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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