I would like to pose some questions about the best way of dealing 
with missing values when using mle.  I had assumed, wrongly, that mle 
simply applies case-wise deletion to cases with missing 
values.  Instead it terminates with a more or less informative error 
message.  On the other hand, built-in estimation commands seem to 
handle missing values internally.
A.  Is this the right thing to do?  Deleting cases with missing 
values in calculating the log-likelihood, etc is not difficult, but 
there may be models when missing values cause problems if not dealt 
with properly - eg any lag structure.  Would it be possible to 
provide an option (--delmiss) for case-wise deletion of missing values?
B.  In writing a function it is not difficult to test for missing 
values, but then what should the function do?  Here the question is 
what are the consequences of adjusting the sample to exclude cases 
with missing values within the function?  Would this affect the 
sample used in the program that calls the function?  If so, one would 
really need a "smpl --restore" command to set the sample back to its 
state on entry to the function.
C.  Finally, the present arrangement can be maintained, in which case 
I think that more consistent error messages should be generated by 
mle if that is possible.
Gordon