pysal.model.spreg.GM_Error_Hom

class pysal.model.spreg.GM_Error_Hom(y, x, w, max_iter=1, epsilon=1e-05, A1='hom_sc', vm=False, name_y=None, name_x=None, name_w=None, name_ds=None)[source]

GMM method for a spatial error model with homoskedasticity, with results and diagnostics; based on Drukker et al. (2013) [Drukker2013], following Anselin (2011) [Anselin2011].

Parameters:
y : array

nx1 array for dependent variable

x : array

Two dimensional array with n rows and one column for each independent (exogenous) variable, excluding the constant

w : pysal W object

Spatial weights object

max_iter : int

Maximum number of iterations of steps 2a and 2b from Arraiz et al. Note: epsilon provides an additional stop condition.

epsilon : float

Minimum change in lambda required to stop iterations of steps 2a and 2b from Arraiz et al. Note: max_iter provides an additional stop condition.

A1 : string

If A1=’het’, then the matrix A1 is defined as in Arraiz et al. If A1=’hom’, then as in Anselin (2011). If A1=’hom_sc’ (default), then as in Drukker, Egger and Prucha (2010) and Drukker, Prucha and Raciborski (2010).

vm : boolean

If True, include variance-covariance matrix in summary results

name_y : string

Name of dependent variable for use in output

name_x : list of strings

Names of independent variables for use in output

name_w : string

Name of weights matrix for use in output

name_ds : string

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 and pysal to perform all the analysis.

>>> import numpy as np
>>> import pysal.lib

Open data on Columbus neighborhood crime (49 areas) using pysal.lib.io.open(). This is the DBF associated with the Columbus shapefile. Note that pysal.lib.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.

>>> db = pysal.lib.io.open(pysal.lib.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(db.by_col("HOVAL"))
>>> y = np.reshape(y, (49,1))

Extract INC (income) and CRIME (crime) 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.

>>> X = []
>>> X.append(db.by_col("INC"))
>>> X.append(db.by_col("CRIME"))
>>> X = np.array(X).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 create one from columbus.shp.

>>> w = pysal.lib.weights.Rook.from_shapefile(pysal.lib.examples.get_path("columbus.shp"))

Unless there is a good reason not to do it, the weights have to be row-standardized 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.

>>> reg = GM_Error_Hom(y, X, w=w, A1='hom_sc', name_y='home value', 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. This class offers an error model that assumes homoskedasticity but that unlike the models from spreg.error_sp, it allows for inference on the spatial parameter. This is why you obtain as many coefficient estimates as standard errors, which you calculate taking the square root of the diagonal of the variance-covariance matrix of the parameters:

>>> print np.around(np.hstack((reg.betas,np.sqrt(reg.vm.diagonal()).reshape(4,1))),4)
[[ 47.9479  12.3021]
 [  0.7063   0.4967]
 [ -0.556    0.179 ]
 [  0.4129   0.1835]]
Attributes:
summary : string

Summary of regression results and diagnostics (note: use in conjunction with the print command)

betas : array

kx1 array of estimated coefficients

u : array

nx1 array of residuals

e_filtered : array

nx1 array of spatially filtered residuals

predy : array

nx1 array of predicted y values

n : integer

Number of observations

k : integer

Number of variables for which coefficients are estimated (including the constant)

y : array

nx1 array for dependent variable

x : array

Two dimensional array with n rows and one column for each independent (exogenous) variable, including the constant

iter_stop : string

Stop criterion reached during iteration of steps 2a and 2b from Arraiz et al.

iteration : integer

Number of iterations of steps 2a and 2b from Arraiz et al.

mean_y : float

Mean of dependent variable

std_y : float

Standard deviation of dependent variable

pr2 : float

Pseudo R squared (squared correlation between y and ypred)

vm : array

Variance covariance matrix (kxk)

sig2 : float

Sigma squared used in computations

std_err : array

1xk array of standard errors of the betas

z_stat : list of tuples

z statistic; each tuple contains the pair (statistic, p-value), where each is a float

xtx : float

X’X

name_y : string

Name of dependent variable for use in output

name_x : list of strings

Names of independent variables for use in output

name_w : string

Name of weights matrix for use in output

name_ds : string

Name of dataset for use in output

title : string

Name of the regression method used

__init__(y, x, w, max_iter=1, epsilon=1e-05, A1='hom_sc', 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__(y, x, w[, max_iter, epsilon, A1, …]) Initialize self.

Attributes

mean_y
std_y