topic Re: SAS statistical procedures that allow multiple dependent variables? in Statistical Procedures

SAS statistical procedures that allow multiple dependent variables?

xtc283x — Thu, 19 Feb 2015 18:38:12 GMT

Besides Proc GLM, which SAS statistical or regression modeling procedures allow the specification of multiple dependent variables?

Re: SAS statistical procedures that allow multiple dependent variables?

art297 — Thu, 19 Feb 2015 18:58:04 GMT

Re: SAS statistical procedures that allow multiple dependent variables?

lvm — Thu, 19 Feb 2015 21:28:11 GMT

Different possibilities. What is your goal? Simultaneous modeling of all the (possibly correlated) response variables or just having a convenient way of getting separate analyses for several response variables? Check out PROC PLS (partial least squares).

Re: SAS statistical procedures that allow multiple dependent variables?

xtc283x — Fri, 20 Feb 2015 00:39:59 GMT

Arthur-

Thanks. Somehow, the link you provided exploded in a window full of HTML.

Re: SAS statistical procedures that allow multiple dependent variables?

xtc283x — Fri, 20 Feb 2015 00:46:45 GMT

LVM-

Simultaneous modeling...as in MANOVA but extended to approaches such as hierarchical mixture models (e.g., Proc Mixed with multiple DVs which this procedure won't allow), maximum likelihood estimation for mixtures of DV forms, and so on. Proc PLS is kind of the idea but I prefer to obtain estimates that aren't collapsed across a PLS-derived component.

Make sense?

Thanks,

Tom

Re: SAS statistical procedures that allow multiple dependent variables?

art297 — Fri, 20 Feb 2015 02:02:16 GMT

I'll try and repost it here. Remove the two embedded spaces from the following and copy and paste the url to your browser: www.ats.ucla.edu /stat/ sas/whatstat/

Re: SAS statistical procedures that allow multiple dependent variables?

lvm — Fri, 20 Feb 2015 04:06:06 GMT

Lots of ways of doing this with proc mixed and glimmix for hierarchical models. This old article is technically about repeated measures in GLM and MIXED, but is ultimately a comparison of MANOVA in the two procedures.

https://support.sas.com/rnd/app/stat/papers/mixedglm.pdf

Here is a more recent blog on the topic:

http://blogs.sas.com/content/sastraining/2011/02/02/the-punchline-manova-or-a-mixed-model/

The syntax is different, and the data are stacked, for analysis in MIXED and GLIMMIX compared with GLM, but you can handle so many more situations with the mixed-model procedures. You are not even restricted to the same distribution. One of the nice examples in the User's Guide for GLIMMIX deals with a multivariate analysis of two random variables, one binary (Bernoulli) and the other count (Poisson).

SAS/STAT(R) 9.3 User's Guide

Re: SAS statistical procedures that allow multiple dependent variables?

xtc283x — Fri, 20 Feb 2015 11:50:38 GMT

Arthur--Thanks, I always forget about canonical correlation. That's a good cross-software comparison, focused on OLS. Tom

Re: SAS statistical procedures that allow multiple dependent variables?

xtc283x — Fri, 20 Feb 2015 12:01:01 GMT

LVM-Interesting. The different data structures -- e.g., stacking up the "Y" variable and adding an "Age" predictor in Proc Mixed vs using "Y1-Y4" with a REPEATED statement in Proc GLM isn't quite what I have in mind. Proc Mixed (or Glimmix) still only permit a single "Y" or dependent variable. Tom

Re: SAS statistical procedures that allow multiple dependent variables?

PaigeMiller — Fri, 20 Feb 2015 14:19:33 GMT

xtc283x wrote:

Besides Proc GLM, which SAS statistical or regression modeling procedures allow the specification of multiple dependent variables?

PROC PLS

Re: SAS statistical procedures that allow multiple dependent variables?

