David LeBauer wrote:
Jorge,
Thanks for your help. which.min() on the sorted vector divided by the
vector length gave me the value I was looking for (I was looking for
the probability p(mean(x) | x)):
x <- runif(1000)
x.sort <- sort(x)
x.length <- length(x)
x.mean <- mean(x)
p.mean <- which.min((x.sort - x.mean)^2) / x.length
Another user pointed out a function suited for this purpose, findInterval()
p.mean <- findInterval(x.mean, xsort) / x.length
Thanks for your help,
David
No need to sort:
x <- runif(1000)
p.mean <- mean(x <= mean(x))
J. R. M. Hosking
On Thu, Jun 17, 2010 at 10:15 PM, Jorge Ivan Velez
<jorgeivanve...@gmail.com> wrote:
Hi David,
You might try:
set.seed(1)
x <- runif(10, 3, 7)
x
[1] 4.062035 4.488496 5.291413 6.632831 3.806728 6.593559 6.778701 5.643191
5.516456 3.247145
(x-mean(x))^2
[1] 1.308783661 0.514892188 0.007285983 2.035688832 1.958118177 1.925165288
2.473214156
[8] 0.191087609 0.096348590 3.837329960
which.min((x-mean(x))^2)
[1] 3
x[which.min((x-mean(x))^2)]
[1] 5.291413
which.min(scale(x, scale = FALSE)**2)
[1] 3
See ?which.min and ?scale for more information.
HTH,
Jorge
On Thu, Jun 17, 2010 at 7:06 PM, David LeBauer <> wrote:
Hello,
I am interested in finding the quantile of the mean of a vector,
something analogous to using the pnorm(), but for an mcmc chain
instead of a distribution with known parameters.
One approach would be to write a function that finds the index of x_i
that minimizes (x-mean(x))^2
I suspect there is a function available to do this, but I can't find it?
Thank you,
David
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