"cell_type": "markdown",
"metadata": {},
"source": [
- "This example file shows how to use a few of the ``statsmodels`` regression diagnostic tests in a real-life context. You can learn about more tests and find out more information abou the tests here on the [Regression Diagnostics page.](http://www.statsmodels.org/stable/diagnostic.html) \n",
+ "This example file shows how to use a few of the ``statsmodels`` regression diagnostic tests in a real-life context. You can learn about more tests and find out more information about the tests here on the [Regression Diagnostics page.](http://www.statsmodels.org/stable/diagnostic.html) \n",
"\n",
"Note that most of the tests described here only return a tuple of numbers, without any annotation. A full description of outputs is always included in the docstring and in the online ``statsmodels`` documentation. For presentation purposes, we use the ``zip(name,test)`` construct to pretty-print short descriptions in the examples below."
]
"""
#JP: heavily adjusted to work as plugin replacement for bspline
- # smoother in gam.py initalized by function default_smoother
+ # smoother in gam.py initialized by function default_smoother
# Only fixed exceptions, I didn't check whether it is statistically
# correctand I think it is not, there are still be some dimension
# problems, and there were some dimension problems initially.
C[1,1,1] = 0.5
xhat3, err3 = VARMA(x,B,C)
- x = np.r_[np.zeros((P,K)),x] #prepend inital conditions
+ x = np.r_[np.zeros((P,K)),x] #prepend initial conditions
xhat4, err4 = VARMA(x,B,C)
C[1,1,1] = 1
Notes
------
If fit by 'mle', it is assumed for the Kalman Filter that the initial
- unkown state is zero, and that the inital variance is
+ unknown state is zero, and that the initial variance is
P = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel('F')).reshape(r,
r, order = 'F')
Notes
------
If fit by 'mle', it is assumed for the Kalman Filter that the initial
- unkown state is zero, and that the inital variance is
+ unknown state is zero, and that the initial variance is
P = dot(inv(identity(m**2)-kron(T,T)),dot(R,R.T).ravel('F')).reshape(r,
r, order = 'F')
"""
initialize_known(initial_state, initial_state_cov)
"""
- validate_vector_shape('inital state', &initial_state.shape[0], self.k_states, None)
+ validate_vector_shape('initial state', &initial_state.shape[0], self.k_states, None)
validate_matrix_shape('initial state covariance', &initial_state_cov.shape[0], self.k_states, self.k_states, None)
self.initial_state = initial_state