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linear predictor 1multinomial logit link 2η mij = α m + β m' x ij + ξ mjPY ij= m | x ij ξ j δ ij =+ δ mijexp { η }m ij1 + Σ M l = 2 {exp η l i j }12m = 1 2 L M responsecategory j = 1 2 L J the cluster i = 1 2 Ln j j 。 Y ij multinomialdistribution conditional on the random effects 1 2 LM M = 1 random error 0Y ij = 1 1 / ( 1 + Σ M expl = 2 [ η ] )l Grilli & Rampichiniij2007。 1- 2 m l odds PY ij = m | x ij ξ j δ ij PY ij = l | x ij ξ j δ ij = expηm ij2。- η mij 3random effect modeling。 U mm = 1 2 L M M ijindividual utilities U mij > U lij l ≠ m Y ij m 。U mij= η mij + ε mij4 η mij ε mij Gumbel distributionindependent and identically distributed errorsMcFadden 1973Grilli & Rampichini 2007。m Intra-class Correlation CoefficientICC m =Varξ mj Varξ mj + Varδ mij + π 2 /35187
2011. 5homogeneity。。、。10% 。14. 6% 、。。、、。、、、。、。 “ ” 。。 2 、 LR testparsimony principles 3。、、、、p < 0. 05 。。、odds ratio 1. 52= e 0. 42 、1. 35= e 0. 30 1. 65= e 0. 51 。9. 4% = e 0. 09 - 1 、 50. 8%= 1 - e - 0. 71 、49. 3% = 1 - e - 0. 68 。、、81% = e - 0. 21 、188
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