本文整理汇总了Python中statsmodels.datasets.longley.load_pandas函数的典型用法代码示例。如果您正苦于以下问题：Python load_pandas函数的具体用法？Python load_pandas怎么用？Python load_pandas使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了load_pandas函数的12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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_formula_predict
def test_formula_predict():
from numpy import log
formula = """TOTEMP ~ log(GNPDEFL) + log(GNP) + UNEMP + ARMED +
POP + YEAR"""
data = load_pandas()
dta = load_pandas().data
results = ols(formula, dta).fit()
npt.assert_equal(results.fittedvalues, results.predict(data.exog))
开发者ID:Tskatom，项目名称:Embers_VT，代码行数:8，代码来源:test_formula.py
示例2: test_formula_predict
def test_formula_predict():
# `log` is needed in the namespace for patsy to find
from numpy import log # noqa:F401
formula = """TOTEMP ~ log(GNPDEFL) + log(GNP) + UNEMP + ARMED +
POP + YEAR"""
data = load_pandas()
dta = load_pandas().data
results = ols(formula, dta).fit()
npt.assert_almost_equal(results.fittedvalues.values,
results.predict(data.exog), 8)
开发者ID:bashtage，项目名称:statsmodels，代码行数:10，代码来源:test_formula.py
示例3: test_pandas_const_df_prepend
def test_pandas_const_df_prepend():
dta = longley.load_pandas().exog
# regression test for #1025
dta["UNEMP"] /= dta["UNEMP"].std()
dta = tools.add_constant(dta, prepend=True)
assert_string_equal("const", dta.columns[0])
assert_equal(dta.var(0)[0], 0)
开发者ID:nsolcampbell，项目名称:statsmodels，代码行数:7，代码来源:test_tools.py
示例4: test_summary_as_latex
def test_summary_as_latex():
# GH#734
import re
dta = longley.load_pandas()
X = dta.exog
X["constant"] = 1
y = dta.endog
res = OLS(y, X).fit()
with pytest.warns(UserWarning):
table = res.summary().as_latex()
# replace the date and time
table = re.sub("(?<=\n\\\\textbf\\{Date:\\} &).+?&",
" Sun, 07 Apr 2013 &", table)
table = re.sub("(?<=\n\\\\textbf\\{Time:\\} &).+?&",
" 13:46:07 &", table)
expected = """\\begin{center}
\\begin{tabular}{lclc}
\\toprule
\\textbf{Dep. Variable:} & TOTEMP & \\textbf{ R-squared: } & 0.995 \\\\
\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.992 \\\\
\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 330.3 \\\\
\\textbf{Date:} & Sun, 07 Apr 2013 & \\textbf{ Prob (F-statistic):} & 4.98e-10 \\\\
\\textbf{Time:} & 13:46:07 & \\textbf{ Log-Likelihood: } & -109.62 \\\\
\\textbf{No. Observations:} & 16 & \\textbf{ AIC: } & 233.2 \\\\
\\textbf{Df Residuals:} & 9 & \\textbf{ BIC: } & 238.6 \\\\
\\textbf{Df Model:} & 6 & \\textbf{ } & \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lcccccc}
& \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\
\\midrule
\\textbf{GNPDEFL} & 15.0619 & 84.915 & 0.177 & 0.863 & -177.029 & 207.153 \\\\
\\textbf{GNP} & -0.0358 & 0.033 & -1.070 & 0.313 & -0.112 & 0.040 \\\\
\\textbf{UNEMP} & -2.0202 & 0.488 & -4.136 & 0.003 & -3.125 & -0.915 \\\\
\\textbf{ARMED} & -1.0332 & 0.214 & -4.822 & 0.001 & -1.518 & -0.549 \\\\
\\textbf{POP} & -0.0511 & 0.226 & -0.226 & 0.826 & -0.563 & 0.460 \\\\
\\textbf{YEAR} & 1829.1515 & 455.478 & 4.016 & 0.003 & 798.788 & 2859.515 \\\\
\\textbf{constant} & -3.482e+06 & 8.9e+05 & -3.911 & 0.004 & -5.5e+06 & -1.47e+06 \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lclc}
\\textbf{Omnibus:} & 0.749 & \\textbf{ Durbin-Watson: } & 2.559 \\\\
\\textbf{Prob(Omnibus):} & 0.688 & \\textbf{ Jarque-Bera (JB): } & 0.684 \\\\
\\textbf{Skew:} & 0.420 & \\textbf{ Prob(JB): } & 0.710 \\\\
\\textbf{Kurtosis:} & 2.434 & \\textbf{ Cond. No. } & 4.86e+09 \\\\
\\bottomrule
\\end{tabular}
%\\caption{OLS Regression Results}
\\end{center}
Warnings: \\newline
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline
[2] The condition number is large, 4.86e+09. This might indicate that there are \\newline
strong multicollinearity or other numerical problems."""
