diagonal matrices

Have a look at adiag in the magic package.

Hello all,

I have matrices V.i  of dimension n.i x n.i, where i = 1, ..., J, and the  sum of n.i  equals N.  (and n.i ! = n.j)

goal: create one large matrix V, where V has matrices V.i on diagonal.

I create each matrix V.i in a for loop (1 to J), so each time I'd like to augment V with the
most recently calculated V.i, such that I'll have V after the final iteration of the for loop.

question: how to initialize V and do the augmentation.   any advice much appreciated.

cheers,
dave

ps - please respond directly to afshar at miami.edu, thanks!



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I have matrices V.i of dimension n.i x n.i, where i = 1, ..., J, and the 
sum of n.i equals N.  (and n.i ! = n.j)

goal: create one large matrix V, where V has matrices V.i on diagonal.

I create each matrix V.i in a for loop (1 to J), so each time I'd like 
to augment V with the most recently calculated V.i, such that I'll have 
V after the final iteration of the for loop.

question: how to initialize V and do the augmentation.  any advice much 
appreciated.

It is probably best to pre-create a matrix and fill in the blocks.  As in

V <- matrix(0, N, N)
# let n be a vector of what you called n.i
n0 <- c(0, cumsum(n))
for(i in 1:J) {
    ind <- (n0[i]+1):n0[i+1]
    V[ind, ind] <- V.i
}

cheers,
dave

ps - please respond directly to afshar at miami.edu, thanks!

Please set that as your reply address to ease the lot of your helpers.

Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

Hello, I have posted this mail a few days ago without any answer:

I have the following doubts related with lm.ridge, from MASS package. To
show the problem using the Longley example, I have the following doubts:

First: I think coefficients from lm(Employed~.,data=longley) should be
equal coefficients from lm.ridge(Employed~.,data=longley, lambda=0) why
it does not happen?
Second: if I have for example
Ridge<-lm.ridge(Employed~., data=longley, lambda = seq(0,0.1,0.001)), I
suppose intercept coefficient is defined implicit, why it does not
appear in Ridge$coef?

Third: I suppose that if I define
1) y<-longley$Employed
2) X<-as.matrix(cbind(1,Longley[,1:6])
3) I as the identity
the following should be true:
         Coef=(X'X+kI)^(-1) X'y
and if a take k=Ridge$kHKV, the coefficients should be approx equal to
Ridge$Coef[near value of kHKV] and it does not seem to happen, why?

Values:

Ridge$kHKB

[1] 0.004275357

Using the calculation above (third question, third point):
Coef=
                      [,1]
  1            -0.095492310
  GNP.deflator -0.052759002
  GNP           0.070993540
  Unemployed   -0.004244391
  Armed.Forces -0.005725582
  Population   -0.413341544
  Year          0.048420107

And if I take from Ridge&coef:

Ridge$coef[0.004]
GNP.deflator -0.03098507
GNP -1.32553151
Unemployed -1.53237769
Armed.Forces -0.63334911
Population -0.88690241
Year 6.82105049

Any help, suggestion or orientation?
Thanks in advance
Daniel Rozengardt