The SPATIALREG Procedure

MODEL Statement

MODEL dependent-variable = <regressors> </ options>;

The MODEL statement specifies the dependent-variable and independent covariates (regressors) for the regression model. If you specify no regressors, PROC SPATIALREG fits a model that contains only an intercept. The dependent-variable is treated as a continuous variable in the primary input data set (specified in the DATA= option). Models in PROC SPATIALREG do not allow missing values. If there are missing values, you get an error message.

You can specify more than one MODEL statement. You can specify the following options in the MODEL statement after a slash (/):

NOINT

suppresses the intercept parameter.

TYPE=LINEAR | SAC | SAR | SARMA | SEM | SMA

specifies the type of model to be fitted. If you specify this option in both the MODEL statement and the PROC SPATIALREG statement, the MODEL statement overrides the PROC SPATIALREG statement. You can specify the following model types:

LINEAR: specifies the linear model.
SAC: specifies the spatial autoregressive confused model.
SAR: specifies the spatial autoregressive model.
SARMA: specifies the spatial autoregressive moving average model.
SEM: specifies the spatial error model.
SMA: specifies the spatial moving average model.

By default, TYPE=SAR.

Printing Options

CORRB: prints the correlation matrix of the parameter estimates. You can also specify this option in the PROC SPATIALREG statement.
COVB: prints the covariance matrix of the parameter estimates. You can also specify this option in the PROC SPATIALREG statement.
ITPRINT: prints the objective function and parameter estimates at each iteration. The objective function is the negative log-likelihood function. You can also specify this option in the PROC SPATIALREG statement.
PRINTALL: requests all available output. You can also specify this option in the PROC SPATIALREG statement.

Last updated: August 25, 2017