Via: uk.ac.hull.cc.sequent; Wed, 9 Sep 1992 10:51:33 +0100
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Original-Sender: R.A.Reese@sequent.cc.hull.ac.uk
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Date:         Wed, 9 Sep 1992 05:51:35 -0400
Reply-To:     Allan Reese <R.A.Reese@SEQUENT.CC.HULL.AC.UK>
From:         Allan Reese <R.A.Reese@SEQUENT.CC.HULL.AC.UK>
Subject:      Matrix input / Cluster procedure.
Comments: To: SPSSX-L list <spssx-l@uga.cc.uga.edu>

The Manual's description is getting a similarity matrix into Cluster
analysis is (by SPSS standards) poor. Here is an example with the commands
that now work and a few notes on the problems.

A user here is able to measure similarities directly. He measures the
amount of DNA that sticks to a sample compared with the amount that sticks
to itself. This gives a reading between 0 and 100% for similarity. The
user had found MATRIX DATA and CLUSTER commands but came along to have
some of the output interpreted. Fortunately, I noticed that CLUSTER had
not picked up on the MATRIX DATA, so had taken the input as raw cases and
computed new "similarities".

*** I strongly recommend that SPSS add a check in each analysis for the
presence of a variable called ROWTYPE_ and issue a warning if the values
of this variable indicate the "wrong type" of data. Maybe ROWTYPE_ should
become $ROWTYPE and be a system variable in all active files with default
value of "CASE".

You may care to check the algebra, but multiplying the similarity matrix
by itself produced a "similar" pattern so the results did not look
unreasonable. There was a line in the output reporting which similarity
had been computed, and this was what I picked on.

So we added "MATRIX=IN(*)" to the CLUSTER and got results that were
clearly barmy, because the default is to read the matrix as
dissimilarities. It was then very difficult to pick up on hints as to how
to tell SPSS that they were similarities. P81 describes the MATRIX SYSTEM
Format, but is obtuse in saying that PROX is assigned a VALUE LABEL and
is <also> assigned a keyword. MATRIX DATA (pp439-453) doesn't have an
example with PROX input. PROXIMITIES (p560) has the same cryptic
information as CLUSTER. So I ran a made up PROXIMITIES and used DISPLAY
on the results.

** Here then, is an example of how to read a similarity matrix and analyse
it with CLUSTER. I've tweaked some figures in case the data's secret:

TITLE "Prof R. Goulder.  DNA hybridization"
MATRIX DATA VARIABLES=GP1 GP2 GP3 R1 R2 R2
  /contents=prox
BEGIN DATA
1.0
0.39 1.0
0.34 0.19 1.0
0.05 0.02 0.09 1.0
0.06 0.09 0.14 0.17 1.0
0.11 0.07 0.15 0.25 0.24 1.0
END DATA
value labels rowtype_ 'prox'  'SIMILARITY         C'
LIST
cluster /method=baverage
   /print distance schedule cluster(1,6)   /plot dendrogram
   /matrix=in(*)
FINISH

** The dendrogram output is rescaled so that the distance corresponding to
the final merge is made equal to 25. Does anyone know why? In this example
there are clear end points (0 and 100% similar) and the researcher would
prefer the dendrogram on that scale.


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