def __init__(self, X, dist='OLS', alphas=[0.05, 0.01], log=True):
self.X, self.xLen, self.dist, self.permute, self.zero_prob, self.alphas, self.dfd, self.dfn = X, len(
X.names), dist, self.permute_REG, 0.0, alphas, len(
X.names) - 1, len(X.array) - len(X.names)
#F_KEY = {'TW': sfams.Tweedie(link=slinks.log), 'PO': sfams.Poisson(link=slinks.log), 'NB': sfams.NegativeBinomial(link=slinks.log), 'GA': sfams.Gamma(link=slinks.log), 'NO': sfams.Gaussian(link=slinks.log)}
F_KEY = {
'TW': sfams.Tweedie(),
'PO': sfams.Poisson(),
'NB': sfams.NegativeBinomial(),
'GA': sfams.Gamma(),
'NO': sfams.Gaussian()
}
if self.dist.upper() == 'OLS':
self.reg, self.execute = sm.OLS, self.execute_REG
elif self.dist.upper()[0] == 'G':
self.reg, self.execute, self.family = scm.ZeroInflatedNegativeBinomialP, self.execute_GIN, F_KEY[
'NB']
elif self.dist.upper()[0] != 'Z':
self.execute, self.permute, self.family = self.execute_GLM, self.permute_GLM, F_KEY[
self.dist.upper()[0:2]]
else:
if self.dist.upper()[0:3] in ['ZIP', 'ZPO']:
self.reg, self.execute, self.family = CUSTOM_ZPO, self.execute_ZIN, F_KEY[
'PO']
elif self.dist.upper()[0:3] in ['ZIN', 'ZNB']:
self.reg, self.execute, self.family = CUSTOM_ZNB, self.execute_ZIN, F_KEY[
'NB']
elif self.dist.upper()[0:3] in ['ZGP', 'ZGP']:
self.reg, self.execute, self.family = CUSTOM_ZGP, self.execute_ZIN, F_KEY[
'GP']
def multiple_linear_regression():
'''Multiple linear regression
chapter 6.3, p. 98'''
# get the data from the web
inFile = r'GLM_data/Table 6.3 Carbohydrate diet.xls'
df = get_data(inFile)
# do the fit, for the original model ...
model = smf.ols('carbohydrate ~ age + weight + protein', data=df).fit()
print(model.summary())
print(sm_stats.anova_lm(model))
# as GLM
glm = smf.glm('carbohydrate ~ age + weight + protein',
family=sm_families.Gaussian(), data=df).fit()
print('Same model, calculated with GLM')
''' The confidence intervals are different than those from OLS.
The reason (from Nathaniel Smith):
OLS uses a method that gives exact results, but only works in the special
case where all the usual OLS criteria apply - iid Gaussian noise etc. GLM
instead uses an approximate method which is correct asymptotically but may
be off for small samples; the tradeoff you get in return is that this method
works the same way for all GLM models, including those with non-Gaussian
error terms and non-trivial link functions. So that's why they're different.
