Thanks very much Andy for the code and the
explanation.
The meaning of AC is much more clear now.
I did notice, when I tried the code, the results were
not exactly the same as yours.
> sapply(c(.25,.5), testAC, x=x[1:4],
method="single")
Loading required package: cluster
Error in FUN(X[[1]], ...) : Object "x" not found
> x=rnorm(50)
> sapply(c(.25,.5), testAC, x=x[1:4],
method="single")
[1] 0.7450599 0.9926918
> version
_
platform i686-pc-linux-gnu
arch i686
os linux-gnu
system i686, linux-gnu
status
major 2
minor 0.1
year 2004
month 11
day 15
language R
Regards,
Weiguang
--- "Liaw, Andy" <[EMAIL PROTECTED]> wrote:
> It has to do with sample sizes. Consider the
> following:
>
> testAC <- function(prop1=0.5, x=rnorm(50),
> center=c(0, 100), ...) {
> stopifnot(require(cluster))
> n <- length(x)
> n1 <- ceiling(n * prop1)
> n2 <- n - n1
> agnes(x + rep(center, c(n1, n2)), ...)$ac
> }
>
> Now some tests:
>
> > sapply(c(.25, .5), testAC, x=x[1:4],
> method="single")
> [1] 0.7427591 0.9862944
> > sapply(1:5 / 10, testAC, x=x[1:10],
> method="single")
> [1] 0.8977139 0.9974224 0.9950061 0.9946366
> 0.9946366
> > sapply(1:5 / 10, testAC, x=x, method="single")
> [1] 0.9982955 0.9969757 0.9971114 0.9971127
> 0.9975111
>
> So it seems like AC does not consider isolated
> singletons as cluster
> structures. This is only discernable in small
> sample size, though.
>
> Andy
>
>
>
> > --- "Liaw, Andy" <[EMAIL PROTECTED]> wrote:
> > > BTW, I checked the book. You're not going find
> much
> > > more than that.
> > >
> > Thanks for checking.
> >
> > Weiguang
> >
> >
>
______________________________________________________________
> > ________
> > Post your free ad now! http://personals.yahoo.ca
> >
> >
>
>
>
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