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On Fri, Jul 30, 2010 at 8:14 AM, leepama <butchman@hanmail.net> wrote:

>
> 1)
> dmatrix1<-function(n,p,rho,sigma,k){
>  muvec1=zeros(1,p)
>  truep<-as.matrix(c(3,1.5,0,0,2,0,0,0))
>  A=eye(p)
>  for(i in 1:p){
>   for(j in 1:p){
>    A[i,j]=rho^(abs(i-j))
>     X=mvrnorm(n,muvec1,A)
>     y=X%*%truep+as.matrix(rnorm(n,0,sigma))
>     Y=X[1:k,]
>     w=y[1:k]
>     Z=X[(k+1):n,]
>     z=y[(k+1):n]}}
>  return(list(X,Y,w,Z,z))
> }
> I made this code which performs design matrix..
> Can you help me how to make several data set....??
> the code i made compute only one data set...
>
> 2) we generate 50 data with 30 variables. The true regression
> vector is such that
> bj = 3 − 0.1j j = 1, . . . , 10,
> bj = −5 + 0.3j j = 20, . . . , 25,
> bj = 0 for the others j.
> The noise is such that sigma = 9 and the correlations are such that (j, k)
> =
> exp (−|j−k|/2  for (j, k) ∈ {11, . . . , 25}2 and the others variables are
> i.i.d. N(0, 1),
> also independent from x11, . . . , x25
> how to simulate this data set....
>
> 3)we simulated 50 data sets and 40 predictors.
>
> β=(3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
> and σ=15. The predictors X were generated as follows:
> xi=Z1+"xi
> , Z1∼N.(0,1), i=1, . . . ,5,
> xi=Z2+"xi
> , Z2∼N.(0,1), i=6, . . . ,10,
> xi=Z3+"xi
> , Z3∼N.(0,1), i=11, . . . ,15,
> xi∼N.(0,1), xi independent identically distributed, i=16, : : : , 40
> how to simulate this data set....
>
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