PROC CLUSTER: Evaluating the Effects of Ties :: SAS/STAT(R) 9.22 User's Guide

The CLUSTER Procedure

Example 29.4 Evaluating the Effects of Ties

If, at some level of the cluster history, there is a tie for minimum distance between clusters, then one or more levels of the sample cluster tree are not uniquely determined. This example shows how the degree of indeterminacy can be assessed.

Mammals have four kinds of teeth: incisors, canines, premolars, and molars. The following data set gives the number of teeth of each kind on one side of the top and bottom jaws for 32 mammals.

Since all eight variables are measured in the same units, it is not strictly necessary to rescale the data. However, the canines have much less variance than the other kinds of teeth and, therefore, have little effect on the analysis if the variables are not standardized. An average linkage cluster analysis is run with and without standardization to enable comparison of the results.

title 'Hierarchical Cluster Analysis of Mammals'' Teeth Data';
title2 'Evaluating the Effects of Ties';
data teeth;
   input Mammal &$16. v1-v8 @@;
   label v1='Top incisors'
         v2='Bottom incisors'
         v3='Top canines'
         v4='Bottom canines'
         v5='Top premolars'
         v6='Bottom premolars'
         v7='Top molars'
         v8='Bottom molars';
   datalines;
Brown Bat         2 3 1 1 3 3 3 3   Mole              3 2 1 0 3 3 3 3
Silver Hair Bat   2 3 1 1 2 3 3 3   Pigmy Bat         2 3 1 1 2 2 3 3
House Bat         2 3 1 1 1 2 3 3   Red Bat           1 3 1 1 2 2 3 3
Pika              2 1 0 0 2 2 3 3   Rabbit            2 1 0 0 3 2 3 3
Beaver            1 1 0 0 2 1 3 3   Groundhog         1 1 0 0 2 1 3 3
Gray Squirrel     1 1 0 0 1 1 3 3   House Mouse       1 1 0 0 0 0 3 3
Porcupine         1 1 0 0 1 1 3 3   Wolf              3 3 1 1 4 4 2 3
Bear              3 3 1 1 4 4 2 3   Raccoon           3 3 1 1 4 4 3 2
Marten            3 3 1 1 4 4 1 2   Weasel            3 3 1 1 3 3 1 2
Wolverine         3 3 1 1 4 4 1 2   Badger            3 3 1 1 3 3 1 2
River Otter       3 3 1 1 4 3 1 2   Sea Otter         3 2 1 1 3 3 1 2
Jaguar            3 3 1 1 3 2 1 1   Cougar            3 3 1 1 3 2 1 1
Fur Seal          3 2 1 1 4 4 1 1   Sea Lion          3 2 1 1 4 4 1 1
Grey Seal         3 2 1 1 3 3 2 2   Elephant Seal     2 1 1 1 4 4 1 1
Reindeer          0 4 1 0 3 3 3 3   Elk               0 4 1 0 3 3 3 3
Deer              0 4 0 0 3 3 3 3   Moose             0 4 0 0 3 3 3 3
;

The following statements produce Output 29.4.1:

title3 'Raw Data';
proc cluster data=teeth method=average nonorm noeigen;
   var v1-v8;
   id mammal;
run;

