Hi,
I am afraid you misunderstood it. I do not have repeated records, but
for every record I have two, possibly different, simultaneously
present, instanciations of an explanatory variable.
My data is as follows :
yield haplo1 haplo2
100 A B
151 B A
212 A A
So I have one effect (haplo), but two copies of each affect "yield".
If I use lm() I get:
> a=data.frame(yield=c(100,151,212),haplo1=c("A","B","A"),haplo2=c("B","A","A"))
Call:
lm(formula = yield ~ -1 + haplo1 + haplo2, data = a)
Coefficients:
haploA haploB haplo2B
212 151 -112
But I get different coefficients for the two "A"s (in fact oe was set
to 0) and the Two "Bs" . That is, the model has four unknowns but in
my example I have just two!
A least-squares solution is simple to do by hand:
X=matrix(c(1,1,1,1,2,0),ncol=2) #the incidence matrix
> X
[,1] [,2]
[1,] 1 1
[2,] 1 2
[3,] 1 0
> solve(crossprod(X,X),crossprod(X,a$yield))
[,1]
[1,] 184.8333
[2,] -30.5000
where [1,] is the solution for A and [2,] is the solution for B
This is not difficult to do by hand, but it is for a simple case and I
miss all the machinery in lm()
Thank you
Andres
On Wed, Mar 19, 2008 at 6:57 PM, Michael Dewey <[EMAIL PROTECTED]> wrote:
> At 09:11 18/03/2008, Andres Legarra wrote:
> >Dear all,
> >I have a data set (QTL detection) where I have two cols of factors in
> >the data frame that correspond logically (in my model) to the same
> >factor. In fact these are haplotype classes.
> >Another real-life example would be family gas consumption as a
> >function of car company (e.g. Ford, GM, and Honda) (assuming 2 cars by
> >family).
>
> Unless I completely misunderstand this it looks like you have the
> dataset in wide format when you really wanted it in long format (to
> use the terminology of ?reshape). Then you would fit a model allowing
> for the clustering by family.
>
>
>
>
> >An artificial example follows:
> >set.seed(1234)
> >L3 <- LETTERS[1:3]
> >(d <- data.frame( y=rnorm(10), fac=sample(L3, 10,
> >repl=TRUE),fac1=sample(L3,10,repl=T)))
> >
> > lm(y ~ fac+fac1,data=d)
> >
> >and I get:
> >
> >Coefficients:
> >(Intercept) facB facC fac1B fac1C
> > 0.3612 -0.9359 -0.2004 -2.1376 -0.5438
> >
> >However, to respect my model, I need to constrain effects in fac and
> >fac1 to be the same, i.e. facB=fac1B and facC=fac1C. There are
> >logically just 4 unknowns (average,A,B,C).
> >With continuous covariates one might do y ~ I(cov1+cov2), but this is
> >not the case.
> >
> >Is there any trick to do that?
> >Thanks,
> >
> >Andres Legarra
> >INRA-SAGA
> >Toulouse, France
>
> Michael Dewey
> http://www.aghmed.fsnet.co.uk
>
>
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