'''
print(glm.summary())
# ... and for model 1
model1 = smf.ols('carbohydrate ~ weight + protein', data=df).fit()
print(model1.summary())
print(sm_stats.anova_lm(model1))
def setup_class(cls):
cls.cov_type = 'HC0'
mod1 = GLM(endog, exog, family=families.Gaussian())
cls.res1 = mod1.fit(cov_type='HC0')
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='HC0')
def setup_class(cls):
cls.cov_type = 'cluster'
mod1 = GLM(endog, exog, family=families.Gaussian())
cls.res1 = mod1.fit(cov_type='cluster', cov_kwds=dict(groups=group))
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='cluster', cov_kwds=dict(groups=group))
def setup_class(cls):
endog_bin = (endog > endog.mean()).astype(int)
cls.cov_type = 'cluster'
mod1 = GLM(endog_bin, exog, family=families.Gaussian(link=links.CDFLink()))
cls.res1 = mod1.fit(cov_type='cluster', cov_kwds=dict(groups=group))
mod1 = smd.Probit(endog_bin, exog)
cls.res2 = mod1.fit(cov_type='cluster', cov_kwds=dict(groups=group))
def setup_class(cls):
cls.cov_type = 'HAC'
kwds = {'maxlags': 2}
mod1 = GLM(endog, exog, family=families.Gaussian())
cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds)
def setup_class(cls):
nobs, k_vars = 500, 5
np.random.seed(786452)
x = np.random.randn(nobs, k_vars)
x[:, 0] = 1
x2 = np.random.randn(nobs, 2)
xx = np.column_stack((x, x2))
if cls.dispersed:
het = np.random.randn(nobs)
y = np.random.randn(nobs) + x.sum(1) * 0.5 + het
#y_mc = np.random.negative_binomial(np.exp(x.sum(1) * 0.5), 2)
else:
y = np.random.randn(nobs) + x.sum(1) * 0.5
cls.exog_extra = x2
cls.model_full = GLM(y, xx, family=families.Gaussian())
cls.model_drop = GLM(y, x, family=families.Gaussian())
def __init__(self,
endog,
exog,
smoothers=None,
family=families.Gaussian()):
#self.family = family
#TODO: inconsistent super __init__
AdditiveModel.__init__(self, exog, smoothers=smoothers, family=family)
GLM.__init__(self, endog, exog, family=family)
assert self.family is family #make sure we got the right family
def setup_class(cls):
cls.cov_type = 'HAC'
# check kernel specified as string
kwds = {'kernel': 'bartlett', 'maxlags': 2}
mod1 = GLM(endog, exog, family=families.Gaussian())
cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
mod2 = OLS(endog, exog)
kwds2 = {'maxlags': 2}
cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds2)
def setup_class(cls):
cls.cov_type = 'HAC'
kwds={'kernel': sw.weights_uniform, 'maxlags': 2}
mod1 = GLM(endog, exog, family=families.Gaussian())
cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
# check kernel as string
mod2 = OLS(endog, exog)
kwds2 = {'kernel': 'uniform', 'maxlags': 2}
cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds)
def setup_class(cls):
cls.cov_type = 'HAC'
kwds = {'kernel': sw.weights_uniform, 'maxlags': 2}
mod1 = GLM(endog, exog, family=families.Gaussian())
cls.res1 = mod1.fit(cov_type='HAC', cov_kwds=kwds)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='HAC', cov_kwds=kwds)
#for debugging
cls.res3 = mod2.fit(cov_type='HAC', cov_kwds={'maxlags': 2})
def setup_class(cls):
cls.cov_type = 'hac-groupsum'
# time index is just made up to have a test case
time = np.tile(np.arange(7), 5)[:-1]
mod1 = GLM(endog, exog, family=families.Gaussian())
kwds = dict(time=time,
maxlags=2,
use_correction='hac',
df_correction=False)
cls.res1 = mod1.fit(cov_type='hac-groupsum', cov_kwds=kwds)
cls.res1b = mod1.fit(cov_type='nw-groupsum', cov_kwds=kwds)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='hac-groupsum', cov_kwds=kwds)
def setup_class(cls):
