I found no fruitful suggestions as yet, therefore I have devised a simple
mechanism for that.
Here I can modify my model as : Y = X*a + error, X = (X1, X2), a = t(a1, a2)
Now I can apply the standard LS procedure, to estimate a. Here is my code :
Y <- replicate(10, matrix(rnorm(2),2), simplify = F)
X1 <- replicate(10, matrix(rnorm(4),2), simplify = F)
X2 <- replicate(10, matrix(rnorm(4),2), simplify = F)
X <- lapply(1:10, function(i) cbind(X1[[i]], X2[[i]]))
temp1 <- Reduce("cbind", lapply(X, "t")); temp2 <- Reduce("rbind", X); temp3
<- Reduce("rbind", Y)
a <- solve(temp1%*%temp2) %*% (temp1%*%temp3); a
Can I go ahead with this procedure? Somebody please validate this?
Thanks
megh wrote:
>
> Hi, I have following kind of model : Y = X1 * a1 + X2 * a2 + error
>
> Here sampled data for Y, X1, X2 are like that :
>
> Y <- replicate(10, matrix(rnorm(2),2), simplify = F)
> X1 <- replicate(10, matrix(rnorm(4),2), simplify = F)
> X2 <- replicate(10, matrix(rnorm(4),2), simplify = F)
>
> My goal is to calculate LS estimates of vectors "a1" and "a2". Can anyone
> please guide me how to do that in R? Is there any special function to
> handle this kind of problem?
>
> Thanks
>
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