Any reference to the appropriate documentation would
be most appreciated.
I am using the TSP module for clustering of HIV
genetic sequences. The distances have already been
computed and available as either upper-triangular
or square, i.e.:
a 1 2 3
b 4 5
c 6
d
or
a 0 1 2 3
b 1 0 4 5
c 2 4 0 6
d 3 5 6 0
The TSP modules takes in a "dist" object.
Catch: The only way I can see to get a dist
object is with dist(), which computes the
distances for itself rather than taking them
as-is.
Q: How does one convert either of the strucutres
above into a "dist" object without having to
first feed them through dist()?
I can easily split the labels into a seprate
output file, leaving me with the rownames
and colnames values for the result in a separate
place if that makes explaining how to get the
numeric values into a dist any easier.
Google, searching r-project.org, and the R
Nutshell book all lead me back to dist() or
daisy().
thanks
--
Steven Lembark 85-09 90th St.
Workhorse Computing Woodhaven, NY, 11421
lembark at wrkhors.com +1 888 359 3508
Where is the construction of a dist object from raw data described?
2 messages · Steven Lembark, Gavin Simpson
On Wed, 2010-05-19 at 10:10 -0400, Steven Lembark wrote:
Any reference to the appropriate documentation would
be most appreciated.
I am using the TSP module for clustering of HIV
genetic sequences. The distances have already been
computed and available as either upper-triangular
or square, i.e.:
a 1 2 3
b 4 5
c 6
d
or
a 0 1 2 3
b 1 0 4 5
c 2 4 0 6
d 3 5 6 0
The TSP modules takes in a "dist" object.
Catch: The only way I can see to get a dist
object is with dist(), which computes the
distances for itself rather than taking them
as-is.
Q: How does one convert either of the strucutres
above into a "dist" object without having to
first feed them through dist()?
as.dist() will convert the square matrix into a dist object I'm not sure of a convenient way of importing data in the "upper" form you show without having to go via a matrix, or build the dist object by hand, so if this really is an either/or situation and the square form is always available and it is not a problem loading the dissimilarity matrix into RAM, I'd stick with as.dist. HTH G
I can easily split the labels into a seprate output file, leaving me with the rownames and colnames values for the result in a separate place if that makes explaining how to get the numeric values into a dist any easier. Google, searching r-project.org, and the R Nutshell book all lead me back to dist() or daisy(). thanks -- Steven Lembark 85-09 90th St. Workhorse Computing Woodhaven, NY, 11421 lembark at wrkhors.com +1 888 359 3508
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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