cls.cov_type = 'hac-panel'
# time index is just made up to have a test case
groups = np.repeat(np.arange(5), 7)[:-1]
mod1 = GLM(endog.copy(), exog.copy(), family=families.Gaussian())
kwds = dict(groups=groups,
maxlags=2,
kernel=sw.weights_uniform,
use_correction='hac',
df_correction=False)
cls.res1 = mod1.fit(cov_type='hac-panel', cov_kwds=kwds)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='hac-panel', cov_kwds=kwds)
def setup_class(cls):
import statsmodels.stats.sandwich_covariance as sw
cls.cov_type = 'hac-panel'
# time index is just made up to have a test case
time = np.tile(np.arange(7), 5)[:-1]
mod1 = GLM(endog.copy(), exog.copy(), family=families.Gaussian())
kwds = dict(time=time,
maxlags=2,
kernel=sw.weights_uniform,
use_correction='hac',
df_correction=False)
cls.res1 = mod1.fit(cov_type='hac-panel', cov_kwds=kwds)
cls.res1b = mod1.fit(cov_type='nw-panel', cov_kwds=kwds)
mod2 = OLS(endog, exog)
cls.res2 = mod2.fit(cov_type='hac-panel', cov_kwds=kwds)
def _check_inputs(self, family, offset, exposure, endog):
# Default family is Gaussian
if family is None:
family = families.Gaussian()
self.family = family
if exposure is not None:
if not isinstance(self.family.link, families.links.Log):
raise ValueError("exposure can only be used with the log "
"link function")
elif exposure.shape[0] != endog.shape[0]:
raise ValueError("exposure is not the same length as endog")
if offset is not None:
if offset.shape[0] != endog.shape[0]:
raise ValueError("offset is not the same length as endog")
def __init__(self, exog, smoothers=None, weights=None, family=None):
self.exog = exog
if not weights is None:
self.weights = weights
else:
self.weights = np.ones(self.exog.shape[0])
self.smoothers = smoothers or [default_smoother(exog[:,i]) for i in range(exog.shape[1])]
#TODO: why do we set here df, refactoring temporary?
for i in range(exog.shape[1]):
self.smoothers[i].df = 10
if family is None:
self.family = families.Gaussian()
else:
self.family = family
def __init__(self,
endog,
exog,
groups,
family=None,
cov_struct=None,
missing='none',
**kwargs):
# Handle the family argument
if family is None:
family = families.Gaussian()
else:
if not issubclass(family.__class__, families.Family):
raise ValueError("QIF: `family` must be a genmod "
"family instance")
self.family = family
self._fit_history = defaultdict(list)
# Handle the cov_struct argument
if cov_struct is None:
cov_struct = QIFIndependence()
else:
if not isinstance(cov_struct, QIFCovariance):
raise ValueError(
"QIF: `cov_struct` must be a QIFCovariance instance")
self.cov_struct = cov_struct
groups = np.asarray(groups)
super(QIF, self).__init__(endog,
exog,
groups=groups,
missing=missing,
**kwargs)
self.group_names = list(set(groups))
self.nobs = len(self.endog)
groups_ix = defaultdict(list)
for i, g in enumerate(groups):
groups_ix[g].append(i)
self.groups_ix = [groups_ix[na] for na in self.group_names]
self._check_args(groups)
def setup_class(cls):
vs = Independence()
family = families.Gaussian()
np.random.seed(987126)
Y = np.random.normal(size=100)
X1 = np.random.normal(size=100)
X2 = np.random.normal(size=100)
X3 = np.random.normal(size=100)
groups = np.kron(np.arange(20), np.ones(5))
D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3})
md = GEE.from_formula("Y ~ X1 + X2 + X3",
groups,
D,
family=family,
cov_struct=vs)
cls.result1 = md.fit()
cls.result2 = GLM.from_formula("Y ~ X1 + X2 + X3", data=D).fit()
def anova():
'''ANOVA
chapter 6.4, p. 108, and p. 113
GLM does not work with anova_lm.
