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