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

 ``` 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 49 50 51 52 53 54``` ```# Alligators: multinomial - logistic regression # http://www.openbugs.info/Examples/Aligators.html model { # PRIORS alpha[1] <- 0; # zero contrast for baseline food for (k in 2 : K) { alpha[k] ~ dnorm(0, 0.00001) # vague priors } # Loop around lakes: for (k in 1 : K){ beta[1, k] <- 0 } # corner-point contrast with first lake for (i in 2 : I) { beta[i, 1] <- 0 ; # zero contrast for baseline food for (k in 2 : K){ beta[i, k] ~ dnorm(0, 0.00001) # vague priors } } # Loop around sizes: for (k in 1 : K){ gamma[1, k] <- 0 # corner-point contrast with first size } for (j in 2 : J) { gamma[j, 1] <- 0 ; # zero contrast for baseline food for ( k in 2 : K){ gamma[j, k] ~ dnorm(0, 0.00001) # vague priors } } # LIKELIHOOD for (i in 1 : I) { # loop around lakes for (j in 1 : J) { # loop around sizes # