def test_more_than_one_glm_is_ok(self):
with Model():
glm.glm('y ~ x', self.data_logistic,
family=glm.families.Binomial(link=glm.families.logit),
name='glm1')
glm.glm('y ~ x', self.data_logistic,
family=glm.families.Binomial(link=glm.families.logit),
name='glm2')
def test_glm_link_func(self):
with Model() as model:
glm.glm('y ~ x', self.data_logistic,
family=glm.families.Binomial(link=glm.families.logit))
step = Slice(model.vars)
trace = sample(1000, step, progressbar=False, random_seed=self.random_seed)
self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept, 1)
self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)
def test_glm(self):
with Model() as model:
glm.glm('y ~ x', self.data_linear)
step = Slice(model.vars)
trace = sample(500, step, progressbar=False, random_seed=self.random_seed)
self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept, 1)
self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)
self.assertAlmostEqual(np.mean(trace['sd']), self.sd, 1)
def test_more_than_one_glm_is_ok(self):
with Model():
glm.glm('y ~ x',
self.data_logistic,
family=glm.families.Binomial(link=glm.families.logit),
name='glm1')
glm.glm('y ~ x',
self.data_logistic,
family=glm.families.Binomial(link=glm.families.logit),
name='glm2')
def test_glm(self):
with Model() as model:
glm.glm('y ~ x', self.data_linear)
step = Slice(model.vars)
trace = sample(500,
step,
progressbar=False,
random_seed=self.random_seed)
self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept,
1)
self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)
self.assertAlmostEqual(np.mean(trace['sd']), self.sd, 1)
def test_glm_link_func(self):
with Model() as model:
glm.glm('y ~ x',
self.data_logistic,
family=glm.families.Binomial(link=glm.families.logit))
step = Slice(model.vars)
trace = sample(1000,
step,
progressbar=False,
random_seed=self.random_seed)
self.assertAlmostEqual(np.mean(trace['Intercept']), self.intercept,
1)
self.assertAlmostEqual(np.mean(trace['x']), self.slope, 1)
import scipy.optimize as opt
from pymc3 import Model, glm, find_MAP, sample
# Generate data
size = 50
true_intercept = 1
true_slope = 2
x = np.linspace(0, 1, size)
y = true_intercept + x*true_slope + np.random.normal(scale=.5, size=size)
data = dict(x=x, y=y)
with Model() as model:
glm.glm('y ~ x', data)
def run(n=2000):
if n == "short":
n = 50
import matplotlib.pyplot as plt
with model:
start = find_MAP(fmin=opt.fmin_powell)
trace = sample(n, Slice(), start=start)
plt.plot(x, y, 'x')
glm.plot_posterior_predictive(trace)
# plt.show()
import scipy.optimize as opt
from pymc3 import Model, glm, find_MAP, sample
# Generate data
size = 50
true_intercept = 1
true_slope = 2
x = np.linspace(0, 1, size)
y = true_intercept + x * true_slope + np.random.normal(scale=.5, size=size)
data = dict(x=x, y=y)
with Model() as model:
glm.glm('y ~ x', data)
def run(n=2000):
if n == "short":
n = 50
import matplotlib.pyplot as plt
with model:
start = find_MAP(fmin=opt.fmin_powell)
trace = sample(n, Slice(), start=start)
plt.plot(x, y, 'x')
glm.plot_posterior_predictive(trace)
# plt.show()
from pymc3.glm import glm
import pylab as plt
import pandas
from scipy.stats import uniform, norm
# Data
np.random.seed(1056) # set seed to replicate example
nobs= 250 # number of obs in model
x1 = uniform.rvs(size=nobs) # random uniform variable
beta0 = 2.0 # intercept
beta1 = 3.0 # angular coefficient
xb = beta0 + beta1 * x1 # linear predictor, xb
y = norm.rvs(loc=xb, scale=1.0, size=nobs) # create y as adjusted
# Fit
df = pandas.DataFrame({'x1': x1, 'y': y}) # re-write data
with Model() as model_glm:
glm('y ~ x1', df)
trace = sample(5000)
# Output
summary(trace)
# show graphical output
traceplot(trace)
plt.show()
import matplotlib.pyplot as plt
from pymc3 import *
from pymc3.glm import glm
import pandas as pd
import scipy
# Initialize random number generator
np.random.seed(123)
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
# Size of dataset
size = 100
# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.2
# Simulate outcome variable
Y = alpha + beta[0] * X1 + beta[1] * X2 + np.random.randn(size) * sigma
df = pd.DataFrame({'x1': X1, 'x2': X2, 'y': Y})
with Model() as model_glm:
glm('y ~ x1 + x2', df)
trace = sample(5000)
traceplot(trace)
from pymc3 import Model, sample, summary, traceplot
from pymc3.glm import glm
import pylab as plt
import pandas
from scipy.stats import uniform, norm
# Data
np.random.seed(1056) # set seed to replicate example
nobs = 250 # number of obs in model
x1 = uniform.rvs(size=nobs) # random uniform variable
beta0 = 2.0 # intercept
beta1 = 3.0 # angular coefficient
xb = beta0 + beta1 * x1 # linear predictor, xb
y = norm.rvs(loc=xb, scale=1.0, size=nobs) # create y as adjusted
# Fit
df = pandas.DataFrame({'x1': x1, 'y': y}) # re-write data
with Model() as model_glm:
glm('y ~ x1', df)
trace = sample(5000)
# Output
summary(trace)
# show graphical output
traceplot(trace)
plt.show()
