本文整理汇总了Python中statsmodels.datasets.longley.load函数的典型用法代码示例。如果您正苦于以下问题：Python load函数的具体用法？Python load怎么用？Python load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函数的13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: setupClass
def setupClass(cls):
R = np.zeros(7)
R[4:6] = [1,-1]
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
res1 = OLS(data.endog, data.exog).fit()
cls.Ttest1 = res1.t_test(R)示例2: setupClass
def setupClass(cls):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
ols_res = OLS(data.endog, data.exog).fit()
gls_res = GLS(data.endog, data.exog).fit()
cls.res1 = gls_res
cls.res2 = ols_res示例3: test_missing
def test_missing(self):
data = longley.load()
data.exog = add_constant(data.exog, prepend=False)
data.endog[[3, 7, 14]] = np.nan
mod = OLS(data.endog, data.exog, missing='drop')
assert_equal(mod.endog.shape[0], 13)
assert_equal(mod.exog.shape[0], 13)示例4: __init__
def __init__(self):
from statsmodels.datasets.cpunish import load
self.data = load()
self.endog = self.data.endog
self.exog = self.data.exog
np.random.seed(1234)
self.weight = np.random.randint(5, 100, len(self.endog))
self.endog_big = np.repeat(self.endog, self.weight)
self.exog_big = np.repeat(self.exog, self.weight, axis=0)示例5: setup_class
def setup_class(cls):
data = longley.load(as_pandas=False)
data.exog = add_constant(data.exog, prepend=False)
ols_res = OLS(data.endog, data.exog).fit()
gls_res = GLS(data.endog, data.exog).fit()
gls_res_scalar = GLS(data.endog, data.exog, sigma=1)
cls.endog = data.endog
cls.exog = data.exog
cls.res1 = gls_res
cls.res2 = ols_res
cls.res3 = gls_res_scalar示例6: setupClass
def setupClass(cls):
from results.results_glm import Cpunish
from statsmodels.datasets.cpunish import load
data = load()
data.exog[:,3] = np.log(data.exog[:,3])
data.exog = add_constant(data.exog)
exposure = [100] * len(data.endog)
cls.res1 = GLM(data.endog, data.exog, family=sm.families.Poisson(),
exposure=exposure).fit()
cls.res1.params[-1] += np.log(100) # add exposure back in to param
# to make the results the same
cls.res2 = Cpunish()示例7: setupClass
def setupClass(cls):
from results.results_regression import LongleyGls
data = longley.load()
exog = add_constant(np.column_stack(\
(data.exog[:,1],data.exog[:,4])))
tmp_results = OLS(data.endog, exog).fit()
rho = np.corrcoef(tmp_results.resid[1:],
tmp_results.resid[:-1])[0][1] # by assumption
order = toeplitz(np.arange(16))
sigma = rho**order
GLS_results = GLS(data.endog, exog, sigma=sigma).fit()
cls.res1 = GLS_results
cls.res2 = LongleyGls()示例8: __init__
def __init__(self):
'''
Tests Poisson family with canonical log link.
Test results were obtained by R.
'''
from results.results_glm import Cpunish
from statsmodels.datasets.cpunish import load
self.data = load()
self.data.exog[:,3] = np.log(self.data.exog[:,3])
self.data.exog = add_constant(self.data.exog)
self.res1 = GLM(self.data.endog, self.data.exog,
family=sm.families.Poisson()).fit()
self.res2 = Cpunish()示例9: setupClass
def setupClass(cls):
# if skipR:
# raise SkipTest, "Rpy not installed"
# try:
# r.library('car')
# except RPyRException:
# raise SkipTest, "car library not installed for R"
R = np.zeros(7)
R[4:6] = [1,-1]
# self.R = R
data = longley.load()
data.exog = add_constant(data.exog)
res1 = OLS(data.endog, data.exog).fit()
cls.Ttest1 = res1.t_test(R)示例10: __init__
def __init__(self):
'''
Tests Poisson family with canonical log link.
Test results were obtained by R.
'''
from .results.results_glm import Cpunish
from statsmodels.datasets.cpunish import load
self.data = load()
self.data.exog[:,3] = np.log(self.data.exog[:,3])
self.data.exog = add_constant(self.data.exog, prepend=False)
self.res1 = GLM(self.data.endog, self.data.exog,
family=sm.families.Poisson()).fit()
self.res2 = Cpunish()
# compare with discrete, start close to save time
modd = discrete.Poisson(self.data.endog, self.data.exog)
self.resd = modd.fit(start_params=self.res1.params * 0.9, disp=False)示例11: test_wtd_patsy_missing
def test_wtd_patsy_missing():
from statsmodels.datasets.cpunish import load
import pandas as pd
data = load()
data.exog[0, 0] = np.nan
data.endog[[2, 4, 6, 8]] = np.nan
data.pandas = pd.DataFrame(data.exog, columns=data.exog_name)
data.pandas['EXECUTIONS'] = data.endog
weights = np.arange(1, len(data.endog)+1)
formula = """EXECUTIONS ~ INCOME + PERPOVERTY + PERBLACK + VC100k96 +
SOUTH + DEGREE"""
mod_misisng = GLM.from_formula(formula, data=data.pandas, freq_weights=weights)
assert_equal(mod_misisng.freq_weights.shape[0],
mod_misisng.endog.shape[0])
assert_equal(mod_misisng.freq_weights.shape[0],
mod_misisng.exog.shape[0])
assert_equal(mod_misisng.freq_weights.shape[0], 12)
keep_weights = np.array([ 2, 4, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17])
assert_equal(mod_misisng.freq_weights, keep_weights)示例12: load
"""Example: statsmodels.OLS
"""
from statsmodels.datasets.longley import load
import statsmodels.api as sm
from statsmodels.iolib.table import SimpleTable, default_txt_fmt
import numpy as np
data = load()
data_orig = (data.endog.copy(), data.exog.copy())
# Note: In this example using zscored/standardized variables has no effect on
# regression estimates. Are there no numerical problems?
rescale = 0
# 0: no rescaling, 1:demean, 2:standardize, 3:standardize and transform back
rescale_ratio = data.endog.std() / data.exog.std(0)
if rescale > 0:
# rescaling
data.endog -= data.endog.mean()
data.exog -= data.exog.mean(0)
if rescale > 1:
data.endog /= data.endog.std()
data.exog /= data.exog.std(0)
# skip because mean has been removed, but dimension is hardcoded in table
data.exog = sm.tools.add_constant(data.exog, prepend=False)
ols_model = sm.OLS(data.endog, data.exog)示例13: setupClass
def setupClass(cls):
cls.data = load()