SPATIALREG Procedure
Displayed Output
PROC SPATIALREG produces the following displayed output.
Class Level Information
If you specify the CLASS statement, the SPATIALREG procedure displays a table that contains the following information:
CLASS variable name
number of levels of the CLASS variable
list of values of the CLASS variable
Iteration History for Parameter Estimates
If you specify the ITPRINT or PRINTALL option in the PROC SPATIALREG statement, PROC SPATIALREG displays a table that contains the following information for each iteration. Some information is specific to the model-fitting procedure that you choose (for example, Newton-Raphson, trust region, quasi-Newton).
iteration number
number of restarts since the fitting began
number of function calls
number of active constraints at the current solution
value of the objective function (the negative log-likelihood value) at the current solution
change in the objective function from previous iteration
value of the maximum absolute gradient element
step size (for Newton-Raphson and quasi-Newton methods)
slope of the current search direction (for Newton-Raphson and quasi-Newton methods)
lambda (for trust region method)
radius value at current iteration (for trust region method)
Model Fit Summary
The "Model Fit Summary" table contains the following information:
dependent variable name
number of observations used
data set name
name of the spatial weights data set (specified by the WMAT= option)
type of model that was fit
log-likelihood value at solution
maximum absolute gradient at solution
number of iterations
AIC value at the solution (a smaller value indicates a better fit)
SBC value at the solution (a smaller value indicates a better fit)
Below the "Model Fit Summary" table is a statement about whether the algorithm successfully converged.
Parameter Estimates
The "Parameter Estimates" table displays the estimates of the model parameters. In the SAR model, estimates are also displayed for the autoregressive coefficient 
and the variance of the error terms 
. For the SEM, SDEM, SMA, and SDMA models, estimates are given for the autoregressive coefficient 
and the variance of the error terms . In addition, for SAC, SDAC, SARMA, and SDARMA models, estimates are given for the autoregressive coefficients and , and the variance of the error terms . In the linear and SLX models, estimates are given for the variance of the error terms .
"_rho" is the internal name of the autoregressive coefficient in the SAR, SDM, SARMA, SDARMA, SAC, and SDAC models. The t statistic given for "_rho" is a test of autoregressive coefficient. In addition, "_lambda" is the internal name of the autoregressive coefficient in the SEM, SDEM, SMA, SARMA, SAC, and SDAC models. Moreover, "_sigma2" is the internal name of the variance parameter .
Last Evaluation of the Gradient
If you specify the ITPRINT option in the MODEL statement, the SPATIALREG procedure displays the last evaluation of the gradient vector.
Covariance of Parameter Estimates
If you specify the COVB option in the MODEL statement or in the PROC SPATIALREG statement, the SPATIALREG procedure displays the estimated covariance matrix, defined as the inverse of the information matrix, evaluated at the final iteration.
Correlation of Parameter Estimates
If you specify the CORRB option in the MODEL statement or in the PROC SPATIALREG statement, PROC SPATIALREG displays the estimated correlation matrix. It is based on the Hessian matrix that is used in the final iteration.