PROC QLIM: Naming :: SAS/ETS(R) 9.2 User's Guide

The QLIM Procedure

Naming of Parameters

When there is only one equation in the estimation, parameters are named in the same way as in other SAS procedures such as REG, PROBIT, etc. The constant in the regression equation is called Intercept. The coefficients on independent variables are named by the independent variables. The standard deviation of the errors is called _Sigma. If there are Box-Cox transformations, the coefficients are named _Lambda $\text{[math]}$ , where $\text{[math]}$ increments from 1, or as specified by the user. The limits for the discrete dependent variable are named _Limit $\text{[math]}$ . If the LIMIT=varying option is specified, then _Limit $\text{[math]}$ starts from 1. If the LIMIT=varying option is not specified, then _Limit $\text{[math]}$ is set to 0 and the limit parameters start from $\text{[math]}$ . If the HETERO statement is included, the coefficients of the independent variables in the hetero equation are called _H. $\text{[math]}$ , where $\text{[math]}$ is the name of the independent variable. If the parameter name includes interaction terms, it needs to be enclosed in quotation marks followed by $\text{[math]}$ . The following example restricts the parameter that includes the interaction term to be greater than zero:

   proc qlim data=a;
      model y = x1|x2;
      endogenous y ~ discrete;
      restrict "x1*x2"N>0;
   run;

When there are multiple equations in the estimation, the parameters in the main equation are named in the format of $\text{[math]}$ . $\text{[math]}$ , where $\text{[math]}$ is the name of the dependent variable and $\text{[math]}$ is the name of the independent variable. The standard deviation of the errors is called _Sigma. $\text{[math]}$ . The correlation of the errors is called _Rho for bivariate model. For the model with three variables it is _Rho. $\text{[math]}$ . $\text{[math]}$ , _Rho. $\text{[math]}$ . $\text{[math]}$ , _Rho. $\text{[math]}$ . $\text{[math]}$ . The construction of correlation names for multivariate models is analogous. Box-Cox parameters are called _Lambda $\text{[math]}$ . $\text{[math]}$ and limit variables are called _Limit $\text{[math]}$ . $\text{[math]}$ . Parameters in the HETERO statement are named as _H. $\text{[math]}$ . $\text{[math]}$ . In the OUTEST= data set, all variables are changed from ’.’ to ’_’.

Naming of Output Variables

The following table shows the option in the OUTPUT statement, with the corresponding variable names and their explanation.

Option	Name	Explanation
PREDICTED	P_y	Predicted value of $\text{[math]}$
RESIDUAL	RESID_y	Residual of $\text{[math]}$ , (y-PredictedY)
XBETA	XBETA_y	Structure part ( $\text{[math]}$ ) of $\text{[math]}$ equation
ERRSTD	ERRSTD_y	Standard deviation of error term
PROB	PROB_y	Probability that $\text{[math]}$ is taking the observed value in this observation (discrete $\text{[math]}$ only)
PROBALL	PROB $\text{[math]}$ _y	Probability that $\text{[math]}$ is taking the $\text{[math]}$ th value (discrete $\text{[math]}$ only)
MILLS	MILLS_y	Inverse Mills ratio for $\text{[math]}$
EXPECTED	EXPCT_y	Unconditional expected value of $\text{[math]}$
CONDITIONAL	CEXPCT_y	Conditional expected value of $\text{[math]}$ , condition on the truncation.
MARGINAL	MEFF_x	Marginal effect of $\text{[math]}$ on $\text{[math]}$ ( $\text{[math]}$ ) with single equation
	MEFF_y_x	Marginal effect of $\text{[math]}$ on $\text{[math]}$ ( $\text{[math]}$ ) with multiple equations
	MEFF_P $\text{[math]}$ _x	Marginal effect of $\text{[math]}$ on $\text{[math]}$ ( $\text{[math]}$ ) with single equation and discrete $\text{[math]}$
	MEFF_P $\text{[math]}$ _y_x	Marginal effect of $\text{[math]}$ on $\text{[math]}$ ( $\text{[math]}$ ) with multiple equations and discrete $\text{[math]}$

If you prefer to name the output variables differently, you can use the RENAME option in the data set. For example, the following statements rename the residual of $\text{[math]}$ as Resid:

   proc qlim data=one;
      model y = x1-x10 / censored;
      output out=outds(rename=(resid_y=resid)) residual;
   run;

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