Bernd Dittmann wrote:
>
> (Sample boot code see below)
> ...
> So far so good. But how can I bring it into table-form whereby the
> combination of all 10 different weights (setting steps as 1) and their
> respective bootstrapped means are printed:
>
> weights mean.boot
> 1 ....
> 2 ....
> ... ....
> 10 ....
>
>
Divide et impera.
Dieter
x <- rnorm(100, mean=100, sd=4)
y <- rnorm(100, mean=120, sd=10)
# Step 1: use a variables instead of constants
n1 = 8
maxn = 10
portfolio <- c(rep(x, n1), rep(y, maxn-n1))
mean.boot <- function(z){
mean(sapply(lapply(1:20, function(i)sample(z, replace=F, size=30)),mean))}
mean.boot(portfolio)
# Step 2 : Wrap it in a function and test it
meanBoot <- function(n1) {
portfolio <- c(rep(x, n1), rep(y, 10-n1))
mean.boot <- function(z){
mean(sapply(lapply(1:20, function(i)sample(z, replace=F,
size=30)),mean))}
mean.boot(portfolio)
}
# Test it
meanBoot(8)
# Step 3: The c-programmer's way
res = data.frame()
n1s = 1:(maxn-1)
for (n1 in n1s) {
res <- rbind(res,data.frame(n1 = n1, mean =meanBoot(n1)))
}
res
# Room for improvement here: use one of the Xapply functions here, or
# pre-allocate res if you have many data
lapply(n1s, meanBoot)
# ugly output, let's simplify
sapply(n1s, meanBoot)
# looks better, decorate it!
data.frame(n= n1s, mean=sapply(n1s, meanBoot))
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