topic Re: How to transform this data matrix into dissimilar matrix through Jaccard index in SAS Procedures

How to transform this data matrix into dissimilar matrix through Jaccard index

joneryn — Sat, 30 Jun 2012 08:49:21 GMT

For example, there is a data set like this:

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14

F1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

F2 1 1 0 0 0 0 0 0 0 0 0 0 0 0

F3 0 0 1 0 0 0 0 0 0 0 0 0 1 0

F4 0 0 1 1 0 0 0 0 0 0 0 0 1 0

F5 0 0 0 0 1 1 1 0 0 0 0 0 0 0

F6 0 0 0 0 0 1 1 1 0 0 0 0 0 0

F7 0 0 0 0 0 0 0 0 1 1 1 0 0 0

F8 0 0 0 0 0 0 0 0 0 0 1 1 0 0

F9 0 0 0 0 0 0 0 0 0 0 1 1 0 0

F10 0 0 0 0 0 0 0 0 0 0 0 0 1 1

How to transform this data matrix into dissimilar matrix through Jaccard index?

Then calculation the distance between the two of F1-F10 ? How to calculate the distance matrix?

Based on these, I want to do cluster analysis among F1-F10.

I‘m a beginner. I really want to know how to programme it.

Thank you very much!

Re: How to transform this data matrix into dissimilar matrix through Jaccard index

PGStats — Sat, 30 Jun 2012 21:19:04 GMT

The Jaccard index is a similarity measure. For clustering, you need a dissimilarity measure (a distance) such as DJACCARD or Bray-Curtis. You can check the definitions in the SAS doc at :

http://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#statug_distance_sect016.htm

or in the reference :

Legendre, Pierre & Louis Legendre. 1998. Numerical ecology. 2nd English

edition. Elsevier Science BV, Amsterdam.

xv + 853 pages

Here is how to do it in SAS:

data test;
input id $ M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14;
datalines;
F1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
F2 1 1 0 0 0 0 0 0 0 0 0 0 0 0
F3 0 0 1 0 0 0 0 0 0 0 0 0 1 0
F4 0 0 1 1 0 0 0 0 0 0 0 0 1 0
F5 0 0 0 0 1 1 1 0 0 0 0 0 0 0
F6 0 0 0 0 0 1 1 1 0 0 0 0 0 0
F7 0 0 0 0 0 0 0 0 1 1 1 0 0 0
F8 0 0 0 0 0 0 0 0 0 0 1 1 0 0
F9 0 0 0 0 0 0 0 0 0 0 1 1 0 0
F10 0 0 0 0 0 0 0 0 0 0 0 0 1 1
;

proc distance data=test method= /*BRAYCURTIS*/ DJACCARD out=testDist;
var anominal(M: / absent=0); /* M: means all variable names starting with M */
id id;
run;

proc cluster method=AVERAGE data=testDist outtree=testTree print=0;
ID id;
run;

The CLUSTER procedure will give you a dendrogram by default and you can use the testTree dataset as input to PROC TREE for further manipulation.

Re: How to transform this data matrix into dissimilar matrix through Jaccard index

joneryn — Sun, 01 Jul 2012 01:58:23 GMT

Thanks for your help! I think you give me power to learn it. Best wishes!