def test_missing():
# Datasets
endog = np.arange(10).reshape(10,1)
endog_pre_na = np.ascontiguousarray(np.c_[
endog.copy() * np.nan, endog.copy() * np.nan, endog, endog])
endog_post_na = np.ascontiguousarray(np.c_[
endog, endog, endog.copy() * np.nan, endog.copy() * np.nan])
endog_inject_na = np.ascontiguousarray(np.c_[
endog, endog.copy() * np.nan, endog, endog.copy() * np.nan])
# Base model
mod = KalmanFilter(np.ascontiguousarray(np.c_[endog, endog]), k_states=1,
initialization='approximate_diffuse')
mod['design', :, :] = 1
mod['obs_cov', :, :] = np.eye(mod.k_endog)*0.5
mod['transition', :, :] = 0.5
mod['selection', :, :] = 1
mod['state_cov', :, :] = 0.5
llf = mod.loglikeobs()
# Model with prepended nans
mod = KalmanFilter(endog_pre_na, k_states=1,
initialization='approximate_diffuse')
mod['design', :, :] = 1
mod['obs_cov', :, :] = np.eye(mod.k_endog)*0.5
mod['transition', :, :] = 0.5
mod['selection', :, :] = 1
mod['state_cov', :, :] = 0.5
llf_pre_na = mod.loglikeobs()
assert_allclose(llf_pre_na, llf)
# Model with appended nans
mod = KalmanFilter(endog_post_na, k_states=1,
initialization='approximate_diffuse')
mod['design', :, :] = 1
mod['obs_cov', :, :] = np.eye(mod.k_endog)*0.5
mod['transition', :, :] = 0.5
mod['selection', :, :] = 1
mod['state_cov', :, :] = 0.5
llf_post_na = mod.loglikeobs()
assert_allclose(llf_post_na, llf)
# Model with injected nans
mod = KalmanFilter(endog_inject_na, k_states=1,
initialization='approximate_diffuse')
mod['design', :, :] = 1
mod['obs_cov', :, :] = np.eye(mod.k_endog)*0.5
mod['transition', :, :] = 0.5
mod['selection', :, :] = 1
mod['state_cov', :, :] = 0.5
llf_inject_na = mod.loglikeobs()
assert_allclose(llf_inject_na, llf)