'''
# get the data from the web
inFile = r'GLM_data/Table 6.6 Plant experiment.xls'
df = get_data(inFile)
# fit the model (p 109)
glm = smf.glm('weight~group', family=sm_families.Gaussian(), data=df)
print(glm.fit().summary())
print('-'*65)
print('OLS')
model = smf.ols('weight~group', data=df)
print(model.fit().summary())
print(sm_stats.anova_lm(model.fit()))
# The model corresponding to the null hypothesis of no treatment effect is
model0 = smf.ols('weight~1', data=df)
# Get the data for the two-factor ANOVA (p 113)
inFile = r'GLM_data/Table 6.9 Two-factor data.xls'
df = get_data(inFile)
# adjust the header names from the Excel-file
df.columns = ['A','B', 'data']
# two-factor anova, with interactions
ols_int = smf.ols('data~A*B', data=df)
sm_stats.anova_lm(ols_int.fit())
# The python commands for the other four models are
ols_add = smf.ols('data~A+B', data=df)
ols_A = smf.ols('data~A', data=df)
ols_B = smf.ols('data~B', data=df)
ols_mean = smf.ols('data~1', data=df)
import numpy as np
from numpy.testing import assert_allclose
import pandas as pd
import pytest
from statsmodels.genmod.qif import (QIF, QIFIndependence, QIFExchangeable,
QIFAutoregressive)
from statsmodels.tools.numdiff import approx_fprime
from statsmodels.genmod import families
@pytest.mark.parametrize(
"fam", [families.Gaussian(),
families.Poisson(),
families.Binomial()])
@pytest.mark.parametrize(
"cov_struct", [QIFIndependence(),
QIFExchangeable(),
QIFAutoregressive()])
def test_qif_numdiff(fam, cov_struct):
# Test the analytic scores against numeric derivatives
np.random.seed(234234)
n = 200
q = 4
x = np.random.normal(size=(n, 3))
if isinstance(fam, families.Gaussian):
e = np.kron(np.random.normal(size=n // q), np.ones(q))
e = np.sqrt(0.5) * e + np.sqrt(1 - 0.5**2) * np.random.normal(size=n)
y = x.sum(1) + e
elif isinstance(fam, families.Poisson):
y = np.random.poisson(5, size=n)
def __init__(self, endog, ndim, offset=None, family=None, penmat=None):
"""
Fit a generalized principal component analysis.
This analysis fits a generalized linear model (GLM) to a
rectangular data array. The linear predictor, which in a GLM
would be derived from covariates, is instead represented as a
factor-structured matrix. If endog is n x p and we wish to
extract d factors, then the linear predictor is represented as
1*icept' + (s - 1*icept')*F*F', where 1 is a column vector of
n 1's, s is a n x p matrix containing the 'saturated' linear
predictor, and F is a p x d orthogonal matrix of loadings.
Parameters
----------
endog : array-like
The data to which a reduced-rank structure is fit.
ndim : integer
The dimension of the low-rank structure.
family : GLM family instance
The GLM family to use in the analysis
offset : array-like
An optional offset vector
Returns
-------
A GPCAResults instance.
Notes
-----
Estimation uses the Grassmann optimization approach of Edelman,
rather than the approaches from Landgraf and Lee.
References
----------
A. Landgraf, Y.Lee (2019). Generalized Principal Component Analysis:
Projection of saturated model parameters. Technometrics.
https://www.asc.ohio-state.edu/lee.2272/mss/tr890.pdf
Edelman,Arias, Smith (1999). The geometry of algorithms with orthogonality
constraints.
https://arxiv.org/abs/physics/9806030
"""
if family is None:
# Default family
family = families.Gaussian()
self.family = family
self.endog = np.asarray(endog)
self.ndim = ndim
if offset is not None:
if offset.shape != endog.shape:
msg = "endog and offset must have the same shape"
raise ValueError(msg)
self.offset = np.asarray(offset)
if penmat is not None:
pm = []
if len(penmat) != 2:
msg = "penmat must be a tuple of length 2"
raise ValueError(msg)
for j in range(2):
if np.isscalar(penmat[j]):
n, p = endog.shape
pm.append(self._gen_penmat(penmat[j], n, p))
else:
pm.append(penmat[j])
self.penmat = pm
# Calculate the saturated parameter
if isinstance(family, families.Poisson):
satparam = np.where(endog != 0, np.log(endog), -3)
elif isinstance(family, families.Binomial):
satparam = np.where(endog == 1, 3, -3)
elif isinstance(family, families.Gaussian):
satparam = endog
else:
raise ValueError("Unknown family")
self.satparam = satparam
