Chapter Contents |
Previous |
Next |

The NLIN Procedure |

- _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.

- _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.

_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.

Chapter Contents |
Previous |
Next |
Top |

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.