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 The NLIN Procedure

## Special Variables

Several special variables are created automatically and can be used in PROC NLIN program statements.

### Special Variables with Values that are Set by PROC NLIN

The values of the following six special variables are set by PROC NLIN and should not be reset to a different value by programming statements:

_ERROR_
is set to 1 if a numerical error or invalid argument to a function occurs during the current execution of the program. It is reset to 0 before each new execution.

_ITER_
represents the current iteration number. The variable _ITER_ is set to -1 during the grid search phase.

_MODEL_
is set to 1 for passes through the data when only the predicted values are needed, not the derivatives. It is 0 when both predicted values and derivatives are needed. If your derivative calculations consume a lot of time, you can save resources by coding

if _model_ then return;

after your MODEL statement but before your derivative calculations. The derivatives generated by PROC NLIN do this automatically.

_N_
indicates the number of times the PROC NLIN step has been executed. It is never reset for successive passes through the data set.

_OBS_
indicates the observation number in the data set for the current program execution. It is reset to 1 to start each pass through the data set (unlike the _N_ variable).

_SSE_
has the error sum of squares of the last iteration. During the grid search phase, the _SSE_ variable is set to 0. For iteration 0, the _SSE_ variable is set to the SSE associated with the point chosen from the grid search.

### Special Variable Used to Determine Convergence Criteria

The special variable _LOSS_ can be used to determine convergence criteria:
_LOSS_
is used to determine the criterion function for convergence and step shortening. PROC NLIN looks for the variable _LOSS_ in the program statements and, if it is defined, uses the (weighted) sum of this value instead of the residual sum of squares to determine the criterion function for convergence and step shortening. This feature is useful in certain types of maximum-likelihood estimation where the residual sum of squares is not the basic criterion.

### Weighted Regression with the Special Variable _WEIGHT_

To get weighted least squares estimates of parameters, the _WEIGHT_ variable can be given a value in an assignment statement:

_weight_ = expression;

When this statement is included, the expression on the right-hand side of the assignment statement is evaluated for each observation in the data set to be analyzed. The values obtained are taken as inverse elements of the diagonal variance-covariance matrix of the dependent variable.

When a variable name is given after the equal sign, the values of the variable are taken as the inverse elements of the variance-covariance matrix. The larger the _WEIGHT_ value, the more importance the observation is given.

If the _WEIGHT_= statement is not used, the default value of 1 is used, and regular least squares estimates are obtained.

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