

def test_poisson_newton():
    nobs = 10000
    np.random.seed(987689)
    x = np.random.randn(nobs, 3)
    x = sm.add_constant(x, prepend=True)
    y_count = np.random.poisson(np.exp(x.sum(1)))
    mod = sm.Poisson(x, x)
    from pandas.util.testing import assert_produces_warning
    with assert_produces_warning():
        res = mod.fit(start_params=(- np.ones(4)), method='newton', disp=0)
    assert_((not res.mle_retvals['converged']))

dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/discrete/tests/test_discrete.py test_poisson_newton/VarMisuse@75/89 `y_count`->`x`