Distance Macro Example Usage

SAS Program

dm "output;clear;log;clear";
****************************************************************;
* DistanceExample.sas                                          *;
* Example showing use of distance macro and then subsequent    *;
* use of the distance output by procedures CLUSTER and MDS.    *;
*                                                              *;
* The program assumes that all macros listed below are found   *;
* in the current working directory. You could also move them   *;
* to an macro autocall directory and comment the include       *;
* lines so that they do not execute.                           *;
*                                                              *;
* The data set is taken from the DISTANCE macro documentation. *;
****************************************************************;
Options PS=55 LS=80 PageNo=1 NoDate
        FORMCHAR='|----|+|---+=|-/\<>*';

Title1 "Distance Macro Examples: Mammals Teeth";

/* 
 * Include SAS macros: 
 * (These are assumed to reside in the current working directory.
 *  Include an appropriate path as necessary.)
 */
%include "xmacro.sas"  / NoSource;   /* Needed by STDIZE and DISTANCE */
%include "stdize.sas"  / NoSource;   /* Needed by DISTANCE            */
%include "distnew.sas" / NoSource;   /* DISTANCE macro                */
%include "dendro.sas"  / NoSource;   /* Plots a dendrogram            */
%include "plotit7.sas" / NoSource;   /* Plots scaling results         */

/* Example data set from DISTANCE documentation */
Data Teeth;
 Input Mammal $ 1-16 @21 (V1-V8) (1.);
 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;
Armadillo           00000088
Mouse               11000033
Beaver              11002133
Groundhog           11002133
Rabbit              21003233
Moose               04003333
Mole                32103333
Wolf                33114423
Raccoon             33114432
Jaguar              33113211
;

/* Call the DISTANCE macro to compute Euclidean distances */
Title2 "METHOD=EUCLIDEAN";
%distance(data=teeth, 
          id=mammal,
          options=print nomiss,
          shape=square,
          method=euclid,
          std=std, 
          var=v1-v8,
          out=Euclid 
          );

/* 
 * What's in the output data set?           
 * Notice that DATA SET TYPE is DISTANCE.   
 * A DISTANCE data set can be used directly 
 * by the CLUSTER and MDS procedures.       
 */
Proc Contents Data=Euclid;
Run;

Proc Print Data=Euclid;
Run;

/*
 * Perform a single-linkage cluster analysis using
 * the distances computed by the DISTANCE macro.
 * Save the results into a TREE data set that can
 * be graphed using the DENDRO macro.
 */
Title3 "Single-linkage Cluster Analysis";
Proc Cluster Data=Euclid Method=Single OutTree=Tree;
 Var Armadillo--Jaguar;
 Id Mammal;
Run;

/*
 * Create a graphical tree of the cluster results.
 */
%dendro;

/*
 * Illustrate performing multidimensional scaling of
 * the objects with distances computed by the DISTANCE
 * macro. Save the results in MDSout and plot them.
 */ 
Title3 "Metric Multidimensional Scaling";
Proc MDS Data=Euclid Level=Absolute Out=MDSout
         Dimension=2 PData PConfig PFinal;
 Var Armadillo--Jaguar;
 Id Mammal;
Run;

/*
 * Plot the 2-dimensional solution from MDS.
 */
%Plotit(Data=MDSout,Datatype=mds,Monochro=Black);

Output

 

