R help - Jan 2014 - How can I cluster a set of formulae?

I am working on a problem in which I have derived a set of D  formulae
relating a different dependent variable to a grouping of independent variables.


D1 = intercept + ax1 + bx2 + bx3 + bx4
D2 = intercept + ex2 + fx7 + gx8
D3= intercept + hx1 + ix3 + jx7

etc to ... D8.

I have 3 categorical variables P, Q and A  [which are actually hierarchical
with A within Q with P, each containing a different number of classes]  ? I
want to look at each of the categorical variables as a separate issue
clustering each of the D formulae into classes, so I can say something about
how the D's vary / interact across classes.

Intuitively this seems to be a discriminant function problem because the
classes are already known.  However, a PCA or FA might be necessary ? and
then do a DFA on the clusters.  Either way I am not sure how to set it up or
even if I can interpret it to make sense.

Alternatively, I might be climbing up/down the wrong tree [pun intended]. 
Other methods might be better.

Help!

George F. Hart

I'm sending this to a number of statistics groups so apologize if you get
this note more than once.

SO (stats.stackexchange.com) is the better list for methodological
issues like this.

Cheers,
Bert

Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374

"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch




On Sun, Jan 26, 2014 at 1:33 PM, Professor George F.Hart <ghart at
lsu.edu> wrote:
> I am working on a problem in which I have derived a set of D  formulae
> relating a different dependent variable to a grouping of independent
variables.
>
>
> D1 = intercept + ax1 + bx2 + bx3 + bx4
> D2 = intercept + ex2 + fx7 + gx8
> D3= intercept + hx1 + ix3 + jx7
>
> etc to ... D8.
>
> I have 3 categorical variables P, Q and A  [which are actually hierarchical
> with A within Q with P, each containing a different number of classes]  ? I
> want to look at each of the categorical variables as a separate issue
> clustering each of the D formulae into classes, so I can say something
about
> how the D's vary / interact across classes.
>
> Intuitively this seems to be a discriminant function problem because the
> classes are already known.  However, a PCA or FA might be necessary ? and
> then do a DFA on the clusters.  Either way I am not sure how to set it up
or
> even if I can interpret it to make sense.
>
> Alternatively, I might be climbing up/down the wrong tree [pun intended].
> Other methods might be better.
>
> Help!
>
> George F. Hart
>
> I'm sending this to a number of statistics groups so apologize if you
get
> this note more than once.
>
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