def test_resid_rename_warnings_ols():
model = OLSModel(design=X)
results_ols = model.fit(Y)
with pytest.warns(FutureWarning, match="'df_resid'"):
assert_array_equal(results_ols.df_resid, results_ols.df_residuals)
with pytest.warns(FutureWarning, match="'resid'"):
assert_array_equal(results_ols.resid, results_ols.residuals)
with pytest.warns(FutureWarning, match="'wresid'"):
assert_array_equal(results_ols.wresid, results_ols.whitened_residuals)
with pytest.warns(FutureWarning, match="'norm_resid'"):
assert_array_equal(results_ols.norm_resid,
results_ols.normalized_residuals)
with pytest.warns(FutureWarning, match="'wY'"):
assert_array_equal(results_ols.wY, results_ols.whitened_Y)
with pytest.warns(FutureWarning, match="'wdesign'"):
assert_array_equal(results_ols.wdesign, results_ols.whitened_design)
with pytest.warns(FutureWarning, match="'df_resid'"):
assert_array_equal(model.df_resid, model.df_residuals)
with pytest.warns(FutureWarning, match="'wdesign'"):
assert_array_equal(model.wdesign, model.whitened_design)
def test_residuals():
Xintercept = X.copy()
# If design matrix contains an intercept, the
# mean of the residuals should be 0 (short of
# some numerical rounding errors)
Xintercept[:, 0] = 1
model = OLSModel(design=Xintercept)
results = model.fit(Y)
assert_almost_equal(results.residuals.mean(), 0)
assert len(results.whitened_residuals) == 40
def test_predicted_r_square():
Xshort = X.copy()[:10, :]
Yshort = Y.copy()[:10]
# Signal of 10 elements should be completely
# predicted by 10 predictors (short of some numerical
# rounding errors)
model = OLSModel(design=Xshort)
results = model.fit(Yshort)
assert_almost_equal(results.residuals.sum(), 0)
assert_array_almost_equal(results.predicted, Yshort)
assert_almost_equal(results.r_square, 1.0)
def orthogonalize(y, x):
"""Orthogonalize variable y with respect to variable x. Convert 1-d array
to 2-d array with shape (n, 1)"""
yT = np.atleast_2d(y).T
xT = np.atleast_2d(x).T
model = OLSModel(xT).fit(yT)
return model.residuals.squeeze()
def test_OLS_degenerate():
Xd = X.copy()
Xd[:, 0] = Xd[:, 1] + Xd[:, 2]
model = OLSModel(design=Xd)
results = model.fit(Y)
assert results.df_residuals == 31
def test_OLS():
model = OLSModel(design=X)
results = model.fit(Y)
assert results.df_residuals == 30
assert results.residuals.shape[0] == 40
assert results.predicted.shape[0] == 40
""" Testing models module
"""
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
from nilearn.glm import OLSModel
N = 10
X = np.c_[np.linspace(-1, 1, N), np.ones((N, ))]
Y = np.r_[range(5), range(1, 6)]
MODEL = OLSModel(X)
RESULTS = MODEL.fit(Y)
""" R script
::
X = cbind(0:9 * 2/9 -1, 1)
Y = as.matrix(c(0:4, 1:5))
results = lm(Y ~ X-1)
print(results)
print(summary(results))
gives::
Call:
lm(formula = Y ~ X - 1)
Coefficients:
X1 X2