def _estimate_scale(self, resid):
"""
Estimates the scale based on the option provided to the fit method.
"""
if isinstance(self.scale_est, str):
if self.scale_est.lower() == 'mad':
return scale.mad(resid)
if self.scale_est.lower() == 'stand_mad':
return scale.stand_mad(resid)
elif isinstance(self.scale_est, scale.HuberScale):
return scale.hubers_scale(self.df_resid, self.nobs, resid)
else:
return scale.scale_est(self, resid)**2
def _estimate_scale(self, resid):
"""
Estimates the scale based on the option provided to the fit method.
"""
if isinstance(self.scale_est, str):
if self.scale_est.lower() == 'mad':
return scale.mad(resid)
if self.scale_est.lower() == 'stand_mad':
return scale.stand_mad(resid)
elif isinstance(self.scale_est, scale.HuberScale):
return scale.hubers_scale(self.df_resid, self.nobs, resid)
else:
return scale.scale_est(self, resid)**2
def test_axisneg1(self):
m = scale.stand_mad(self.X, axis=-1)
assert_equal(m.shape, (40, 10))
def test_axis0(self):
m = scale.stand_mad(self.X, axis=0)
assert_equal(m.shape, (10, 30))
def test_axis1(self):
m = scale.stand_mad(self.X, axis=1)
assert_equal(m.shape, (40, 30))
def test_stand_mad(self):
assert_almost_equal(scale.stand_mad(self.chem), 0.52632, DECIMAL)
def test_stand_mad(self):
m = scale.stand_mad(self.X)
assert_equal(m.shape, (10, ))
def test_axisneg1(self):
m = scale.stand_mad(self.X, axis=-1)
assert_equal(m.shape, (40,10))
def test_axis1(self):
m = scale.stand_mad(self.X, axis=1)
assert_equal(m.shape, (40,30))
def test_axis0(self):
m = scale.stand_mad(self.X, axis=0)
assert_equal(m.shape, (10,30))
def test_stand_mad(self):
m = scale.stand_mad(self.X)
assert_equal(m.shape, (10,))
def test_stand_mad(self):
assert_almost_equal(scale.stand_mad(self.chem), 0.52632, DECIMAL)
