MDC Procedure
Functional Summary
Table 2 summarizes the statements and options used with the MDC procedure.
Table 2: PROC MDC Functional Summary
| Description | Statement | Option |
|---|---|---|
| Data Set Options | ||
| Formats the data for use by PROC MDC | MDCDATA | |
| Specifies the input data set | MDC | DATA= |
| Specifies the output data set for CLASS STATEMENT | CLASS | OUT = |
| Writes parameter estimates to an output data set | MDC | OUTEST= |
| Includes covariances in the OUTEST= data set | MDC | COVOUT |
| Writes linear predictors and predicted probabilities to an output data set | OUTPUT | OUT= |
| Declaring the Role of Variables | ||
| Specifies the ID variable | ID | |
| Specifies BY-group processing variables | BY | |
| Printing Control Options | ||
| Requests all printing options | MODEL | ALL |
| Displays correlation matrix of the estimates | MODEL | CORRB |
| Displays covariance matrix of the estimates | MODEL | COVB |
| Displays detailed information about optimization iterations | MODEL | ITPRINT |
| Suppresses all displayed output | MODEL | NOPRINT |
| Model Estimation Options | ||
| Specifies the choice variables | MODEL | CHOICE=() |
| Specifies the convergence criterion | MODEL | CONVERGE= |
| Specifies the type of covariance matrix | MODEL | COVEST= |
| Specifies the starting point of the Halton sequence | MODEL | HALTONSTART= |
| Specifies options specific to the HEV model | MODEL | HEV=() |
| Sets the initial values of parameters used by the iterative optimization algorithm | MODEL | INITIAL=() |
| Specifies the maximum number of iterations | MODEL | MAXITER= |
| Specifies the options specific to mixed logit | MODEL | MIXED=() |
| Specifies the number of choices for each person | MODEL | NCHOICE= |
| Specifies the number of simulations | MODEL | NSIMUL= |
| Specifies the optimization technique | MODEL | OPTMETHOD= |
| Specifies the type of random number generators | MODEL | RANDNUM= |
| Specifies that initial values are generated using random numbers | MODEL | RANDINIT |
| Specifies the rank dependent variable | MODEL | RANK |
| Specifies optimization restart options | MODEL | RESTART=() |
| Specifies a restriction on inclusive parameters | MODEL | SAMESCALE |
| Specifies a seed for pseudo-random number generation | MODEL | SEED= |
| Specifies a stated preference data restriction on inclusive parameters | MODEL | SPSCALE |
| Specifies the type of the model | MODEL | TYPE= |
| Specifies normalization restrictions on multinomial probit error variances | MODEL | UNITVARIANCE=() |
| Controlling the Optimization Process | ||
| Specifies upper and lower bounds for the parameter estimates | BOUNDS | |
| Specifies linear restrictions on the parameter estimates | RESTRICT | |
| Specifies nonlinear optimization options | NLOPTIONS | |
| Nested Logit Related Options | ||
| Specifies the tree structure | NEST | LEVEL()= |
| Specifies the type of utility function | UTILITY | U()= |
| Output Control Options | ||
| Outputs predicted probabilities | OUTPUT | P= |
| outputs estimated linear predictor | OUTPUT | XBETA= |
| Test Request Options | ||
| Requests Wald, Lagrange multiplier, and likelihood ratio tests | TEST | ALL |
| Requests the Wald test | TEST | WALD |
| Requests the Lagrange multiplier test | TEST | LM |
| Requests the likelihood ratio test | TEST | LR |
Last updated: June 19, 2025