Distance Macro Examples: Mammals Teeth

METHOD=EUCLIDEAN

Mammal	Armadillo	Mouse	Beaver	Groundhog	Rabbit	Moose	Mole	Wolf	Raccoon	Jaguar
Armadillo	0.00000	4.05525	4.34184	4.34184	4.95602	5.77229	5.96370	7.26567	7.26567	7.43075
Mouse	4.05525	0.00000	1.55130	1.55130	2.61543	3.81139	3.91911	5.32308	5.32308	4.65184
Beaver	4.34184	1.55130	0.00000	0.00000	1.25596	2.89200	3.03255	4.39495	4.39495	4.04680
Groundhog	4.34184	1.55130	0.00000	0.00000	1.25596	2.89200	3.03255	4.39495	4.39495	4.04680
Rabbit	4.95602	2.61543	1.25596	1.25596	0.00000	2.90507	2.33288	3.69910	3.69910	3.67730
Moose	5.77229	3.81139	2.89200	2.89200	2.90507	0.00000	3.45120	3.95318	3.95318	4.15534
Mole	5.96370	3.91911	3.03255	3.03255	2.33288	3.45120	0.00000	2.47647	2.47647	2.78786
Wolf	7.26567	5.32308	4.39495	4.39495	3.69910	3.95318	2.47647	0.00000	0.77981	1.95177
Raccoon	7.26567	5.32308	4.39495	4.39495	3.69910	3.95318	2.47647	0.77981	0.00000	1.95177
Jaguar	7.43075	4.65184	4.04680	4.04680	3.67730	4.15534	2.78786	1.95177	1.95177	0.00000

 

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Distance Macro Examples: Mammals Teeth

METHOD=EUCLIDEAN

The CONTENTS Procedure

Data Set Name:	WORK.EUCLID	Observations:	10
Member Type:	DATA	Variables:	11
Engine:	V8	Indexes:	0
Created:	16:18 Tuesday, November 27, 2001	Observation Length:	96
Last Modified:	16:18 Tuesday, November 27, 2001	Deleted Observations:	0
Protection:		Compressed:	NO
Data Set Type:	DISTANCE	Sorted:	NO
Label:

 

-----Engine/Host Dependent Information-----
Data Set Page Size:	8192
Number of Data Set Pages:	1
First Data Page:	1
Max Obs per Page:	84
Obs in First Data Page:	10
Number of Data Set Repairs:	0
File Name:	C:\WINDOWS\TEMP\SAS Temporary Files\_TD28169\euclid.sas7bdat
Release Created:	8.0202M0
Host Created:	WIN_98

 

-----Alphabetic List of Variables and Attributes-----
#	Variable	Type	Len	Pos
2	Armadillo	Num	8	0
4	Beaver	Num	8	16
5	Groundhog	Num	8	24
11	Jaguar	Num	8	72
1	Mammal	Char	16	80
8	Mole	Num	8	48
7	Moose	Num	8	40
3	Mouse	Num	8	8
6	Rabbit	Num	8	32
10	Raccoon	Num	8	64
9	Wolf	Num	8	56

 

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Distance Macro Examples: Mammals Teeth

METHOD=EUCLIDEAN

Obs	Mammal	Armadillo	Mouse	Beaver	Groundhog	Rabbit	Moose	Mole	Wolf	Raccoon	Jaguar
1	Armadillo	0.00000	4.05525	4.34184	4.34184	4.95602	5.77229	5.96370	7.26567	7.26567	7.43075
2	Mouse	4.05525	0.00000	1.55130	1.55130	2.61543	3.81139	3.91911	5.32308	5.32308	4.65184
3	Beaver	4.34184	1.55130	0.00000	0.00000	1.25596	2.89200	3.03255	4.39495	4.39495	4.04680
4	Groundhog	4.34184	1.55130	0.00000	0.00000	1.25596	2.89200	3.03255	4.39495	4.39495	4.04680
5	Rabbit	4.95602	2.61543	1.25596	1.25596	0.00000	2.90507	2.33288	3.69910	3.69910	3.67730
6	Moose	5.77229	3.81139	2.89200	2.89200	2.90507	0.00000	3.45120	3.95318	3.95318	4.15534
7	Mole	5.96370	3.91911	3.03255	3.03255	2.33288	3.45120	0.00000	2.47647	2.47647	2.78786
8	Wolf	7.26567	5.32308	4.39495	4.39495	3.69910	3.95318	2.47647	0.00000	0.77981	1.95177
9	Raccoon	7.26567	5.32308	4.39495	4.39495	3.69910	3.95318	2.47647	0.77981	0.00000	1.95177
10	Jaguar	7.43075	4.65184	4.04680	4.04680	3.67730	4.15534	2.78786	1.95177	1.95177	0.00000

