This is my approach:
If "cosa" is your data.frame . e.g.
>cosa
i1 i2 dis
[1,] 1 1 0.00
[2,] 1 2 0.93
[3,] 1 3 0.80
[4,] 1 4 1.00
[5,] 2 2 0.00
[6,] 2 3 0.02
[7,] 2 4 0.22
[8,] 3 3 0.00
[9,] 3 4 0.95
[10,] 4 4 0.00
# first crate a square matrix of 0´s, of the
appropriate dimensions (in this example, 4)
pepe <- matrix(0, nr=4, nc=4)
# then assign your long disimilarity data to trhe
lower tree of the square matrix
# check the diag argument!
pepe[lower.tri(pepe, diag=T)] <- cosa[,3]
# then transpose the matrix
pepe <- t(pepe)
# and symmetrize the resulting matrix
library(calibrator)
pepe <- symmetrize(pepe)
# finaly, transform to a dist matrix:
pepe <- as.dist (pepe)
HTH,
Marcelino
kirsten-be...@uiowa.edu wrote:
I have a dissimilarity dataset with the form:
1 1 dissimilarity value
1 2 ...
1 3
1 4
2 2
2 3
2 4
...
I would like to do nonmetric multidimensional scaling with this data, but I
am having trouble using this format. I would like to either find a function
that accepts this format or find a way to easily convert this format to a
matrix for use with existing functions.
Thanks!
________________________________
Marcelino de la Cruz Rot
Departamento de Biología Vegetal
E.U.T.I. Agrícola
Universidad Politécnica de Madrid
28040-Madrid
Tel.: 91 336 54 35
Fax: 91 336 56 56
marcelino.delac...@upm.es
_________________________________
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