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
- /
- SAS Programming
- /
- SAS Procedures
- /
- How to model hypergeometric distribution in PROC G...

Topic Options

- 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
- RSS Feed
- Permalink
- Email to a Friend
- Report Inappropriate Content

08-07-2013 02:09 AM

There is geometric distribution in PROC GLIMMIX. What about hypergeometric? As far as I understand it is the same as binomial but without replacement. Thank you in advance.

Accepted Solutions

Solution

08-07-2013
01:24 PM

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

Posted in reply to stan

08-07-2013 01:24 PM

A hypergeometric distribution is not available in GLIMMIX, probably because it is really hard to fix marginal totals and still do REML. Maybe future releases will make it available. For now, the only way to fit a mixed model with a hypergeometric distribution would be to write the necessary code in PROC NLMIXED.

I'm curious as to how a hypergeometric distribution could be implemented in the context of mixed models, where anything that defined the marginal total would be a random variable, rather than a fixed sum.

Steve Denham

All Replies

Solution

08-07-2013
01:24 PM

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

Posted in reply to stan

08-07-2013 01:24 PM

A hypergeometric distribution is not available in GLIMMIX, probably because it is really hard to fix marginal totals and still do REML. Maybe future releases will make it available. For now, the only way to fit a mixed model with a hypergeometric distribution would be to write the necessary code in PROC NLMIXED.

I'm curious as to how a hypergeometric distribution could be implemented in the context of mixed models, where anything that defined the marginal total would be a random variable, rather than a fixed sum.

Steve Denham

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

Posted in reply to SteveDenham

08-16-2013 04:48 AM

Steve,

I wanted to model hypergeometric distribution as my response variable is a characteristic of *cells* and there is no need to put them back in a vial. We consider cells with a certain characteristic and count their percent / proportion within a certain cell type. So the analyzed cell type might be considered as a subtype/subclass of cells.

Robin R. High wrote "[...] the *hypergeometric* distribution converges to the binomial, and then with the binomial, as **n** gets large and **p** gets small, so that the **mean = n*p** remains constant, the *binomial* converges to the Poisson ... and, the *Poisson* converges to the *normal*. So, if the event of interest is somewhat "rare" and the denominator is very large (a "measure" of total size...), the binomial and poisson [...] will likely give you similar results, including fitted values". (I hope I cite correctly.)

As I understand correctly the denominator in my case is the number of cells of a type *T*. It ranges from 1000 to 3000 cells. The number of cells of interest *t* (the numerator) ranges from few decades (or less) to hundreds.

So, if the hypergeometric distribution is unavailable in PROC GLIMMIX now, I'll model the response variable as binomially distributed. Do I guess correct?

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

Posted in reply to stan

08-16-2013 08:21 AM

Given everything you have, either a binomial as planned, or maybe even a beta, if all values are bounded away from zero and one.

Steve Denham