lab:zhang:bayesian_exploratory_factor_analysis_using_informative_priors
Table of Contents
Bayesian exploratory factor analysis using informative priors
Data generation
model{ for (i in 1:N){ f[i,1:2]~dmnorm(muf[1:2], pre.phi[1:2,1:2]) for (j1 in 1:6){ muy[i,j1]<-l1[j1]*f[i,1]+l2[j1]*f[i,2] y[i,j1]~dnorm(muy[i,j1],pre.p[j1]) } } } list(N=200, l1=c(.6,.6,.6,0,0,0), l2=c(0,0,0, .6,.6,.6), muf=c(0,0), pre.p=c(1.5625,1.5625, 1.5625, 2.78, 2.78, 2.78), pre.phi=structure(.Data =c(1.33, -0.67, -.67, 1.33), .Dim = c(2,2)))
Data analysis
model{ for (i in 1:N){ f[i,1:2]~dmnorm(muf[1:2], pre.phi[1:2,1:2]) for (j1 in 1:6){ muy[i,j1]<-l1[j1]*f[i,1]+l2[j1]*f[i,2] y[i,j1]~dnorm(muy[i,j1],pre.p[j1]) } } muf[1]<-0 muf[2]<-0 for (i in 1:6){ l1[i]~dbeta(.5, .5) l2[i]~dbeta(.5,.5) pre.p[i]~dgamma(.001,.001) } pre.phi[1:2,1:2]<-inverse(phi[1:2,1:2]) phi[1,1]<-1 phi[2,2]<-1 phi[1,2]~dunif(0,1) phi[2,1]<-phi[1,2] } list( pre.p=c(1.5625,1.5625, 1.5625, 2.78, 2.78, 2.78), phi=structure(.Data =c(NA, 0, NA, NA), .Dim = c(2,2))) list(N=200, y = structure(.Data = c( -2.226,-0.1909,-1.194,-1.218,-1.153, -0.6484,0.8834,-0.08889,-0.6603,-0.6184, 0.2678,-0.2329,0.2418,0.4796,-0.005651, 0.1539,0.1769,-0.1913,0.1777,0.4438, -0.984,0.776,0.8142,1.485,0.2186, -0.356,-0.35,0.3692,0.6646,-0.7837, -2.954,-0.7462,-1.149,-0.2208,-0.5557, -0.02962,0.7973,2.989,2.072,1.432, 1.242,1.122,-0.5304,2.044,0.1183, -0.346,-0.1987,-0.428,-0.5017,0.149, -1.003,-0.5784,0.2037,-0.6927,-1.568, -0.9764,-1.015,-0.5815,-0.387,0.1545, 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lab/zhang/bayesian_exploratory_factor_analysis_using_informative_priors.txt · Last modified: 2016/01/24 09:48 by 127.0.0.1