Re: [R] Help with Mahalanobis

From: Gabor Grothendieck <ggrothendieck_at_gmail.com>
Date: Mon 11 Jul 2005 - 09:26:34 EST

This one adds the labels:

D2Mah4 = function(y, x) {

 stopifnot(is.data.frame(y), !missing(x))  stopifnot(dim(y)[1] != dim(x)[1])

 y    = as.matrix(y)
 x    = as.factor(x)
 man  = manova(y ~ x)
 E    = summary(man)$SS[2] #Matrix E
 S    = as.matrix(E$Residuals)/man$df.residual
 InvS = solve(S)
 mds  = matrix(unlist(by(y, x, mean)), byrow=T, ncol=ncol(y))

 f <- function(a,b) mapply(function(a,b)    mahalanobis(mds[a,],mds[b,],InvS,TRUE), a, b)  seq. <- seq(length = nrow(mds))
 names(seq.) <- levels(x)
 D2 <- outer(seq., seq., f)
}

#
# test
#

D2M4 = D2Mah4(iris[,1:4], iris[,5])
print(D2M4)

On 7/10/05, Jose Claudio Faria <joseclaudio.faria@terra.com.br> wrote:
> Indeed, it is very nice Gabor (as always)!
>
> So, a doubt: how to preserve the 'rowname' and 'colname' of D2, like in the
> first function? I think it is useful to posterior analyzes (as cluster, for
> example).
>
> Regards,
>
> # A small correction (reference to gtools was eliminated)
> D2Mah2 = function(y, x) {
> stopifnot(is.data.frame(y), !missing(x))
> stopifnot(dim(y)[1] != dim(x)[1])
> y = as.matrix(y)
> x = as.factor(x)
> man = manova(y ~ x)
> E = summary(man)$SS[2] #Matrix E
> S = as.matrix(E$Residuals)/man$df.residual
> InvS = solve(S)
> mds = matrix(unlist(by(y, x, mean)), byrow=T, ncol=ncol(y))
> nObjects = nrow(mds)
> f = function(a,b) mapply(function(a,b)
> (mds[a,] - mds[b,])%*%InvS%*%(mds[a,] - mds[b,]), a, b)
> D2 = outer(seq(nObjects), seq(nObjects), f)
> }
>
> #
> # test
> #
> D2M2 = D2Mah2(iris[,1:4], iris[,5])
> print(D2M2)
>
> Gabor Grothendieck wrote:
> > I think you could simplify this by replacing everything after the
> > nObjects = nrow(mds) line with just these two statements.
> >
> > f <- function(a,b) mapply(function(a,b)
> > (mds[a,] - mds[b,])%*%InvS%*%(mds[a,] - mds[b,]), a,b)
> >
> > D2 <- outer(seq(nObjects), seq(nObjects), f)
> >
> > This also eliminates dependence on gtools and the complexity
> > of dealing with triangular matrices.
> >
> > Regards.
> >
> > Here it is in full:
> >
> > D2Mah2 = function(y, x) {
> >
> > stopifnot(is.data.frame(y), !missing(x))
> > stopifnot(dim(y)[1] != dim(x)[1])
> > y = as.matrix(y)
> > x = as.factor(x)
> > man = manova(y ~ x)
> > E = summary(man)$SS[2] #Matrix E
> > S = as.matrix(E$Residuals)/man$df.residual
> > InvS = solve(S)
> > mds = matrix(unlist(by(y, x, mean)), byrow=T, ncol=ncol(y))
> >
> > library(gtools)
> > nObjects = nrow(mds)
> >
> > ### changed part is next two statements
> > f <- function(a,b) mapply(function(a,b)
> > (mds[a,] - mds[b,])%*%InvS%*%(mds[a,] - mds[b,]), a,b)
> >
> > D2 <- outer(seq(nObjects), seq(nObjects), f)
> > }
> >
> > #
> > # test
> > #
> > D2M2 = D2Mah2(iris[,1:4], iris[,5])
> > print(D2M2)
> >
> >
> >
> >
> > On 7/10/05, Jose Claudio Faria <joseclaudio.faria@terra.com.br> wrote:
> >
> >>Well, as I did not get a satisfactory reply to the original question I tried to
> >>make a basic function that, I find, solve the question.
> >>
> >>I think it is not the better function, but it is working.
> >>
> >>So, perhaps it can be useful to other people.
> >>
> >>#
> >># Calculate the matrix of Mahalanobis Distances between groups
> >># from data.frames
> >>#
> >># by: José Cláudio Faria
> >># date: 10/7/05 13:23:48
> >>#
> >>
> >>D2Mah = function(y, x) {
> >>
> >> stopifnot(is.data.frame(y), !missing(x))
> >> stopifnot(dim(y)[1] != dim(x)[1])
> >> y = as.matrix(y)
> >> x = as.factor(x)
> >> man = manova(y ~ x)
> >> E = summary(man)$SS[2] #Matrix E
> >> S = as.matrix(E$Residuals)/man$df.residual
> >> InvS = solve(S)
> >> mds = matrix(unlist(by(y, x, mean)), byrow=T, ncol=ncol(y))
> >>
> >> colnames(mds) = names(y)
> >> Objects = levels(x)
> >> rownames(mds) = Objects
> >>
> >> library(gtools)
> >> nObjects = nrow(mds)
> >> comb = combinations(nObjects, 2)
> >>
> >> tmpD2 = numeric()
> >> for (i in 1:dim(comb)[1]){
> >> a = comb[i,1]
> >> b = comb[i,2]
> >> tmpD2[i] = (mds[a,] - mds[b,])%*%InvS%*%(mds[a,] - mds[b,])
> >> }
> >>
> >> # Thanks Gabor for the below
> >> tmpMah = matrix(0, nObjects, nObjects, dimnames=list(Objects, Objects))
> >> tmpMah[lower.tri(tmpMah)] = tmpD2
> >> D2 = tmpMah + t(tmpMah)
> >> return(D2)
> >>}
> >>
> >>#
> >># To try
> >>#
> >>D2M = D2Mah(iris[,1:4], iris[,5])
> >>print(D2M)
> >>
> >>Thanks all for the complementary aid (specially to Gabor).
> >>
> >>Regards,
> >>--
> >>Jose Claudio Faria
> >>Brasil/Bahia/UESC/DCET
> >>Estatistica Experimental/Prof. Adjunto
> >>mails:
> >> joseclaudio.faria@terra.com.br
> >> jc_faria@uesc.br
> >> jc_faria@uol.com.br
> >>tel: 73-3634.2779
> >>
> >>______________________________________________
> >>R-help@stat.math.ethz.ch mailing list
> >>https://stat.ethz.ch/mailman/listinfo/r-help
> >>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
> >>
> >
> >
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> >
> >
>
>
> --
> Jose Claudio Faria
> Brasil/Bahia/UESC/DCET
> Estatistica Experimental/Prof. Adjunto
> mails:
> joseclaudio.faria@terra.com.br
> jc_faria@uesc.br
> jc_faria@uol.com.br
> tel: 73-3634.2779
>



R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Mon Jul 11 10:28:34 2005

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