The CSPATIALREG 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 CSPATIALREG fits a model that contains only an intercept. The dependent-variable is treated as a continuous variable in the primary input data table (specified in the DATA= option). Models in PROC CSPATIALREG do not allow missing values. If there are missing values, an error message is returned.

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

NOINT

suppresses the intercept parameter.

TYPE=CAR | 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 CSPATIALREG statement, the MODEL statement overrides the PROC CSPATIALREG statement. You can specify the following model types:

CAR: fits a conditional autoregressive model.
LINEAR: fits a linear model.
SAC: fits a spatial autoregressive confused model.
SAR: fits a spatial autoregressive model.
SARMA: fits a spatial autoregressive moving average model.
SEM: fits a spatial error model.
SMA: fits a spatial moving average model.

By default, TYPE=SAR.

Printing Options

You can specify the following options in either the PROC CSPATIALREG statement or the MODEL statement. If you specify these options in both the PROC CSPATIALREG and MODEL statements, the MODEL statement overrides the PROC CSPATIALREG statement.

CORRB: prints the correlation matrix of the parameter estimates.
COVB: prints the covariance matrix of the parameter estimates.
NOPRINT: suppresses all printed output.
PRINTALL: prints all available output.

Last updated: April 15, 2021