When it comes down to estimating parameters the standard error is often forgotten. This is a slightly more complex way to estimate the standard error. However, this is a good approach when dealing with complex samples. This is a simple example of how to calculate a jackknife standard error. This approach can be extended to a cluster samples.
x = rbeta(100,runif(1,0,10),runif(1,0,10));
x = cbind(x);
msum = matrix(NA,nrow=length(x),ncol=1);
for(i in 1:length(x)){
msum[i,1] = (mean(x[-i]) – mean(x))^2;
}
jk.var = (length(msum)-1)/length(msum)*sum(msum[,1]);
jk.se = sqrt(jk.var);
se = sd(x[,1]);