Output 29.4.1 Average Linkage Analysis of Mammals’ Teeth Data: Raw Data

Hierarchical Cluster Analysis of Mammals' Teeth Data

Evaluating the Effects of Ties

Raw Data

The CLUSTER Procedure

Average Linkage Cluster Analysis

Root-Mean-Square Total-Sample Standard Deviation	0.898027

Cluster History
NCL	Clusters Joined		FREQ	RMS Dist	T i e
31	Beaver	Groundhog	2	0	T
30	Gray Squirrel	Porcupine	2	0	T
29	Wolf	Bear	2	0	T
28	Marten	Wolverine	2	0	T
27	Weasel	Badger	2	0	T
26	Jaguar	Cougar	2	0	T
25	Fur Seal	Sea Lion	2	0	T
24	Reindeer	Elk	2	0	T
23	Deer	Moose	2	0
22	Brown Bat	Silver Hair Bat	2	1	T
21	Pigmy Bat	House Bat	2	1	T
20	Pika	Rabbit	2	1	T
19	CL31	CL30	4	1	T
18	CL28	River Otter	3	1	T
17	CL27	Sea Otter	3	1	T
16	CL24	CL23	4	1
15	CL21	Red Bat	3	1.2247
14	CL17	Grey Seal	4	1.291
13	CL29	Raccoon	3	1.4142	T
12	CL25	Elephant Seal	3	1.4142
11	CL18	CL14	7	1.5546
10	CL22	CL15	5	1.5811
9	CL20	CL19	6	1.8708	T
8	CL11	CL26	9	1.9272
7	CL8	CL12	12	2.2278
6	Mole	CL13	4	2.2361
5	CL9	House Mouse	7	2.4833
4	CL6	CL7	16	2.5658
3	CL10	CL16	9	2.8107
2	CL3	CL5	16	3.7054
1	CL2	CL4	32	4.2939

The following statements produce Output 29.4.2:

title3 'Standardized Data';
proc cluster data=teeth std method=average nonorm noeigen;
   var v1-v8;
   id mammal;
run;

Output 29.4.2 Average Linkage Analysis of Mammals’ Teeth Data: Standardized Data

Hierarchical Cluster Analysis of Mammals' Teeth Data

Evaluating the Effects of Ties

Standardized Data

The CLUSTER Procedure

Average Linkage Cluster Analysis

The data have been standardized to mean 0 and variance 1

Root-Mean-Square Total-Sample Standard Deviation	1

Cluster History
NCL	Clusters Joined		FREQ	RMS Dist	T i e
31	Beaver	Groundhog	2	0	T
30	Gray Squirrel	Porcupine	2	0	T
29	Wolf	Bear	2	0	T
28	Marten	Wolverine	2	0	T
27	Weasel	Badger	2	0	T
26	Jaguar	Cougar	2	0	T
25	Fur Seal	Sea Lion	2	0	T
24	Reindeer	Elk	2	0	T
23	Deer	Moose	2	0
22	Pigmy Bat	Red Bat	2	0.9157
21	CL28	River Otter	3	0.9169
20	CL31	CL30	4	0.9428	T
19	Brown Bat	Silver Hair Bat	2	0.9428	T
18	Pika	Rabbit	2	0.9428
17	CL27	Sea Otter	3	0.9847
16	CL22	House Bat	3	1.1437
15	CL21	CL17	6	1.3314
14	CL25	Elephant Seal	3	1.3447
13	CL19	CL16	5	1.4688
12	CL15	Grey Seal	7	1.6314
11	CL29	Raccoon	3	1.692
10	CL18	CL20	6	1.7357
9	CL12	CL26	9	2.0285
8	CL24	CL23	4	2.1891
7	CL9	CL14	12	2.2674
6	CL10	House Mouse	7	2.317
5	CL11	CL7	15	2.6484
4	CL13	Mole	6	2.8624
3	CL4	CL8	10	3.5194
2	CL3	CL6	17	4.1265
1	CL2	CL5	32	4.7753

There are ties at 16 levels for the raw data but at only 10 levels for the standardized data. There are more ties for the raw data because the increments between successive values are the same for all of the raw variables but different for the standardized variables.

One way to assess the importance of the ties in the analysis is to repeat the analysis on several random permutations of the observations and then to see to what extent the results are consistent at the interesting levels of the cluster history. Three macros are presented to facilitate this process, as follows.

/* --------------------------------------------------------- */
/*                                                           */
/* The macro CLUSPERM randomly permutes observations and     */
/* does a cluster analysis for each permutation.             */
/* The arguments are as follows:                             */
/*                                                           */
/*    data    data set name                                  */
/*    var     list of variables to cluster                   */
/*    id      id variable for proc cluster                   */
/*    method  clustering method (and possibly other options) */
/*    nperm   number of random permutations.                 */
/*                                                           */
/* --------------------------------------------------------- */
%macro CLUSPERM(data,var,id,method,nperm);

   /* ------CREATE TEMPORARY DATA SET WITH RANDOM NUMBERS------ */
   data _temp_;
      set &data;
      array _random_ _ran_1-_ran_&nperm;
      do over _random_;
        _random_=ranuni(835297461);
      end;
   run;

