1.正交设计
Design;alts = alt1, alt2 ;rows = 36 #选择集数量;block = 6 #分块;orth = seq #定义seq正交设计，另有sim正交设计;reject: #设定拒绝的组合alt1.transport = 1 AND alt1.pull = 3,alt2.transport = 1 AND alt2.pull = 3;model:U(alt1) = b1 * transport[1,2,3] + b2 * pull[1,2,3] + b3 * medical[1,2,3] + b4 * care[1,2,3] + b5 * room[1,2,3] + b6 * price[1,2,3]/U(alt2) = b1 * transport + b2 * pull + b3 * medical + b4 * care + b5 * room + b6 * price
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2.贝叶斯Efficient设计
Design;alts = alt1, alt2;rows = 36 #选择集数量;block = 6, minsum #分块;eff = (mnl, d, mean) #d代表优化指标为d-error，mnl代表为mnl模型设计;alg = mfederov;reject: #拒绝xxx设定，D-efficient设计中或许不应该使用alt1.A1 = 1 AND alt1.A2 = 3, alt2.A1 = 1 AND alt2.A2 = 3 #各系数中的(n, xxx, xxx)分别为正态分布、先验系数、系数标准误
;model:U(alt1) =b1[(n,0.316353,0.193319)] * A1[1,2,3]+ b2[(n,0.117917,0.193189)] * A2[1,2,3]+ b3[(n,0.141798,0.219122)] * A3[1,2,3]+ b4[(n,0.400984,0.218145)] * A4[1,2,3]+ b5[(n,1.228532,0.272834)] * A5[1,2,3]+ b6[(n,-0.466797,0.223458)] * A6[1,2,3]/U(alt2) =b1 * A1+ b2 * A2+ b3 * A3+ b4 * A4+ b5 * A5+ b6 * A6
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Design;alts = alt1, alt2 ;rows = 36 ;block = 6, minsum ;eff = (mnl,d) ;alg = mfederov #下述dummy为分类变量时使用，以后续选择1作为基准，[(第2级系数的贝叶斯参数)|(第3级系数的贝叶斯参数)]
;model:U(alt1) = b1.dummy[(n,0.205162,0.587737)|(n,0.349369,0.477450)] * transport[1,2,3] + b2.dummy[(n,1.053903,0.482206)|(n,-0.350811,0.604574)] * pull[1,2,3] + b3.dummy[(n,1.315023,0.614181)|(n,0.706547,0.524775)] * medical[1,2,3] + b4.dummy[(n,1.716064,0.580963)|(n,2.271936,0.684494)] * care[1,2,3] + b5.dummy[(n,2.716013,0.746149)|(n,3.293016,0.780793)] * room[1,2,3] + b6.dummy[(n,-0.370111,0.693573)|(n,-1.251211,0.609407)] * price[1,2,3]/U(alt2) = b1 * transport + b2 * pull + b3 * medical + b4 * care + b5 * room + b6 * price
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