def test_default_time(self):
"""
Check that the time defaults work correctly.
"""
endog, exog, group = load_data("gee_logistic_1.csv")
# Time values for the autoregressive model
T = np.zeros(len(endog))
idx = set(group)
for ii in idx:
jj = np.flatnonzero(group == ii)
T[jj] = lrange(len(jj))
family = Binomial()
va = Autoregressive()
md1 = GEE(endog, exog, group, family=family, cov_struct=va)
mdf1 = md1.fit()
md2 = GEE(endog, exog, group, time=T, family=family, cov_struct=va)
mdf2 = md2.fit()
assert_almost_equal(mdf1.params, mdf2.params, decimal=6)
assert_almost_equal(mdf1.standard_errors(),
mdf2.standard_errors(),
decimal=6)
def test_formulas(self):
"""
Check formulas, especially passing groups and time as either
variable names or arrays.
"""
n = 100
Y = np.random.normal(size=n)
X1 = np.random.normal(size=n)
mat = np.concatenate((np.ones((n, 1)), X1[:, None]), axis=1)
Time = np.random.uniform(size=n)
groups = np.kron(lrange(20), np.ones(5))
data = pd.DataFrame({"Y": Y, "X1": X1, "Time": Time, "groups": groups})
va = Autoregressive()
family = Gaussian()
mod1 = GEE(Y, mat, groups, time=Time, family=family, cov_struct=va)
rslt1 = mod1.fit()
mod2 = GEE.from_formula("Y ~ X1",
groups,
data,
time=Time,
family=family,
cov_struct=va)
rslt2 = mod2.fit()
mod3 = GEE.from_formula("Y ~ X1",
groups,
data,
time="Time",
family=family,
cov_struct=va)
rslt3 = mod3.fit()
mod4 = GEE.from_formula("Y ~ X1",
"groups",
data,
time=Time,
family=family,
cov_struct=va)
rslt4 = mod4.fit()
mod5 = GEE.from_formula("Y ~ X1",
"groups",
data,
time="Time",
family=family,
cov_struct=va)
rslt5 = mod5.fit()
assert_almost_equal(rslt1.params, rslt2.params, decimal=8)
assert_almost_equal(rslt1.params, rslt3.params, decimal=8)
assert_almost_equal(rslt1.params, rslt4.params, decimal=8)
assert_almost_equal(rslt1.params, rslt5.params, decimal=8)
check_wrapper(rslt2)
def gendat_ar1():
ars = AR_simulator()
ars.ngroups = 200
ars.params = np.r_[0, -0.8, 1.2, 0, 0.5]
ars.error_sd = 2
ars.dparams = [ar,]
ars.simulate()
return ars, Autoregressive()
def gendat_ar0(msg = False):
ars = AR_simulator()
ars.ngroups = 200
ars.params = np.r_[0, -1, 1, 0, 0.5]
ars.error_sd = 2
ars.dparams = [ar,]
ars.simulate()
return ars, Autoregressive()
def test_autoregressive(self):
dep_params_true = [0, 0.589208623896, 0.559823804948]
params_true = [[1.08043787, 1.12709319, 0.90133927],
[0.9613677, 1.05826987, 0.90832055],
[1.05370439, 0.96084864, 0.93923374]]
np.random.seed(342837482)
num_group = 100
ar_param = 0.5
k = 3
ga = Gaussian()
for gsize in 1, 2, 3:
ix = np.arange(gsize)[:, None] - np.arange(gsize)[None, :]
ix = np.abs(ix)
cmat = ar_param**ix
cmat_r = np.linalg.cholesky(cmat)
endog = []
exog = []
groups = []
for i in range(num_group):
x = np.random.normal(size=(gsize, k))
exog.append(x)
expval = x.sum(1)
errors = np.dot(cmat_r, np.random.normal(size=gsize))
endog.append(expval + errors)
groups.append(i * np.ones(gsize))
endog = np.concatenate(endog)
groups = np.concatenate(groups)
exog = np.concatenate(exog, axis=0)
ar = Autoregressive()
md = GEE(endog, exog, groups, family=ga, cov_struct=ar)
mdf = md.fit()
assert_almost_equal(ar.dep_params, dep_params_true[gsize - 1])
assert_almost_equal(mdf.params, params_true[gsize - 1])
def test_logistic(self):
"""
R code for comparing results:
library(gee)
Z = read.csv("results/gee_logistic_1.csv", header=FALSE)
Y = Z[,2]
Id = Z[,1]
X1 = Z[,3]
X2 = Z[,4]
X3 = Z[,5]
mi = gee(Y ~ X1 + X2 + X3, id=Id, family=binomial,
corstr="independence")
smi = summary(mi)
u = coefficients(smi)
cfi = paste(u[,1], collapse=",")
sei = paste(u[,4], collapse=",")
me = gee(Y ~ X1 + X2 + X3, id=Id, family=binomial,
corstr="exchangeable")
sme = summary(me)
