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*****************************************

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