Documentation Example 4 for PROC CLUSTER
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: CLUSEX4 */
/* TITLE: Documentation Example 4 for PROC CLUSTER */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: HIERARCHICAL CLUSTER ANALYSIS TIES */
/* PROCS: FREQ, SGSCATTER, CLUSTER, TREE, TABULATE */
/* DATA: */
/* */
/* SUPPORT: sasrbk */
/* REF: PROC CLUSTER, Example 4. */
/* MISC: */
/****************************************************************/
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
;
title3 'Raw Data';
proc cluster data=teeth method=average nonorm noeigen;
var v1-v8;
id mammal;
run;
title3 'Standardized Data';
proc cluster data=teeth std method=average nonorm noeigen;
var v1-v8;
id mammal;
run;
/* --------------------------------------------------------- */
/* */
/* 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_(drop=i);
set &data;
array _random_ _ran_1-_ran_&nperm;
do i = 1 to dim(_random_);
_random_[i]=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_;
label _ccc_ = 'CCC' _psf_ = 'Pseudo F' _pst2_ = 'Pseudo T-Squared';
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;
/* -TABULATE does not accept hyphens or colons in VAR lists- */
%let vlist=v1 v2 v3 v4 v5 v6 v7 v8;
title3 'Raw Data';
/* ------CLUSTER RAW DATA WITH AVERAGE LINKAGE-------------- */
%clusperm( teeth, &vlist, mammal, average, 10);
/* -----PLOT STATISTICS FOR THE LAST 20 LEVELS-------------- */
%plotperm(20, 10);
/* ------ANALYZE THE 4-CLUSTER LEVEL------------------------ */
%tabperm( &vlist, mammal, 9.1, 4, 10);
title3 'Standardized Data';
/*------CLUSTER STANDARDIZED DATA WITH AVERAGE LINKAGE------*/
%clusperm( teeth, &vlist, mammal, average std, 10);
/* -----PLOT STATISTICS FOR THE LAST 20 LEVELS-------------- */
%plotperm(20, 10);
/* ------ANALYZE THE 4-CLUSTER LEVEL------------------------ */
%tabperm( &vlist, mammal, 9.1, 4, 10);
