I didn't see bootstrap steps in your code. One way to modify your codes
for (Ncount in 1:100)
{
b.data<-data[sample(1:nrow(data),replace=T),]
y <-b.data[,1]
x <-b.data[,2]
n <- length(x)
... ### make appropriate changes if needed
}
Weidong Gu
On Wed, Jul 20, 2011 at 6:09 PM, Val <valkr...@gmail.com> wrote:
> Hi all,
>
> I am facing difficulty on how to use bootstrap sampling and
> below is my example of function.
>
> Read a data , use some functions and use iteration to find the solution(
> ie, convergence is reached). I want to use bootstrap approach to do it
> several times (200 or 300 times) this whole process and see the
> distribution of parameter of interest.
>
> Below is a small example that resembles my problem. However, I found out
> all samples are the same. So I would appreciate your help on this case.
>
> #**************************************
> rm(list=ls())
> xx <- read.table(textConnection(" y x
> 11 5.16
> 11 4.04
> 14 3.85
> 19 5.68
> 4 1.26
> 23 7.89
> 15 4.25
> 17 3.94
> 7 2.35
> 17 4.74
> 14 5.49
> 11 4.12
> 17 5.92"), header=TRUE)
> data <- as.matrix(xx)
> closeAllconnections()
>
> Nt <- NULL
> for (Ncount in 1:100)
> {
> y <- data[,1]
> x <- data[,2]
> n <- length(x)
>
> X <- cbind(rep(1,n),x) #covariate/design matrix
> obeta<- c(1,1) #previous/starting values of beta
>
> nbeta <- c(0,0) #new beta
> iter=0
>
> while(crossprod(obeta-nbeta)>10^(-12))
> {
> nbeta <- obeta
> eta <- X%*%nbeta
> mu <- eta
> mu1 <- 1/eta
> W <- diag(as.vector(mu1))
> Z <- X%*%nbeta+(y-mu)
> XWX <- t(X)%*%W%*%X
> XWZ <- t(X)%*%W%*%Z
> Cov <- solve(XWX)
> obeta <- Cov%*%XWZ
> iter <- iter+1
>
> cat("Iteration # and beta1= ",iter, nbeta, "\n")
> }
>
> Nt[Ncount] <- nbeta[1,1]
> }
> Nt
> summary(Nt)
> #**************e*****************************************
>
> [[alternative HTML version deleted]]
>
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>
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