u = coefficients(sme)
cfe = paste(u[,1], collapse=",")
see = paste(u[,4], collapse=",")
ma = gee(Y ~ X1 + X2 + X3, id=Id, family=binomial,
corstr="AR-M")
sma = summary(ma)
u = coefficients(sma)
cfa = paste(u[,1], collapse=",")
sea = paste(u[,4], collapse=",")
sprintf("cf = [[%s],[%s],[%s]]", cfi, cfe, cfa)
sprintf("se = [[%s],[%s],[%s]]", sei, see, sea)
"""
endog, exog, group = load_data("gee_logistic_1.csv")
# Time values for the autoregressive model
T = np.zeros(len(endog))
idx = set(group)
for ii in idx:
jj = np.flatnonzero(group == ii)
T[jj] = lrange(len(jj))
family = Binomial()
ve = Exchangeable()
vi = Independence()
va = Autoregressive()
# From R gee
cf = [[
0.0167272965285882, 1.13038654425893, -1.86896345082962,
1.09397608331333
],
[
0.0178982283915449, 1.13118798191788, -1.86133518416017,
1.08944256230299
],
[
0.0109621937947958, 1.13226505028438, -1.88278757333046,
1.09954623769449
]]
se = [[
0.127291720283049, 0.166725808326067, 0.192430061340865,
0.173141068839597
],
[
0.127045031730155, 0.165470678232842, 0.192052750030501,
0.173174779369249
],
[
0.127240302296444, 0.170554083928117, 0.191045527104503,
0.169776150974586
]]
for j, v in enumerate((vi, ve, va)):
md = GEE(endog, exog, group, T, family, v)
mdf = md.fit()
if id(v) != id(va):
assert_almost_equal(mdf.params, cf[j], decimal=6)
assert_almost_equal(mdf.standard_errors(), se[j], decimal=6)
# Test with formulas
D = np.concatenate((endog[:, None], group[:, None], exog[:, 1:]),
axis=1)
D = pd.DataFrame(D)
D.columns = [
"Y",
"Id",
] + ["X%d" % (k + 1) for k in range(exog.shape[1] - 1)]
for j, v in enumerate((vi, ve)):
md = GEE.from_formula("Y ~ X1 + X2 + X3",
"Id",
D,
family=family,
cov_struct=v)
mdf = md.fit()
assert_almost_equal(mdf.params, cf[j], decimal=6)
assert_almost_equal(mdf.standard_errors(), se[j], decimal=6)
# model comparison with likelihood ratio test
LR = 2 * (model_panel2_results.llf - model_panel1_results.llf)
p = chi2.sf(LR, 2)
print('p: %.30f' % p)
# provides a summary of the number of zeros
print(US_cases_long_demogr_week['cases_count_pos'].describe())
print(US_cases_long_demogr_week['cases_count_pos'].value_counts())
count_total = sum(US_cases_long_demogr_week['cases_count_pos'].value_counts().to_dict().values())
count_zero = US_cases_long_demogr_week['cases_count_pos'].value_counts()[0.0]
print("Count of zero is {}, about {:.4f} of the data.".format(count_zero, count_zero / count_total ))
# Approach one to generalized linear models for panel data: Generalized Estimating Equations
# poisson model
poi=Poisson()
ar=Autoregressive()
gee_model0 = GEE.from_formula("cases_count_pos ~ week_of_year + percent_age65over + percent_female + percent_black", groups="state", \
data=US_cases_long_demogr_week, time='week_of_year', cov_struct=ar, family=poi, offset=np.log(np.asarray(US_cases_long_demogr_week["pop_count_2019"])))
gee_model0_results = gee_model0.fit(maxiter=200)
print(gee_model0_results.summary())
print(ar.summary())
print("scale=%.2f" % (gee_model0_results.scale))
# There is warning -- "IterationLimitWarning: Iteration limit reached prior to convergence" even if I specify maxiter = 2000. So, in this case,
# specific starting values are needed to get the estimating algorithm to converge.
# First run with exchangeable dependence structure. We know from this model that the within-state correlation is roughly 0.077.
fam = Poisson()
ex = Exchangeable()
ex_model = GEE.from_formula("cases_count_pos ~ week_of_year + percent_age65over + percent_female + percent_black", groups="state", \
data=US_cases_long_demogr_week, cov_struct=ex, family=fam, offset=np.log(np.asarray(US_cases_long_demogr_week["pop_count_2019"])))
ex_results = ex_model.fit()