Discrete Choice 9
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: MR2010F */
/* TITLE: Discrete Choice 9 */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: marketing research */
/* PROCS: PHREG, TRANSREG, OPTEX, FACTEX, PLAN, IML */
/* DATA: */
/* */
/* SUPPORT: saswfk UPDATE: 01Oct2010 */
/* REF: MR-2010F Discrete Choice */
/* MISC: This file contains SAS code for the "Discrete */
/* Choice" report, the 01Oct2010 edition, for SAS 9.2. */
/* */
/* You must install the following macros from */
/* http://support.sas.com/techsup/tnote/tnote_stat.html#market */
/* */
/* ChoicEff Efficient Choice Designs */
/* MktAllo Process a Choice Allocation Study Data Set */
/* MktBal Balanced Experimental Design */
/* MktBIBD Balanced Incomplete Block Design */
/* MktBlock Block an Experimental Design */
/* MktBSize Balanced Incomplete Block Design Sizes */
/* MktDes Efficient Experimental Designs */
/* MktDups Eliminate Duplicate Runs or Choice Sets */
/* MktEval Evaluate an Experimental Design */
/* MktEx Efficient Experimental Designs */
/* MktKey Aid Making the MktRoll KEY= Data Set */
/* MktLab Change Names, Levels in a Design */
/* MktMDiff MaxDiff (Best-Worst) Choice Modeling */
/* MktMerge Merge a Choice Design with Choice Data */
/* MktOrth List Orthogonal Designs MktEx Can Make */
/* MktPPro Optimal Partial Profile Designs */
/* MktRoll Roll Out a Design Into a Choice Design */
/* MktRuns Experimental Design Sizes */
/* PhChoice Customize PROC PHREG for Choice Models */
/* */
/****************************************************************/
************* Begin PHREG Output Customization **************;
options ls=80 ps=60 nonumber nodate;
%phchoice(on)
****************** Begin Chair Example Code *****************;
title 'Generic Chair Attributes';
* This design will not be used;
%mktex(3 ** 15, n=36, seed=238)
%mktkey(3 5)
%mktroll(design=randomized, key=key, out=cand)
%mktruns(3 ** 5)
%mktex(3 ** 5, n=243)
proc print data=design(obs=27); run;
%choiceff(data=design, /* candidate set of alternatives */
model=class(x1-x5 / sta), /* model with stdzd orthogonal coding */
nsets=6, /* number of choice sets */
maxiter=100, /* maximum number of designs to make */
seed=121, /* random number seed */
flags=3, /* 3 alternatives, generic candidates */
options=relative, /* display relative D-efficiency */
beta=zero) /* assumed beta vector, Ho: b=0 */
proc print; by set; id set; run;
%mktdups(generic, data=best, nalts=3, factors=x1-x5)
%mktex(3 ** 5, n=18)
%choiceff(data=design, /* candidate set of alternatives */
model=class(x1-x5 / sta), /* model with stdzd orthogonal coding */
nsets=6, /* number of choice sets */
maxiter=20, /* maximum number of designs to make */
seed=121, /* random number seed */
flags=3, /* 3 alternatives, generic candidates */
options=relative, /* display relative D-efficiency */
beta=zero) /* assumed beta vector, Ho: b=0 */
proc print; by set; id set; run;
%mktdups(generic, data=best, nalts=3, factors=x1-x5)
%mktex(3 ** 5, n=27, seed=382)
%choiceff(data=design, /* candidate set of alternatives */
model=class(x1-x5 / sta), /* model with stdzd orthogonal coding */
nsets=9, /* number of choice sets */
maxiter=20, /* maximum number of designs to make */
seed=121, /* random number seed */
flags=3, /* 3 alternatives, generic candidates */
options=relative, /* display relative D-efficiency */
beta=zero) /* assumed beta vector, Ho: b=0 */
proc print; id set; by set; var index prob x:; run;
%mktdups(generic, data=best, nalts=3, factors=x1-x5)
title 'Generic Chair Attributes';
%mktex(3 ** 5, n=243, seed=306)
data final(drop=i);
set design end=eof;
retain f1-f3 1 f4 0;
output;
if eof then do;
array x[9] x1-x5 f1-f4;
do i = 1 to 9; x[i] = i le 5 or i eq 9; end;
output;
end;
run;
proc print data=final(where=(x1 eq x3 and x2 eq x4 and x3 eq x5 or f4)); run;
%choiceff(data=final, /* candidate set of alternatives */
model=class(x1-x5 / sta), /* model with stdzd orthogonal coding */
nsets=6, /* number of choice sets */
maxiter=100, /* maximum number of designs to make */
seed=121, /* random number seed */
flags=f1-f4, /* flag which alt can go where, 4 alts */
options=relative, /* display relative D-efficiency */
beta=zero) /* assumed beta vector, Ho: b=0 */
proc print; by set; id set; run;
%mktdups(generic, data=best, nalts=4, factors=x1-x5)
%mktex(3 ** 15, n=81 * 81, seed=522)
%mktkey(3 5)
data key;
input (x1-x5) ($);
datalines;
x1 x2 x3 x4 x5
x6 x7 x8 x9 x10
x11 x12 x13 x14 x15
. . . . .
;
%mktroll(design=randomized, key=key, out=rolled)
* Code the constant alternative;
data final;
set rolled;
if _alt_ = '4' then do; x1 = 1; x2 = 1; x3 = 1; x4 = 1; x5 = 1; end;
run;
proc print; by set; id set; where set in (1, 100, 1000, 5000, 6561); run;
%choiceff(data=final, /* candidate set of choice sets */
model=class(x1-x5 / sta), /* model with stdzd orthogonal coding */
nsets=6, /* number of choice sets */
nalts=4, /* number of alternatives */
maxiter=10, /* maximum number of designs to make */
seed=109, /* random number seed */
options=relative, /* display relative D-efficiency */
beta=zero) /* assumed beta vector, Ho: b=0 */
%mktdups(generic, data=best, nalts=4, factors=x1-x5)
%phchoice(off)