   /* ------PERMUTE AND CLUSTER THE DATA----------------------- */
   %do n=1 %to &nperm;
       proc sort data=_temp_(keep=_ran_&n &var &id) out=_perm_;
          by _ran_&n;
       run;

       proc cluster method=&method noprint outtree=_tree_&n;
          var &var;
          id &id;
       run;
   %end;
%mend;

/* --------------------------------------------------------- */
/*                                                           */
/* The macro PLOTPERM plots various cluster statistics       */
/* against the number of clusters for each permutation.      */
/* The arguments are as follows:                             */
/*                                                           */
/*    nclus   maximum number of clusters to be plotted       */
/*    nperm   number of random permutations.                 */
/*                                                           */
/* --------------------------------------------------------- */
%macro PLOTPERM(nclus,nperm);

   /* ---CONCATENATE TREE DATA SETS FOR 20 OR FEWER CLUSTERS--- */
   data _plot_;
      set %do n=1 %to &nperm; _tree_&n(in=_in_&n) %end;;
      if _ncl_<=&nclus;
      %do n=1 %to &nperm;
         if _in_&n then _perm_=&n;
      %end;
      label _perm_='permutation number';
      keep _ncl_ _psf_ _pst2_ _ccc_ _perm_;
   run;

   /* ---PLOT THE REQUESTED STATISTICS BY NUMBER OF CLUSTERS--- */
   proc sgscatter;
      compare y=(_ccc_ _psf_ _pst2_) x=_ncl_ /group=_perm_;
   run;
%mend;

/* --------------------------------------------------------- */
/*                                                           */
/* The macro TABPERM generates cluster-membership variables  */
/* for a specified number of clusters for each permutation.  */
/* PROC TABULATE gives the frequencies and means.            */
/* The arguments are as follows:                             */
/*                                                           */
/*    var     list of variables to cluster                   */
/*            (no "-" or ":" allowed)                        */
/*    id      id variable for proc cluster                   */
/*    meanfmt format for printing means in PROC TABULATE     */
/*    nclus   number of clusters desired                     */
/*    nperm   number of random permutations.                 */
/*                                                           */
/* --------------------------------------------------------- */
%macro TABPERM(var,id,meanfmt,nclus,nperm);

   /* ------CREATE DATA SETS GIVING CLUSTER MEMBERSHIP--------- */
   %do n=1 %to &nperm;
      proc tree data=_tree_&n noprint n=&nclus
                out=_out_&n(drop=clusname
                              rename=(cluster=_clus_&n));
         copy &var;
         id &id;
      run;

      proc sort;
         by &id &var;
      run;
   %end;

   /* ------MERGE THE CLUSTER VARIABLES------------------------ */
   data _merge_;
      merge
         %do n=1 %to &nperm;
            _out_&n
         %end;;
      by &id &var;
      length all_clus $ %eval(3*&nperm);
      %do n=1 %to &nperm;
         substr( all_clus, %eval(1+(&n-1)*3), 3) =
            put( _clus_&n, 3.);
      %end;
   run;

   /* ------ TABULATE CLUSTER COMBINATIONS------------ */
   proc sort;
      by _clus_:;
   run;
   proc tabulate order=data formchar='           ';
      class all_clus;
      var &var;
      table all_clus, n='FREQ'*f=5. mean*f=&meanfmt*(&var) /
         rts=%eval(&nperm*3+1);
   run;
%mend;

To use these macros, it is first convenient to define a macro, VLIST, listing the teeth variables, since the forms V1-V8 or V: cannot be used with the TABULATE procedure in the TABPERM macro:

/* -TABULATE does not accept hyphens or colons in VAR lists- */
%let vlist=v1 v2 v3 v4 v5 v6 v7 v8;

The CLUSPERM macro is then called to analyze 10 random permutations. The PLOTPERM macro plots the pseudo $\text{[math]}$ and $\text{[math]}$ statistics and the cubic clustering criterion. Since the data are discrete, the pseudo $\text{[math]}$ statistic and the cubic clustering criterion can be expected to increase as the number of clusters increases, so local maxima or large jumps in these statistics are more relevant than the global maximum in determining the number of clusters. For the raw data, only the pseudo $\text{[math]}$ statistic indicates the possible presence of clusters, with the 4-cluster level being suggested. Hence, the macros are used as follows to analyze the results at the 4-cluster level:

title3 'Raw Data';

/* ------CLUSTER RAW DATA WITH AVERAGE LINKAGE-------------- */
%clusperm( teeth, &vlist, mammal, average, 10);

The following statements produce Output 29.4.3.

/* -----PLOT STATISTICS FOR THE LAST 20 LEVELS-------------- */
%plotperm(20, 10);

Output 29.4.3 Analysis of 10 Random Permutations of Raw Mammals’ Teeth Data

The following statements produce Output 29.4.4.

/* ------ANALYZE THE 4-CLUSTER LEVEL------------------------ */
%tabperm( &vlist, mammal, 9.1, 4, 10);

Output 29.4.4 Raw Mammals’ Teeth Data: Indeterminacy at the 4-Cluster Level

Hierarchical Cluster Analysis of Mammals' Teeth Data

Evaluating the Effects of Ties

Raw Data

	FREQ	Mean
	FREQ	Top incisors	Bottom incisors	Top canines	Bottom canines	Top premolars	Bottom premolars	Top molars	Bottom molars
all_clus	4	0.0	4.0	0.5	0.0	3.0	3.0	3.0	3.0
1 3 1 1 1 3 3 3 2 3	4	0.0	4.0	0.5	0.0	3.0	3.0	3.0	3.0
2 2 2 2 2 2 1 2 1 1	15	2.9	2.6	1.0	1.0	3.6	3.4	1.3	1.8
2 4 2 2 4 2 1 2 1 1	1	3.0	2.0	1.0	0.0	3.0	3.0	3.0	3.0
3 1 3 3 3 1 2 1 3 2	5	1.0	1.0	0.0	0.0	1.2	0.8	3.0	3.0
3 4 3 3 4 1 2 1 3 2	2	2.0	1.0	0.0	0.0	2.5	2.0	3.0	3.0
4 4 4 4 4 4 4 4 4 4	5	1.8	3.0	1.0	1.0	2.0	2.4	3.0	3.0

From the TABULATE output, you can see that two types of clustering are obtained. In one case, the mole is grouped with the carnivores, while the pika and rabbit are grouped with the rodents. In the other case, both the mole and the lagomorphs are grouped with the bats.

Next, the analysis is repeated with the standardized data as shown in the following statements. The pseudo $\text{[math]}$ and $\text{[math]}$ statistics indicate 3 or 4 clusters, while the cubic clustering criterion shows a sharp rise up to 4 clusters and then levels off up to 6 clusters. So the TABPERM macro is used again at the 4-cluster level. In this case, there is no indeterminacy, because the same four clusters are obtained with every permutation, although in different orders. It must be emphasized, however, that lack of indeterminacy in no way indicates validity.

title3 'Standardized Data';

/*------CLUSTER STANDARDIZED DATA WITH AVERAGE LINKAGE------*/
%clusperm( teeth, &vlist, mammal, average std, 10);

The following statements produce Output 29.4.5.

/* -----PLOT STATISTICS FOR THE LAST 20 LEVELS-------------- */
%plotperm(20, 10);

Output 29.4.5 Analysis of 10 Random Permutations of Standardized Mammals’ Teeth Data

The following statements produce Output 29.4.6.

/* ------ANALYZE THE 4-CLUSTER LEVEL------------------------ */
%tabperm( &vlist, mammal, 9.1, 4, 10);

Output 29.4.6 Standardized Mammals’ Teeth Data: No Indeterminacy at the 4-Cluster Level

Hierarchical Cluster Analysis of Mammals' Teeth Data

Evaluating the Effects of Ties

Standardized Data

	FREQ	Mean
	FREQ	Top incisors	Bottom incisors	Top canines	Bottom canines	Top premolars	Bottom premolars	Top molars	Bottom molars
all_clus	4	0.0	4.0	0.5	0.0	3.0	3.0	3.0	3.0
1 3 1 1 1 3 3 3 2 3	4	0.0	4.0	0.5	0.0	3.0	3.0	3.0	3.0
2 2 2 2 2 2 1 2 1 1	15	2.9	2.6	1.0	1.0	3.6	3.4	1.3	1.8
3 1 3 3 3 1 2 1 3 2	7	1.3	1.0	0.0	0.0	1.6	1.1	3.0	3.0
4 4 4 4 4 4 4 4 4 4	6	2.0	2.8	1.0	0.8	2.2	2.5	3.0	3.0

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