I have it,

I have used a simply programmation using loops....

I have to run the multiple lm with at least 3 variables (because it needs
at least 2 degrees of freedom).

Many Thanks


2012/5/11 Trying To learn again <tryingtolearnagain@gmail.com>

> Hi all,
>
> I have I got it. I see the way on my mind. I will try this week end and if
> I get it I will send.
>
> Thanks.
>
>
> 2012/5/10 Trying To learn again <tryingtolearnagain@gmail.com>
>
>> Hi all,
>>
>> I want to make the following:
>>
>> I want to run  a linear regression on each column of a matrix "estima" on
>> the correspondent column on the matrix "estima2".
>>
>> You see I want to regress estima[,1] on estima2[,1] this way to all
>> columns....
>>
>> At the same time I want to make a regression adding each time a new
>> observation.
>>
>>  You see, the first regression will regress only one observation with
>> one observation (I now this has no sense in this only one observation step)
>>
>> the second turn of observation will make
>>
>> estima[1:2,n] on estima2[1:2,n]  for all "n".
>>
>> Third stimation will make
>>
>> estima[1:3,n] on estima2[1:3,n]  for all "n".
>>
>> And so on.
>>
>> Make this, I want to make an output matrix on each "t-value" associated
>> with the "regressor".
>>
>> Conclusion my final matrix called "t value" should include al the t
>> values on the regression each of them incorporating a new observation, with
>> the same rows and colums than "estima".
>>
>> I have tried several thing but I cannot achive.
>>
>> I writte to see if you can guide me¡¡¡
>>
>> I swear I´m trying.
>>
>>
>>
>> randz<-matrix(rnorm(5000),50,100)
>>
>> H<-matrix(0,50,100)
>>
>> H[1,]<-randz[1,]
>> for (i in 2:50){
>>  if(i < 26) {
>>    H[i, ] <- 0.6 * H[i-1, ] + randz[i, ]
>>  } else {
>>    H[i, ] <- H[i-1, ] + randz[i, ]
>>  }
>> }
>>
>>
>> write.table(H, file = "datad.txt")
>> g<-read.table("datad.txt")
>>
>> hy<-nrow(g)-1
>> estima<-H[2:nrow(g), ]
>> estima2<-H[ 1:hy, ]
>>
>> mycoef <- function (x,y)
>> a<-estima
>> b<-estima2
>> f<-summary(lm(a~b))
>> ff<-coef(f)
>> ff[2,"t value"]
>> tvalue <- sapply (2:ncol(b) , function (i){
>> y<-a[,i]
>> x<-b[,i]
>> mycoef(x,y)
>>
>> }
>> )
>> print (summary(tvalue))
>>
>>
>>
>

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