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
On 21-Aug-09 18:12:45, megh wrote:
>
> "Y" is 2 dimensional vector, X1 and X2 are 2x2 matrices.
Sorry -- I mis-read your code! I will think about your problem.
Apologies.
Ted.
> Ted.Harding-2 wrote:
>>
>> On 21-Aug-09 16:28:26, megh wrote:
>>> Hi, I have following kind of model : Y = X1 * a1 +
"Y" is 2 dimensional vector, X1 and X2 are 2x2 matrices.
Ted.Harding-2 wrote:
>
> On 21-Aug-09 16:28:26, 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), simpli
On 21-Aug-09 16:28:26, 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
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 calcu
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