spreg.SURerrorGM¶
-
class
spreg.
SURerrorGM
(bigy, bigX, w, regimes=None, nonspat_diag=True, spat_diag=False, vm=False, name_bigy=None, name_bigX=None, name_ds=None, name_w=None, name_regimes=None)[source]¶ User class for SUR Error estimation by Maximum Likelihood
- Parameters
- bigydictionary
with vectors of dependent variable, one for each equation
- bigXdictionary
with matrices of explanatory variables, one for each equation
- wspatial weights object
- regimeslist
List of n values with the mapping of each observation to a regime. Assumed to be aligned with ‘x’.
- nonspat_diagboolean
flag for non-spatial diagnostics, default = False
- spat_diagboolean
flag for spatial diagnostics, default = False (to be implemented)
- vmboolean
flag for asymptotic variance for lambda and Sigma, default = False (to be implemented)
- name_bigydictionary
with name of dependent variable for each equation. default = None, but should be specified is done when sur_stackxy is used
- name_bigXdictionary
with names of explanatory variables for each equation. default = None, but should be specified is done when sur_stackxy is used
- name_dsstring
name for the data set
- name_wstring
name for the weights file
- name_regimesstring
name of regime variable for use in the output
Examples
First import libpysal to load the spatial analysis tools.
>>> import libpysal >>> from libpysal.examples import load_example >>> from libpysal.weights import Queen >>> import spreg >>> np.set_printoptions(suppress=True) #prevent scientific format
Open data on NCOVR US County Homicides (3085 areas) using pysal.open(). This is the DBF associated with the NAT shapefile. Note that pysal.open() also reads data in CSV format.
>>> nat = load_example('Natregimes') >>> db = libpysal.io.open(nat.get_path('natregimes.dbf'), 'r')
The specification of the model to be estimated can be provided as lists. Each equation should be listed separately. Equation 1 has HR80 as dependent variable, and PS80 and UE80 as exogenous regressors. For equation 2, HR90 is the dependent variable, and PS90 and UE90 the exogenous regressors.
>>> y_var = ['HR80','HR90'] >>> x_var = [['PS80','UE80'],['PS90','UE90']] >>> yend_var = [['RD80'],['RD90']] >>> q_var = [['FP79'],['FP89']]
The SUR method requires data to be provided as dictionaries. PySAL provides the tool sur_dictxy to create these dictionaries from the list of variables. The line below will create four dictionaries containing respectively the dependent variables (bigy), the regressors (bigX), the dependent variables’ names (bigyvars) and regressors’ names (bigXvars). All these will be created from th database (db) and lists of variables (y_var and x_var) created above.
>>> bigy,bigX,bigyvars,bigXvars = spreg.sur_dictxy(db,y_var,x_var)
To run a spatial error model, we need to specify the spatial weights matrix. To do that, we can open an already existing gal file or create a new one. In this example, we will create a new one from NAT.shp and transform it to row-standardized.
>>> w = Queen.from_shapefile(nat.get_path("natregimes.shp")) >>> w.transform='r'
We can now run the regression and then have a summary of the output by typing: print(reg.summary)
Alternatively, we can just check the betas and standard errors, asymptotic t and p-value of the parameters:
>>> reg = spreg.SURerrorGM(bigy,bigX,w=w,name_bigy=bigyvars,name_bigX=bigXvars,name_ds="NAT",name_w="nat_queen") >>> reg.bSUR {0: array([[3.97746866], [0.89021219], [0.43050363]]), 1: array([[2.93679119], [1.11002826], [0.48761542]])} >>> reg.sur_inf {0: array([[ 0.37251476, 10.67734504, 0. ], [ 0.14224297, 6.25839153, 0. ], [ 0.04322388, 9.95985608, 0. ]]), 1: array([[ 0.33694902, 8.71583245, 0. ], [ 0.13413626, 8.27537783, 0. ], [ 0.04033105, 12.09032288, 0. ]])}
- Attributes
- nint
number of observations in each cross-section
- n_eqint
number of equations
- bigydictionary
with vectors of dependent variable, one for each equation
- bigXdictionary
with matrices of explanatory variables, one for each equation
- bigKarray
n_eq x 1 array with number of explanatory variables by equation
- bigylagdictionary
spatially lagged dependent variable
- bigXlagdictionary
spatially lagged explanatory variable
- lamsurfloat
spatial autoregressive coefficient in ML SUR Error
- bSURarray
beta coefficients in ML SUR Error
- varbarray
variance of beta coefficients in ML SUR Error
- sigarray
error variance-covariance matrix in ML SUR Error
- bigEarray
n by n_eq matrix of vectors of residuals for each equation
- sur_infarray
inference for regression coefficients, stand. error, t, p
- surchowarray
list with tuples for Chow test on regression coefficients. each tuple contains test value, degrees of freedom, p-value
- name_bigydictionary
with name of dependent variable for each equation
- name_bigXdictionary
with names of explanatory variables for each equation
- name_dsstring
name for the data set
- name_wstring
name for the weights file
- name_regimesstring
name of regime variable for use in the output
-
__init__
(bigy, bigX, w, regimes=None, nonspat_diag=True, spat_diag=False, vm=False, name_bigy=None, name_bigX=None, name_ds=None, name_w=None, name_regimes=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(bigy, bigX, w[, regimes, …])Initialize self.