assert_equal(table, expected)
开发者ID:statsmodels，项目名称:statsmodels，代码行数:56，代码来源:test_regression.py
示例5: test_tests
def test_tests():
formula = 'TOTEMP ~ GNPDEFL + GNP + UNEMP + ARMED + POP + YEAR'
dta = load_pandas().data
results = ols(formula, dta).fit()
test_formula = '(GNPDEFL = GNP), (UNEMP = 2), (YEAR/1829 = 1)'
LC = make_hypotheses_matrices(results, test_formula)
R = LC.coefs
Q = LC.constants
npt.assert_almost_equal(R, [[0, 1, -1, 0, 0, 0, 0],
[0, 0 , 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1./1829]], 8)
npt.assert_array_equal(Q, [[0],[2],[1]])
开发者ID:Tskatom，项目名称:Embers_VT，代码行数:12，代码来源:test_formula.py
示例6: test_summary
def test_summary():
# test 734
import re
dta = longley.load_pandas()
X = dta.exog
X["constant"] = 1
y = dta.endog
with warnings.catch_warnings(record=True):
res = OLS(y, X).fit()
table = res.summary().as_latex()
# replace the date and time
table = re.sub("(?<=\n\\\\textbf\{Date:\} &).+?&",
" Sun, 07 Apr 2013 &", table)
table = re.sub("(?<=\n\\\\textbf\{Time:\} &).+?&",
" 13:46:07 &", table)
expected = """\\begin{center}
\\begin{tabular}{lclc}
\\toprule
\\textbf{Dep. Variable:} & TOTEMP & \\textbf{ R-squared: } & 0.995 \\\\
\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.992 \\\\
\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 330.3 \\\\
\\textbf{Date:} & Sun, 07 Apr 2013 & \\textbf{ Prob (F-statistic):} & 4.98e-10 \\\\
\\textbf{Time:} & 13:46:07 & \\textbf{ Log-Likelihood: } & -109.62 \\\\
\\textbf{No. Observations:} & 16 & \\textbf{ AIC: } & 233.2 \\\\
\\textbf{Df Residuals:} & 9 & \\textbf{ BIC: } & 238.6 \\\\
\\textbf{Df Model:} & 6 & \\textbf{ } & \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lccccc}
& \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$>$$|$t$|$} & \\textbf{[95.0\\% Conf. Int.]} \\\\
\\midrule
\\textbf{GNPDEFL} & 15.0619 & 84.915 & 0.177 & 0.863 & -177.029 207.153 \\\\
\\textbf{GNP} & -0.0358 & 0.033 & -1.070 & 0.313 & -0.112 0.040 \\\\
\\textbf{UNEMP} & -2.0202 & 0.488 & -4.136 & 0.003 & -3.125 -0.915 \\\\
\\textbf{ARMED} & -1.0332 & 0.214 & -4.822 & 0.001 & -1.518 -0.549 \\\\
\\textbf{POP} & -0.0511 & 0.226 & -0.226 & 0.826 & -0.563 0.460 \\\\
\\textbf{YEAR} & 1829.1515 & 455.478 & 4.016 & 0.003 & 798.788 2859.515 \\\\
\\textbf{constant} & -3.482e+06 & 8.9e+05 & -3.911 & 0.004 & -5.5e+06 -1.47e+06 \\\\
\\bottomrule
\\end{tabular}
\\begin{tabular}{lclc}
\\textbf{Omnibus:} & 0.749 & \\textbf{ Durbin-Watson: } & 2.559 \\\\
\\textbf{Prob(Omnibus):} & 0.688 & \\textbf{ Jarque-Bera (JB): } & 0.684 \\\\
\\textbf{Skew:} & 0.420 & \\textbf{ Prob(JB): } & 0.710 \\\\
\\textbf{Kurtosis:} & 2.434 & \\textbf{ Cond. No. } & 4.86e+09 \\\\
