Michael -
I think this does what you want:
helm.raw <-
read.table("http://euclid.psych.yorku.ca/datavis/Private/mdshelm.dat",header=TRUE,
row.names=1)
trans =
c('A'='RPur','C'='Red','E'='Yel','G'='Gy1','I'='Gy2','K'='Green','M'='Blue','O'='BlP','Q'='Pur1','S'='Pur2')
cnames = do.call(rbind,strsplit(rownames(helm.raw), ""))
cnames = apply(cnames,2,function(x)trans[x])
uu = unique(as.vector(cnames))
onecol = function(col){
themat = matrix(NA,10,10)
dimnames(themat) = list(uu,uu)
themat[cnames] = col
as.dist(t(themat))
}
result = lapply(as.data.frame(helm.raw),onecol)
result$CD1
RPur Red Yel Gy1 Gy2 Green Blue BlP Pur1
Red 11.5
Yel 13.1 6.0
Gy1 12.6 7.9 6.2
Gy2 10.6 8.4 8.4 5.2
Green 10.6 9.4 9.9 6.5 4.1
Blue 10.8 10.2 10.3 8.8 7.0 6.4
BlP 7.3 11.3 12.7 11.2 10.4 9.9 4.2
Pur1 5.4 11.5 12.9 11.7 10.8 9.4 8.4 4.5
Pur2 5.0 11.5 10.7 10.2 10.6 10.1 8.1 6.4 3.0
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Tue, 22 Mar 2011, Michael Friendly wrote:
I have a 45 x 16 data frame consisting of dissimilarities among 10 colors,
giving in each
column the 45 = 10*9/2 pairwise judgments for one of 16 subjects. The
rownames
identify each pair of colors, e.g, "AC" = ("A","C"), and the pairs are
ordered by columns
in the lower triangle of each distance matrix.
helm.raw <-
read.table("http://euclid.psych.yorku.ca/datavis/Private/mdshelm.dat";,
header=TRUE, row.names=1)
head(helm.raw)
N1 N2 N3 N4 N5 N6a N6b N7 N8 N9 N10 CD1 CD2a CD2b CD3
CD4
AC 6.8 5.9 7.1 7.5 6.6 5.2 5.8 6.2 7.5 6.0 9.2 11.5 9.3 9.0 10.4
9.9
AE 12.5 11.1 10.2 10.3 10.5 9.4 10.5 10.8 9.1 9.4 10.8 13.1 10.7 10.0 12.4
13.2
AG 13.8 18.8 11.1 10.7 10.2 11.4 13.4 9.9 10.2 9.5 9.7 12.6 10.7 10.4 12.8
12.3
AI 14.2 17.3 12.5 11.6 9.6 13.3 14.0 11.1 12.1 9.5 10.1 10.6 11.9 10.0 13.7
11.1
AK 12.5 16.6 11.8 10.6 10.8 12.0 13.2 10.3 12.5 9.8 10.3 10.6 11.0 9.3 11.8
8.7
AM 11.0 16.5 9.9 9.7 9.7 12.3 11.7 8.8 9.7 8.7 9.7 10.8 9.8 8.6 4.3
5.6
row.names(helm.raw)
[1] "AC" "AE" "AG" "AI" "AK" "AM" "AO" "AQ" "AS" "CE" "CG" "CI" "CK" "CM"
"CO" "CQ" "CS" "EG" "EI" "EK"
[21] "EM" "EO" "EQ" "ES" "GI" "GK" "GM" "GO" "GQ" "GS" "IK" "IM" "IO" "IQ"
"IS" "KM" "KO" "KQ" "KS" "MO"
[41] "MQ" "MS" "OQ" "OS" "QS"
To analyse this (with individual differences MDS, e.g., smacofDiff()), I need
to:
(a) convert this to a list of objects of class "dist", one for each column of
helm.raw
(b) rename the 1-letter codes to color name abbreviations as row/col labels
for each distance matrix,
according to:
'A'='RPur'
'C'='Red'
'E'='Yel'
'G'='Gy1'
'I'='Gy2'
'K'='Green'
'M'='Blue'
'O'='BlP'
'Q'='Pur1'
'S'='Pur2'
I've done this in SAS, but I don't know how to do it in R because neither
dist() nor
as.dist() seem to be able to work with data in this format. I could try
brute-force,
but maybe there is an easier way. Can someone help?
As a distance matrix, the column helm.raw$CD1 for subject CD1 should appear
something like
shown below (without the Obs column, where stim is the rowname)
--------------------------------- Subject=CD1
----------------------------------
Obs stim RPur Red Yel Gy1 Gy2 Green Blue BlP Pur1 Pur2
1 RPur . . . . . . . . . .
2 Red 11.5 . . . . . . . . .
3 Yel 13.1 6.0 . . . . . . . .
4 Gy1 12.6 7.9 6.2 . . . . . . .
5 Gy2 10.6 8.4 8.4 5.2 . . . . . .
6 Green 10.6 9.4 9.9 6.5 4.1 . . . . .
7 Blue 10.8 10.2 10.3 8.8 7.0 6.4 . . . .
8 BlP 7.3 11.3 12.7 11.2 10.4 9.9 4.2 . . .
9 Pur1 5.4 11.5 12.9 11.7 10.8 9.4 8.4 4.5 . .
10 Pur2 5.0 11.5 10.7 10.2 10.6 10.1 8.1 6.4 3 .
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
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and provide commented, minimal, self-contained, reproducible code.
