turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- Why is PROC HPNLMOD taking longer than NLMIXED?

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

a week ago

PROC HPNLMOD and PROC NLMIXED : Which one is quick and good for estimation of model?

Initailly I thought the process was hung when I run PROC HPNLMOD, but actually it was not hung. Rather, it was taking longer than the PROC NLMIXED estimation of the same model.

Why is PROC HPNLMOD taking longer than NLMIXED -- in real time as well as in CPU time? Despite being a high-performance procedure and using 4 CPUs instead of 1, HPNLMOD is faster only if my dataset is smallish (about 100,000 and 13 variables). With a dataset of about 200,000 obs, its CPU time is much longer, and its real time only a little shorter, than NLMIXED. And for 2 million obs, HPNLMOD is *slower* in real time (and also in CPU time).

This doesn't make sense to me, as HPNLMOD is supposed to be a high-performance procedure compared to NLMIXED. Is there something I am not understanding about system parameters/constraints and that prevents me from obtaining the performance benefits of using HPNLMOD instead of NLMIXED?

Runing my estimation routine on SASGrid (9.4V) from EG 7.12

Any help would be greatly appreciated...

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

a week ago

Are both procedures running through Grid?

One thing that would be pure guess is that convergence criteria may be different, possibly with HPNLMOD using defaults that may take more iterations.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

Monday

Yes, both are running on Grid. They are each running with their default optimization method. I believe they are using the same convergence criterion, but I will check that. I’ve tried other optimization methods with each, but the default has proven to be faster than others.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

Tuesday

We'd have to see your syntax to make a guess, but recall that the default solution method in PROC HPNLMOD is optimization of a least squares regression model, whereas PROC NLMIXED uses maximum likelihood estimation. Depending on your data and the model, you might be seeing differences due to the underlying solution technique rather than the procedures themselves. You can read about the advantages and disadvantages of the different optimization algorithms. PROC HPNLMOD uses the LEVMAR technique by default, and the doc says "for large problems, it consumes more memory and takes longer than the other techniques."

Both procedures support a TECHNIQUE= option where you can specify the solution technique. By default, PROC NLMIXED uses TECHNIQUE=QUANEW for quasi-Newton optimization. Try switching to TECHNIQUE=QUANEW in PROC HPNLMOD and see what happens.