## Missing Values

There is one very important reason for using missing values in
program statements specifying the values of the objective
functions and derivatives: it may not be possible to evaluate
the program statements for a particular point *x*. For
example, the extrapolation formula of one of the line-search
algorithms may generate large *x* values for which the exp
function cannot be evaluated without floating point overflow.
The compiler of the program statements may check for such
situations automatically, but it would be safer if you check
the feasibility of your program statements. In some cases,
the specification of boundary or linear constraints
for parameters can avoid such situations.
In many other cases,
you can indicate that *x* is a *bad* point
simply by returning a missing value for the objective function.
In such cases the optimization algorithms in PROC NLP shorten
the step size or reduce the trust-region radius so
that the next point will be closer to the point that was
already successfully evaluated at the last iteration. Note that
the starting point *x*^{(0)} must be a point for which the
program statements can be evaluated.
*Missing Values in Input Data Sets*

Observations with missing values in the DATA= data set for
variables used in the objective function can lead to a missing
value of the objective function implying that the corresponding
BY group of data is not processed. The NOMISS option can be
used to skip those observations of the DATA= data set for
which relevant variables have missing values. Relevant are
such variables that are referred to in program statements.
There can be different reasons to include observations with missing
values in the INEST= data set. The value of
the _RHS_ variable is not used in some cases
and can be missing.
Missing values for the variables corresponding to parameters
in the _TYPE_ = data set are as follows

- PARMS observations cause those parameters to
have initial values assigned by the PARMS statement or by
the RANDOM= or INITIAL= option.
- UPPERBD or LOWERBD observations cause those
parameters to be unconstrained by upper or lower bounds.
- LE, GE, or EQ observations cause those
parameters to have zero values in the constraint.

In general, missing values are treated as zeros.

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