SteveDenham — Fri, 20 Feb 2015 16:22:00 GMT

PROC MIXED can handle this kind-of, sort-of through the doubly repeated measures covariance structures. PROC GLIMMIX can also do this in a kind-of, sort-of manner as in the Example: Joint Modeling of Binary and Count Data, where the distribution of different variables can be specified. The really hard part is specifying multiple variables from the same family, say 4 Gaussian variables, a count and a proportion. I haven't been able to figure that one out. Several variables, each with a different distribution, can be done, with patience.

Steve Denham

Re: SAS statistical procedures that allow multiple dependent variables?

lvm — Fri, 20 Feb 2015 17:52:31 GMT

No, you are incorrect. MIXED and GLIMMIX can absolutely handle multiple dependent variables. This is exactly what is demonstrated in the links I sent.

Re: SAS statistical procedures that allow multiple dependent variables?

SteveDenham — Fri, 20 Feb 2015 18:03:15 GMT

Light bulb goes on. It all comes down to identifying a proper SUBJECT=. Then with an unstructured or factor analytic covariance matrix you can literally have your cake (multivariate) and eat it too (hierarchical), and all you need is a proper machine capable of fitting the model.

Steve Denham

Re: SAS statistical procedures that allow multiple dependent variables?

lvm — Fri, 20 Feb 2015 18:03:28 GMT

By the way, my latest response was directly to xtc, not to Steve. I have often done multivariate mixed model analysis, where there are several different dependent variables. I mostly use normal distribution for each variable. Done with MIXED or GLIMMIX (the stacked Ys are different dependent variables).

These procedures may or may not be relevant for you. I can't tell, because you haven't explained what you are trying to do.

Re: SAS statistical procedures that allow multiple dependent variables?

xtc283x — Fri, 20 Feb 2015 18:16:23 GMT

Steve and LVM--Ah! Now it begins to make sense. The cross-sections can define different response or dependent variables. Clearly, if using Proc Mixed (OLS estimation), those different responses need to be normalized in some way into a similar scale. This would be a challenge for Mixed if one (or more) of the responses was 0-1. In that case, you would probably want to use Glimmix, which uses ML estimation and is, therefore, scale invariant by definition. Does that sound about right?

Re: SAS statistical procedures that allow multiple dependent variables?

SteveDenham — Fri, 20 Feb 2015 18:46:07 GMT

Well, PROC MIXED doesn't use OLS, it is usually REML, and occasionally, ML. GLIMMIX for the other variables, well, it is maximum pseudo-likelihood if you want marginal estimates, or optimization via numerical integration by Laplacian or adaptive quadrature methods if you want conditional estimates.

Going out on a limb here and maybe Larry can talk me down--for a multivariate, hierarchical model, you would want to model the relationship between variables and the hierarchy both as G side.

Steve Denham

Message was edited by: Steve Denham

Re: SAS statistical procedures that allow multiple dependent variables?

lvm — Fri, 20 Feb 2015 18:48:37 GMT

No standardization necessary. In fact, that would be a bad idea. One uses an unstructured variance-covariance matrix (type=UN), which handles the different scales for variables. Better to use type=CHOL in glimmix. If you want to do something like this, you will first need to learn a lot about these procedures. Start with SAS for Mixed Models, 2nd edition.

Re: SAS statistical procedures that allow multiple dependent variables?

xtc283x — Fri, 20 Feb 2015 18:55:23 GMT

Steve and LVM-good corrections re estimation in Mixed. Given that, no normalization would be needed for either procedure. Very helpful. Thanks.

Re: SAS statistical procedures that allow multiple dependent variables?

lvm — Fri, 20 Feb 2015 19:34:46 GMT

Steve, handling the G-side effects can take some care. R-side for within-subject correlations of the different dependent variables, and G side for other effects. One may need a lot of data to precisely estimate G.

Re: SAS statistical procedures that allow multiple dependent variables?

SteveDenham — Fri, 20 Feb 2015 19:39:28 GMT

Thanks, Larry. So, site or block or cage or pen as G side, and all of the variables R side.

I am thinking about clinical pathology panels for cage housed rodents...

Steve Denham