Rui:
Try reading it again. Orthogonal polynomials are generated (subject to
the caveats regarding machine precision stated therein). Note,
especially, the "raw" argument.
Cheers,
Bert
On Thu, Mar 15, 2012 at 1:06 PM, Rui Barradas <rui1...@sapo.pt> wrote:
> Hello, again.
>
>>
>> This is not really working. Here what I have for the moment.
>>
>
> Right, I've read only half of the description line of function 'poly'. The
> other half states that
>
> "These are all orthogonal to the constant polynomial of degree 0."
>
> But not pairwise orthogonal.
>
> You can look for packages implementing Gram-Schmidt orthogonalization
>
> library(sos)
> r1 <- findFn('Gram')
> r2 <- findFn('Schmidt')
> r1 & r2
>
> or use a (time consuming) trick: form a matrix with the first column as x1,
> and the others as the
> residuals, which are orthogonal, of the regressions of that column with all
> the previous ones.
> Example:
>
> orthogonal <- function(x){
> x <- cbind(1, x)
> nc <- ncol(x)
> res <- matrix(NA, nrow(x), nc - 1)
> res[, 1] <- x[, 2]
> if(nc > 2){
> for(j in 3:nc)
> res[, j - 1] <- lm.fit(x[, 1:(j - 1)], x[,
> j])$residuals
> }
> res
> }
>
> set.seed(1)
> X <- matrix(1:10, ncol=1)
> X <- cbind(X, X + rnorm(10), X + rnorm(10))
> cor(X)
> [,1] [,2] [,3]
> [1,] 1.0000000 0.9726364 0.9485793
> [2,] 0.9726364 1.0000000 0.8917848
> [3,] 0.9485793 0.8917848 1.0000000
>
> Y <- orthogonal(X)
> cor(Y)
> [,1] [,2] [,3]
> [1,] 1.000000e+00 2.804232e-17 -3.758236e-17
> [2,] 2.804232e-17 1.000000e+00 -1.492894e-17
> [3,] -3.758236e-17 -1.492894e-17 1.000000e+00
>
> This works but I would use a Gram-Schmidt algorithm.
>
> Rui Barradas
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/Generation-of-correlated-variables-tp4475799p4476257.html
> Sent from the R help mailing list archive at Nabble.com.
>
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--
Bert Gunter
Genentech Nonclinical Biostatistics
Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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