\\bottomrule
\\end{tabular}
%\\caption{OLS Regression Results}
\\end{center}"""
assert_equal(table, expected)
开发者ID:Honglang，项目名称:statsmodels，代码行数:51，代码来源:test_regression.py
示例7: setupClass
def setupClass(cls):
data = dict((k, v.tolist()) for k, v in load_pandas().data.iteritems())
cls.model = ols(longley_formula, data)
super(TestFormulaDict, cls).setupClass()
开发者ID:Tskatom，项目名称:Embers_VT，代码行数:4，代码来源:test_formula.py
示例8: print
res3 = sm.OLS(y, X).fit()
print(res3.f_test(R))
print(res3.f_test("x2 = x3 = 0"))
# ### Multicollinearity
#
# The Longley dataset is well known to have high multicollinearity. That is, the exogenous predictors are highly correlated. This is problematic because it can affect the stability of our coefficient estimates as we make minor changes to model specification.
from statsmodels.datasets.longley import load_pandas
y = load_pandas().endog
X = load_pandas().exog
X = sm.add_constant(X)
# Fit and summary:
ols_model = sm.OLS(y, X)
ols_results = ols_model.fit()
print(ols_results.summary())
# #### Condition number
#
# One way to assess multicollinearity is to compute the condition number. Values over 20 are worrisome (see Greene 4.9). The first step is to normalize the independent variables to have unit length:
开发者ID:BranYang，项目名称:statsmodels，代码行数:29，代码来源:ols.py
示例9: test_pandas_const_df
def test_pandas_const_df():
dta = longley.load_pandas().exog
dta = tools.add_constant(dta, prepend=False)
assert_string_equal("const", dta.columns[-1])
assert_equal(dta.var(0)[-1], 0)
开发者ID:nsolcampbell，项目名称:statsmodels，代码行数:5，代码来源:test_tools.py
示例10: test_pandas_const_series_prepend
def test_pandas_const_series_prepend():
dta = longley.load_pandas()
series = dta.exog["GNP"]
series = tools.add_constant(series, prepend=True)
assert_string_equal("const", series.columns[0])
assert_equal(series.var(0)[0], 0)
开发者ID:nsolcampbell，项目名称:statsmodels，代码行数:6，代码来源:test_tools.py
示例11: test_pandas_const_series
def test_pandas_const_series():
dta = longley.load_pandas()
series = dta.exog['GNP']
series = tools.add_constant(series, prepend=False)
assert_string_equal('const', series.columns[1])
assert_equal(series.var(0)[1], 0)
开发者ID:bfcondon，项目名称:statsmodels，代码行数:6，代码来源:test_tools.py
示例12: test_pandas_const_df_prepend
def test_pandas_const_df_prepend():
dta = longley.load_pandas().exog
dta = tools.add_constant(dta, prepend=True)
assert_string_equal('const', dta.columns[0])
assert_equal(dta.var(0)[0], 0)
开发者ID:dengemann，项目名称:statsmodels，代码行数:5，代码来源:test_tools.py
注：本文中的statsmodels.datasets.longley.load_pandas函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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