Absolutely right!
Thanks to both David for their help.
Cheers
Lorenzo
On Fri, Sep 25, 2015 at 01:54:54PM +0000, David L Carlson wrote:
You defined x and y in your original email as:
x<-rnorm(20)
y<-rnorm(20)
mm<-as.matrix(cbind(x,y))
dst<-(dist(mm))
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Thursday, September 24, 2015 6:30 PM
To: Lorenzo Isella
Cc: David L Carlson; r-help@r-project.org
Subject: Re: [R] Sampling the Distance Matrix
On Sep 24, 2015, at 1:54 PM, Lorenzo Isella wrote:
On Thu, Sep 24, 2015 at 01:30:02PM -0700, David Winsemius wrote:
On Sep 24, 2015, at 12:36 PM, Lorenzo Isella wrote:
Hi,
And thanks for your reply.
Essentially, your script gets the job done.
For instance, if I run
mm <- cbind(5/(1:5), -2*sqrt(1:5))
dst <- dist(mm)
dst2 <- as.matrix(dst)
diag(dst2) <- NA
idx <- which(apply(dst2, 1, function(x) all(na.omit(x)>.9)))
then it correctly detects the first two rows, where all the values are
larger than 0.9.
In other words, it detects the points that are at least 0.9 units away
from *all* the other points.
My other question (I did not realize this until I got your answer) is
the following: I have the distance matrix of a set of N points.
You gave me an algorithm two find all the points that are at least 0.9
units away from any other points.
However, in some cases, for me it is OK even a weaker condition: find
a subset of k points (with k tunable) whose distance *from each other*
is greater than 0.9 units (even if their distance from some other
points may be smaller than 0.9).
If I understand ..... Make a matrix of unique combinations, then apply by rows
to get the qualifying columns that satisfy the distance criterion:
mtxcomb <- combn(1:20, 5)
goodcls <- apply(mtxcomb , 2, function(idx) all( dist( cbind( x[idx], y[idx]) )
> 0.9))
mtxcomb [ , goodcls]
In my sample it was around 9% of the total 5 item combinations.
snipped a lot of output:
.....
[,1440] [,1441]
[1,] 12 13
[2,] 13 16
[3,] 16 17
[4,] 19 19
[5,] 20 20
dim( mtxcomb)
[1] 5 15504
Hi,
Thanks for your reply.
I think I am getting there, but when I run your commands, I get this
error message
Error in cbind(x[idx], y[idx]) : object 'x' not found
Any idea why? Should I combine those 3 lines with something else?
No idea. I was running the setup that you asked for in your original message
which you have now omitted from the mail chain.
Cheers
Lorenzo
David Winsemius
Alameda, CA, USA
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