Thank you for your response. Each v can take a a value from 1 to 100.
Input data subset: v1.data
v1 v2 v3 v4 v5
1 1 59 1 51 52
2 1 59 1 7 83
3 1 59 1 46 11
4 1 99 1 84 11
5 1 59 1 31 4
6 1 99 1 81 3
7 1 27 1 58 68
8 61 59 78 73 75
9 66 71 80 39 68
10 1 99 1 23 34
v1.data$v1 <- as.factor(v1.data$v1)
mlogit.v1.data<-mlogit.data(
v1.data, varying=NULL, choice="v1", shape="wide")
v1.model<-mlogit(v1~1|v2+v3+v4+v5, data=mlogit.v1.data, reflevel="1")
On Thu, Apr 19, 2012 at 1:38 PM, R. Michael Weylandt <
michael.weyla...@gmail.com> wrote:
> There's something in your data that makes the model computationally
> singular when you take the various subsettings... Can you provide a
> small reproducible example so we can help narrow it down? It looks
> like you're using different data for each mlogit though so I'm not
> sure how the comparison that v2 & v5 fail while the other works is
> relevant though...
>
> Michael
>
> On Thu, Apr 19, 2012 at 4:03 PM, geek girl <geek.girl.o...@gmail.com>
> wrote:
> > I am learning five mlogits as follows
> >
> > v1.model<-mlogit(v1~1|v2+v3+v4+v5, data=mlogit.v1.data, reflevel="1")
> > v2.model<-mlogit(v2~1|v1+v3+v4+v5, data=mlogit.v2.data, reflevel="1")
> > v3.model<-mlogit(v3~1|v1+v2+v4+v5, data=mlogit.v3.data, reflevel="1")
> > v4.model<-mlogit(v4~1|v1+v2+v3+v5, data=mlogit.v4.data, reflevel="1")
> > v5.model<-mlogit(v5~1|v1+v2+v3+v4, data=mlogit.v5.data, reflevel="1")
> >
> > v2 and v5 give me the error below during learning, the other 3 models
> work
> > fine
> >
> > "Error in solve.default(H, g[!fixed]) : system is computationally
> singular:
> > reciprocal condition number = 1.12239e-16"
> >
> > What does this error mean?
> >
> > Thanks
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
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