# pygments-main / tests / examplefiles / example.jag

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48``` ```# lsat.jags example from classic-bugs examples in JAGS # See http://sourceforge.net/projects/mcmc-jags/files/Examples/2.x/ var response[R,T], m[R], culm[R], alpha[T], a[T], theta[N], r[N,T], p[N,T], beta, theta.new, p.theta[T], p.item[R,T], P.theta[R]; data { for (j in 1:culm[1]) { r[j, ] <- response[1, ]; } for (i in 2:R) { for (j in (culm[i - 1] + 1):culm[i]) { r[j, ] <- response[i, ]; } } } model { # 2-parameter Rasch model for (j in 1:N) { for (k in 1:T) { probit(p[j,k]) <- delta[k]*theta[j] - eta[k]; r[j,k] ~ dbern(p[j,k]); } theta[j] ~ dnorm(0,1); } # Priors for (k in 1:T) { eta[k] ~ dnorm(0,0.0001); e[k] <- eta[k] - mean(eta[]); # sum-to-zero constraint delta[k] ~ dnorm(0,1) T(0,); # constrain variance to 1, slope +ve d[k] <- delta[k]/pow(prod(delta), 1/T); # PRODUCT_k (d_k) = 1 g[k] <- e[k]/d[k]; # equivalent to B&A's threshold parameters } # Compute probability of response pattern i, for later use in computing G^2 theta.new ~ dnorm(0,1); # ability parameter for random student for(k in 1:T) { probit(p.theta[k]) <- delta[k]*theta.new - eta[k]; for(i in 1:R) { p.item[i,k] <- p.theta[k]^response[i,k] * (1-p.theta[k])^(1-response[i,k]); } } for(i in 1:R) { P.theta[i] <- prod(p.item[i,]) } } ```