Documentation Example 2 for PROC DISCRIM
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: DISCEX2 */
/* TITLE: Documentation Example 2 for PROC DISCRIM */
/* PRODUCT: SAS/STAT */
/* SYSTEM: ALL */
/* KEYS: discriminant analysis */
/* PROCS: DISCRIM */
/* DATA: FISHER (1936) IRIS DATA - SASHELP.IRIS */
/* */
/* SUPPORT: saswfk UPDATE: July 25, 2010 */
/* REF: PROC DISCRIM, EXAMPLE 2 */
/* MISC: */
/* */
/****************************************************************/
title 'Discriminant Analysis of Fisher (1936) Iris Data';
proc template;
define statgraph scatter;
begingraph;
entrytitle 'Fisher (1936) Iris Data';
layout overlayequated / equatetype=fit;
scatterplot x=petallength y=petalwidth /
group=species name='iris';
layout gridded / autoalign=(topleft);
discretelegend 'iris' / border=false opaque=false;
endlayout;
endlayout;
endgraph;
end;
run;
proc sgrender data=sashelp.iris template=scatter;
run;
data plotdata;
do PetalLength = -2 to 72 by 0.5;
do PetalWidth= - 5 to 32 by 0.5;
output;
end;
end;
run;
%let close = thresholdmin=0 thresholdmax=0 offsetmin=0 offsetmax=0;
%let close = xaxisopts=(&close) yaxisopts=(&close);
proc template;
define statgraph contour;
begingraph;
layout overlayequated / equatetype=equate &close;
contourplotparm x=petallength y=petalwidth z=z /
contourtype=fill nhint=30;
scatterplot x=pl y=pw / group=species name='iris'
includemissinggroup=false primary=true;
layout gridded / autoalign=(topleft);
discretelegend 'iris' / border=false opaque=false;
endlayout;
endlayout;
endgraph;
end;
run;
%macro contden;
data contour(keep=PetalWidth PetalLength species z pl pw);
merge plotd(in=d) sashelp.iris(keep=PetalWidth PetalLength species
rename=(PetalWidth=pw PetalLength=pl));
if d then z = max(setosa,versicolor,virginica);
run;
title3 'Plot of Estimated Densities';
proc sgrender data=contour template=contour;
run;
%mend;
%macro contprob;
data posterior(keep=PetalWidth PetalLength species z pl pw into);
merge plotp(in=d) sashelp.iris(keep=PetalWidth PetalLength species
rename=(PetalWidth=pw PetalLength=pl));
if d then z = max(setosa,versicolor,virginica);
into = 1 * (_into_ =: 'Set') + 2 * (_into_ =: 'Ver') +
3 * (_into_ =: 'Vir');
run;
title3 'Plot of Posterior Probabilities ';
proc sgrender data=posterior template=contour;
run;
%mend;
%macro contclass;
title3 'Plot of Classification Results';
proc sgrender data=posterior(drop=z rename=(into=z)) template=contour;
run;
%mend;
title2 'Using Normal Density Estimates with Equal Variance';
proc discrim data=sashelp.iris method=normal pool=yes
testdata=plotdata testout=plotp testoutd=plotd
short noclassify crosslisterr;
class Species;
var Petal:;
run;
%contden
%contprob
%contclass
title2 'Using Normal Density Estimates with Unequal Variance';
proc discrim data=sashelp.iris method=normal pool=no
testdata=plotdata testout=plotp testoutd=plotd
short noclassify crosslisterr;
class Species;
var Petal:;
run;
%contden
%contprob
%contclass
title2 'Using Kernel Density Estimates with Equal Bandwidth';
proc discrim data=sashelp.iris method=npar kernel=normal
r=.5 pool=yes testoutd=plotd
testdata=plotdata testout=plotp
short noclassify crosslisterr;
class Species;
var Petal:;
run;
%contden
%contprob
%contclass
title2 'Using Kernel Density Estimates with Unequal Bandwidth';
proc discrim data=sashelp.iris method=npar kernel=normal
r=.5 pool=no testoutd=plotd
testdata=plotdata testout=plotp
short noclassify crosslisterr;
class Species;
var Petal:;
run;
%contden
%contprob
%contclass