 

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Distance Macro Examples: Mammals Teeth

METHOD=EUCLIDEAN

Single-linkage Cluster Analysis

The CLUSTER Procedure

Single Linkage Cluster Analysis

Mean Distance Between Observations = 3.653877

Cluster History
NCL	Clusters Joined		FREQ	Norm Min Dist	T i e
9	Beaver	Groundhog	2	0
8	Wolf	Raccoon	2	0.2134
7	CL9	Rabbit	3	0.3437
6	Mouse	CL7	4	0.4246
5	CL8	Jaguar	3	0.5342
4	CL6	Mole	5	0.6385
3	CL4	CL5	8	0.6778
2	CL3	Moose	9	0.7915
1	Armadillo	CL2	10	1.1098

 

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Distance Macro Examples: Mammals Teeth

METHOD=EUCLIDEAN

Metric Multidimensional Scaling

Multidimensional Scaling: Data=WORK.EUCLID.DATA

Data Matrix
1	Armadillo	Mouse	Beaver	Groundhog	Rabbit	Moose	Mole	Wolf	Raccoon	Jaguar
Armadillo	0	4.055247	4.341837	4.341837	4.956016	5.772288	5.963696	7.265674	7.265674	7.430754
Mouse	4.055247	0	1.551297	1.551297	2.61543	3.811393	3.919106	5.323078	5.323078	4.65184
Beaver	4.341837	1.551297	0	0	1.255957	2.892001	3.032548	4.39495	4.39495	4.046798
Groundhog	4.341837	1.551297	0	0	1.255957	2.892001	3.032548	4.39495	4.39495	4.046798
Rabbit	4.956016	2.61543	1.255957	1.255957	0	2.90507	2.332879	3.699095	3.699095	3.677302
Moose	5.772288	3.811393	2.892001	2.892001	2.90507	0	3.451201	3.953177	3.953177	4.15534
Mole	5.963696	3.919106	3.032548	3.032548	2.332879	3.451201	0	2.476474	2.476474	2.787862
Wolf	7.265674	5.323078	4.39495	4.39495	3.699095	3.953177	2.476474	0	0.779813	1.951769
Raccoon	7.265674	5.323078	4.39495	4.39495	3.699095	3.953177	2.476474	0.779813	0	1.951769
Jaguar	7.430754	4.65184	4.046798	4.046798	3.677302	4.15534	2.787862	1.951769	1.951769	0

 

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Distance Macro Examples: Mammals Teeth

METHOD=EUCLIDEAN

Metric Multidimensional Scaling

Multidimensional Scaling: Data=WORK.EUCLID.DATA

Shape=TRIANGLE Condition=MATRIX Level=ABSOLUTE

Coef=IDENTITY Dimension=2 Formula=1 Fit=1

Gconverge=0.01 Maxiter=100 Over=1 Ridge=0.0001

Iteration	Type	Badness- of-Fit Criterion	Change in Criterion	Convergence Measure
0	Initial	0.1796	.	0.7946
1	Lev-Mar	0.1007	0.0789	0.3066
2	Gau-New	0.0951	0.005573	0.0944
3	Gau-New	0.0945	0.000654	0.0525
4	Gau-New	0.0943	0.000207	0.0310
5	Gau-New	0.0942	0.0000723	0.0184
6	Gau-New	0.0942	0.0000254	0.0108
7	Gau-New	0.0942	8.7399E-6	0.006309

 

Convergence criterion is satisfied.

 

Configuration
	Dim1	Dim2
Armadillo	4.61	-1.79
Mouse	2.33	1.17
Beaver	1.23	0.57
Groundhog	1.23	0.57
Rabbit	0.48	-0.14
Moose	-0.46	2.50
Mole	-0.86	-1.42
Wolf	-2.78	-1.22
Raccoon	-2.92	-0.81
Jaguar	-2.86	0.58

 

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