.0007112055]
    unadj_p = [0.003177202, 0.021170298, 0.047432955, 0.060427645, 0.066248120,
               0.085783008, 0.095943909, 0.116738318, 0.159368890, 0.275822623,
               0.291386358, 0.314400295, 0.372104049, 0.372122040, 0.382333561,
               0.413260793, 0.423229432, 0.446725370, 0.468363101, 0.508764039,
               0.603971990, 0.610356737, 0.613905871, 0.619802317, 0.621087703,
               0.636621083, 0.669911674, 0.682917818, 0.707414459, 0.723898263,
               0.734904667, 0.760983108, 0.762741124, 0.763360242, 0.852319039,
               0.862874018, 0.887442197, 0.897810225, 0.899398691, 0.936713197,
               0.937538115, 0.942749758, 0.959961394, 0.981506948, 0.999435989]
    bonf_p = [0.1429741, 0.9526634, 2.1344830, 2.7192440, 2.9811654, 3.8602354,
            4.3174759, 5.2532243, 7.1716001, 12.4120180, 13.1123861, 14.1480133,
            16.7446822, 16.7454918, 17.2050103, 18.5967357, 19.0453245,
            20.1026416, 21.0763395, 22.8943818, 27.1787396, 27.4660532,
            27.6257642, 27.8911043, 27.9489466, 28.6479487, 30.1460253,
            30.7313018, 31.8336506, 32.5754218, 33.0707100, 34.2442399,
            34.3233506, 34.3512109, 38.3543568, 38.8293308, 39.9348989,
            40.4014601, 40.4729411, 42.1520939, 42.1892152, 42.4237391,
            43.1982627, 44.1678127, 44.9746195]
    bonf_p = np.array(bonf_p)
    bonf_p[bonf_p > 1] = 1
    sorted_labels = ["minister", "reporter", "contractor", "insurance.agent",
            "machinist", "store.clerk", "conductor", "factory.owner",
            "mail.carrier", "streetcar.motorman", "carpenter", "coal.miner",
            "bartender", "bookkeeper", "soda.clerk", "chemist", "RR.engineer",
            "professor", "electrician", "gas.stn.attendant", "auto.repairman",
            "watchman", "banker", "machine.operator", "dentist", "waiter",
            "shoe.shiner", "welfare.worker", "plumber", "physician", "pilot",
            "engineer", "accountant", "lawyer", "undertaker", "barber",
            "store.manager", "truck.driver", "cook", "janitor", "policeman",
            "architect", "teacher", "taxi.driver", "author"]

    res2 = np.c_[rstudent, unadj_p, bonf_p]
    res = oi.outlier_test(ndarray_mod, method='b', labels=labels, order=True)
    np.testing.assert_almost_equal(res.values, res2, 7)
    np.testing.assert_equal(res.index.tolist(), sorted_labels)  # pylint: disable-msg=E1103

    data = pd.DataFrame(np.column_stack((endog, exog)),
                        columns='y const var1 var2'.split(),
                        index=labels)

    # check `order` with pandas bug in #3971
    res_pd = OLS.from_formula('y ~ const + var1 + var2 - 0', data).fit()

    res_outl2 = oi.outlier_test(res_pd, method='b', order=True)
    assert_almost_equal(res_outl2.values, res2, 7)
    assert_equal(res_outl2.index.tolist(), sorted_labels)

    res_outl1 = res_pd.outlier_test(method='b')
    res_outl1 = res_outl1.sort_values(['unadj_p'], ascending=True)
    assert_almost_equal(res_outl1.values, res2, 7)
    assert_equal(res_outl1.index.tolist(), sorted_labels)
    assert_array_equal(res_outl2.index, res_outl1.index)

    # additional keywords in method
    res_outl3 = res_pd.outlier_test(method='b', order=True)
    assert_equal(res_outl3.index.tolist(), sorted_labels)
    res_outl4 = res_pd.outlier_test(method='b', order=True, cutoff=0.15)
    assert_equal(res_outl4.index.tolist(), sorted_labels[:1])
开发者ID:bashtage,项目名称:statsmodels,代码行数:75,代码来源:test_diagnostic.py


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