def test_ar_modeling():
# Compare against standard routines
rng = np.random.RandomState(20110903)
N = 10
Y = rng.normal(size=(N,1)) * 10 + 100
X = np.c_[np.linspace(-1,1,N), np.ones((N,))]
my_model = OLSModel(X)
results = my_model.fit(Y)
# fmristat wrapper
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2,))
assert_true(np.all(np.abs(rhos <= 1)))
# standard routine
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# Make 2D and 3D Y
Y = rng.normal(size=(N,4)) * 10 + 100
results = my_model.fit(Y)
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2,4))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# 3D
results.resid = np.reshape(results.resid, (N,2,2))
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2,2,2))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
def test_ar_modeling():
# Compare against standard routines
rng = np.random.RandomState(20110903)
N = 10
Y = rng.normal(size=(N, 1)) * 10 + 100
X = np.c_[np.linspace(-1, 1, N), np.ones((N, ))]
my_model = OLSModel(X)
results = my_model.fit(Y)
# fmristat wrapper
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2, ))
assert_true(np.all(np.abs(rhos <= 1)))
# standard routine
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# Make 2D and 3D Y
Y = rng.normal(size=(N, 4)) * 10 + 100
results = my_model.fit(Y)
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2, 4))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
# 3D
results.resid = np.reshape(results.resid, (N, 2, 2))
rhos = estimateAR(results.resid, my_model.design, order=2)
assert_equal(rhos.shape, (2, 2, 2))
assert_true(np.all(np.abs(rhos <= 1)))
rhos2 = ar_bias_correct(results, 2)
assert_array_almost_equal(rhos, rhos2, 8)
def estimateAR(resid, design, order=1):
"""
Estimate AR parameters using bias correction from fMRIstat.
Parameters
----------
resid: array-like
residuals from model
model: an OLS model used to estimate residuals
Returns
-------
output : array
shape (order, resid
"""
invM = ar_bias_corrector(design, spl.pinv(design), order)
return ar_bias_correct(resid, order, invM)
def estimateAR(resid, design, order=1):
"""
Estimate AR parameters using bias correction from fMRIstat.
Parameters
----------
resid: array-like
residuals from model
model: an OLS model used to estimate residuals
Returns
-------
output : array
shape (order, resid
"""
invM = ar_bias_corrector(design, spl.pinv(design), order)
return ar_bias_correct(resid, order, invM)