one way around it is to combine categories of your dep var, or to use x's withfewer
distinctions.
Gary
-----Original Message-----
From: Alejandro Buren <aled38(a)yahoo.com.ar>
Date: Monday, Aug 15, 2005 6:13 pm
Subject: Re: [zelig] non full-rank models in mlogit
Dear Olivia,
thank you very much for the insight.
Actually my problem is that for certain combinations of the levels of the variables I have
no observations, i.e. in the mexico example that's equivalent to not having an
observation for "vote88=1", "pristr=1",
"othcok=2","othsocok=2" Is there a way to deal with that?
Thanks
Alejandro
Olivia Lau <olau(a)fas.harvard.edu> escribió:
Dear Alejandro,
If your data isn't full-rank, it means that you have one of the following problems:
1) You have one or more perfectly collinear variables. (e.g., x2 = x3 + 4)
2) You have a variable that consists either entirely of NA values or 0s.
Some basic data checking (try summary(), is.na(), etc) will clear this up. See
http://gking.harvard.edu/zelig/docs/Data_Sets.html for some guildelines.
Best,
Olivia
----- Original Message -----
From: Alejandro Buren
To: zelig(a)latte.harvard.edu
Sent: Monday, August 15, 2005 5:36 PM
Subject: [zelig] non full-rank models in mlogit
Thanks to all, I have overcome my previous problem.
I'm writing now because I have data which doesn't fulfill the vglm requirement
of being full-rank. Is there a way to fit a multinomial logit non full-rank model?
Thanks,
Alejandro
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