Re: [R] Cluster analysis with missing data

[Bill.Venables]

vegdist() in the vegan package optionally allows pairwise deletion of missing 
values when computing dissimilarities.  The result can be used as the first 
agrument to hclust()

('Caveat emptor', of course.)

From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of 
Hollix [holger.steinm...@web.de]
Sent: 14 July 2009 16:42
To: r-help@r-project.org
Subject: [R]  Cluster analysis with missing data

Hi folks,

I tried for the first time hclust. Unfortunately, with missing data in my
data file, it doesn't seem
to work. I found no information about how to consider missing data.

Omission of all missings is not really an option as I would loose to many
cases.

Thanks in advance
Holger
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Re: [R] Cluster analysis with missing data

[Gavin Simpson]

On Mon, 2009-07-13 at 23:42 -0700, Hollix wrote:
 Hi folks,
 
 I tried for the first time hclust. Unfortunately, with missing data in my
 data file, it doesn't seem
 to work. I found no information about how to consider missing data.
 
 Omission of all missings is not really an option as I would loose to many
 cases.

Holger,

hclust takes a dissimilarity matrix as input, not your data, so the
problem is in finding an appropriate dissimilarity/distance coefficient
that handles missing data.

Once such measure is Gower's coefficient and is implemented in function
'daisy' in recommended package 'cluster'. Try:

require(cluster)
?daisy

to read about it.

Also 'vegdist' in package 'vegan' has an ability to not consider
pairwise missingness. See ?vegdist after loading 'vegan' and in
particular, the 'na.rm' argument.

Whether either of these (i.e. the resulting dissimilarities) make sense
for your particular problem is another matter...

HTH

G
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