spreg.GM_Error¶

class
spreg.
GM_Error
(y, x, w, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None)[source]¶ GMM method for a spatial error model, with results and diagnostics; based on Kelejian and Prucha (1998, 1999) [KP98] [KP99].
 Parameters
 yarray
nx1 array for dependent variable
 xarray
Two dimensional array with n rows and one column for each independent (exogenous) variable, excluding the constant
 wpysal W object
Spatial weights object (always needed)
 vmboolean
If True, include variancecovariance matrix in summary results
 name_ystring
Name of dependent variable for use in output
 name_xlist of strings
Names of independent variables for use in output
 name_wstring
Name of weights matrix for use in output
 name_dsstring
Name of dataset for use in output
Examples
We first need to import the needed modules, namely numpy to convert the data we read into arrays that
spreg
understands andpysal
to perform all the analysis.>>> import libpysal >>> import numpy as np
Open data on Columbus neighborhood crime (49 areas) using libpysal.io.open(). This is the DBF associated with the Columbus shapefile. Note that libpysal.io.open() also reads data in CSV format; since the actual class requires data to be passed in as numpy arrays, the user can read their data in using any method.
>>> dbf = libpysal.io.open(libpysal.examples.get_path('columbus.dbf'),'r')
Extract the HOVAL column (home values) from the DBF file and make it the dependent variable for the regression. Note that PySAL requires this to be an numpy array of shape (n, 1) as opposed to the also common shape of (n, ) that other packages accept.
>>> y = np.array([dbf.by_col('HOVAL')]).T
Extract CRIME (crime) and INC (income) vectors from the DBF to be used as independent variables in the regression. Note that PySAL requires this to be an nxj numpy array, where j is the number of independent variables (not including a constant). By default this class adds a vector of ones to the independent variables passed in.
>>> names_to_extract = ['INC', 'CRIME'] >>> x = np.array([dbf.by_col(name) for name in names_to_extract]).T
Since we want to run a spatial error model, we need to specify the spatial weights matrix that includes the spatial configuration of the observations into the error component of the model. To do that, we can open an already existing gal file or create a new one. In this case, we will use
columbus.gal
, which contains contiguity relationships between the observations in the Columbus dataset we are using throughout this example. Note that, in order to read the file, not only to open it, we need to append ‘.read()’ at the end of the command.>>> w = libpysal.io.open(libpysal.examples.get_path("columbus.gal"), 'r').read()
Unless there is a good reason not to do it, the weights have to be rowstandardized so every row of the matrix sums to one. Among other things, his allows to interpret the spatial lag of a variable as the average value of the neighboring observations. In PySAL, this can be easily performed in the following way:
>>> w.transform='r'
We are all set with the preliminars, we are good to run the model. In this case, we will need the variables and the weights matrix. If we want to have the names of the variables printed in the output summary, we will have to pass them in as well, although this is optional.
>>> model = GM_Error(y, x, w=w, name_y='hoval', name_x=['income', 'crime'], name_ds='columbus')
Once we have run the model, we can explore a little bit the output. The regression object we have created has many attributes so take your time to discover them. Note that because we are running the classical GMM error model from 1998/99, the spatial parameter is obtained as a point estimate, so although you get a value for it (there are for coefficients under model.betas), you cannot perform inference on it (there are only three values in model.se_betas).
>>> print model.name_x ['CONSTANT', 'income', 'crime', 'lambda'] >>> np.around(model.betas, decimals=4) array([[ 47.6946], [ 0.7105], [ 0.5505], [ 0.3257]]) >>> np.around(model.std_err, decimals=4) array([ 12.412 , 0.5044, 0.1785]) >>> np.around(model.z_stat, decimals=6) array([[ 3.84261100e+00, 1.22000000e04], [ 1.40839200e+00, 1.59015000e01], [ 3.08424700e+00, 2.04100000e03]]) >>> round(model.sig2,4) 198.5596
 Attributes
 summarystring
Summary of regression results and diagnostics (note: use in conjunction with the print command)
 betasarray
kx1 array of estimated coefficients
 uarray
nx1 array of residuals
 e_filteredarray
nx1 array of spatially filtered residuals
 predyarray
nx1 array of predicted y values
 ninteger
Number of observations
 kinteger
Number of variables for which coefficients are estimated (including the constant)
 yarray
nx1 array for dependent variable
 xarray
Two dimensional array with n rows and one column for each independent (exogenous) variable, including the constant
 mean_yfloat
Mean of dependent variable
 std_yfloat
Standard deviation of dependent variable
 pr2float
Pseudo R squared (squared correlation between y and ypred)
 vmarray
Variance covariance matrix (kxk)
 sig2float
Sigma squared used in computations
 std_errarray
1xk array of standard errors of the betas
 z_statlist of tuples
z statistic; each tuple contains the pair (statistic, pvalue), where each is a float
 name_ystring
Name of dependent variable for use in output
 name_xlist of strings
Names of independent variables for use in output
 name_wstring
Name of weights matrix for use in output
 name_dsstring
Name of dataset for use in output
 titlestring
Name of the regression method used

__init__
(self, y, x, w, vm=False, name_y=None, name_x=None, name_w=None, name_ds=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(self, y, x, w[, vm, name_y, …])Initialize self.
Attributes
mean_y
std_y