Hi Jeoffrey,

How stable are the results in general ?
If you repeat the analysis in R several times, does it yield the same
results ?


Tal

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On Mon, Apr 26, 2010 at 3:37 PM, Jeoffrey Gaspard <
jeoffrey.gaspard@gmail.com> wrote:

> Hello everyone!
>
> My data is composed of 277 individuals measured on 8 binary variables
> (1=yes, 2=no).
>
> I did two similar cluster analyses, one on SPSS 18.0 and one on R 2.9.2.
> The
> objective is to have the means for each variable per retained cluster.
>
> 1) the R analysis ran as followed:
>
> > call data
> > dist=dist(data,method="euclidean")
> > cluster=hclust(dist,method="ward")
> > cluster
>
> Call:
> hclust(d = dist, method = "ward")
>
> Cluster method   : ward
> Distance         : euclidean
> Number of objects: 277
>
> > plot(cluster)
> > rect.hclust(cluster, k=4, border="red")
> > x=rect.hclust(cluster, k=4, border="red")
> > sapply(x, function(i) colMeans(data[i,]))
> > round(sapply(x, function(i) colMeans(data[i,])),2)
>
> 2) The SPSS analysis ran as follows:
>
> Analysis --> Classify --> Hierarchical cluster analysis --> Cluster method=
> Ward's method and Distance measure= Interval:  Squared Euclidean distance.
> After that, I computed the means of each variable for each cluster.
>
> The problem is I have different results between the two analyses (different
> clusters and means).
>
> However, when I use the "Euclidean distance" (unsquared) in SPSS, I have
> the
> same results!
>
> I thought the R "euclidean" command meant the "usual square distance
> between
> the two vectors (2 norm)" as specified in the documentation, no the
> unsquared distance. Did it not?
>
> Thanks for the comment!
>
> Jeffrey
>
>
>
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>
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