08-14-2022
Mine
Fluorite | Level 6
Member since
08-10-2022
- 4 Posts
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Latest posts by Mine
Subject Views Posted 1914 08-12-2022 05:39 PM 1950 08-12-2022 08:05 AM 1979 08-12-2022 04:04 AM 2043 08-11-2022 07:36 AM -
Activity Feed for Mine
- Liked Re: Designing a discrete choice experiment for WarrenKuhfeld. 08-12-2022 05:41 PM
- Posted Re: Designing a discrete choice experiment on Statistical Procedures. 08-12-2022 05:39 PM
- Posted Re: Designing a discrete choice experiment on Statistical Procedures. 08-12-2022 08:05 AM
- Liked Re: Designing a discrete choice experiment for WarrenKuhfeld. 08-12-2022 07:57 AM
- Liked Re: Designing a discrete choice experiment for WarrenKuhfeld. 08-12-2022 06:18 AM
- Liked Re: Designing a discrete choice experiment for WarrenKuhfeld. 08-12-2022 06:18 AM
- Posted Re: Designing a discrete choice experiment on Statistical Procedures. 08-12-2022 04:04 AM
- Posted Designing a discrete choice experiment on Statistical Procedures. 08-11-2022 07:36 AM
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Posts I Liked
Subject Likes Author Latest Post 1 1 3 5
08-12-2022
05:39 PM
I run the code and it works. Relative D-Efficiency decreased from 79 to 66 and D-error increased from 0.15 to 0.18. However, the choice sets are now more realistic. Thank you very much for your support. I learned a lot about creating a discrete choice experiment. I will consider your suggestions for my studies. Also, your documentations are very helpful. Thank you very much for your help.
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08-12-2022
08:05 AM
Thank you so much. You are very helpful. I want to ask one more thing. There are also choice sets in the design same as the example below. Good features but lower price or vice versa. Should I also write the restrictions for these choice sets? When I add restrictions the D-efficiency score is getting lower. If I want to add another restriction for the example how can I write restriction macro? Thank you
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08-12-2022
04:04 AM
Thank you for the quick answer, I run my code again. I get the same results in both versions (D-eff=79.37 and D-error=0.15). As I understand it, there is no problem in determining n as the exact full factorial (n=32). I edited the final version of the code. %MktRuns(2 2 2 4)
%MktEx (2 2 2 4, n=32) /*candidate sets*/
proc print; run;
%macro res;
do i = 1 to nalts;
do k = i + 1 to nalts;
if all(x[i,] >= x[k,]) then bad = bad + 1; /* alt i dominates alt k */
if all(x[k,] >= x[i,]) then bad = bad + 1; /* alt k dominates alt i */
end;
end;
%mend;
%choiceff(data=design, /* candidate set of alternatives */
model=class(x1-x4/sta), /* model with stdz orthogonal coding */
nsets=8, /* number of choice sets */
flags=2, /* 2 alternatives, generic candidates */
maxiter=60, /* maximum number of designs to make */
seed=12655, /* random number seed */
options=relative, /* display relative D-efficiency */
beta=zero); /* assumed beta vector, Ho: b=0 */
proc print; var x1-x4; id set; by set; run;
proc format;
value x1f 1=’no’ 2=’yes’;
value x2f 1=’no’ 2=’yes’;
value x3f 1=’no’ 2=’yes’;
value x4f 1=’10’ 2=’25’ 3=’50’ 4=’75’;
run;
proc print label;
label x1 = ’X1’ x2 = ’X2’ x3 = ’X3’ x4 = ’Price’;
format x1 x1f. x2 x2f. x3 x3f. x4 x4f.;
by set; id set; var x:;
run;
proc print data=bestcov label;
title ’Variance-Covariance Matrix’;
id __label;
label __label = ’00’x;
var x:;
run;
title;
%mktdups(generic, /* duplicate choice sets or duplicate alternatives within choice sets*/
data=best,
factors=x1-x4,
nalts=2) But I noticed a problem. When I examine the choice sets that were created, I think that some choice sets are not realistic. For example, the choice set shown below; The first alternative has no good features (no no no) but it has a higher price. The second alternative has all the good features (yes yes yes) but has a low price. What can I do to avoid these choice sets? Thank you very much again
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08-11-2022
07:36 AM
Hi everybody, I have a question about designing a discrete choice experiment. I have 2 alternatives (Alt1 and Alt2) and also a no-choice option. My alternatives are unlabeled. I have 4 attributes (X1, X2, X3, X4) with their levels mentioned below: X1: 0=no 1=yes X2: 0=no 1=yes X3: 0=no 1=yes X4: %10 %25 %50 %75 Based on what I read, I created two different versions. Here are the versions and some results; Version 1; %MktRuns(2 2 2 4)
%MktEx (2 2 2 4, n=16)
%choiceff(data=design,
model=class(x1-x4/sta),
nsets=8,
flags=2,
maxiter=60,
seed=123,
options=relative,
beta=zero) Relative D-eff = 64.90 D-error = 0.19 Version 2; %MktRuns(2 2 2 4)
%MktEx (8 2**3 4, n=16, seed=123)
%mktlab(data=randomized, vars=Set x1-x4)
proc sort data=Final; by set; run;
proc print; by set; id set; run;
%choiceff(data=final,
init=final(keep=set),
model=class(x1-x4/sta),
nsets=8,
nalts=2,
options=relative,
beta=zero)
%mkteval(data=Best) Relative D-eff = 79.3 D-error = 0.12 Which one is the better result? or can I create a better version? Thank you so much!
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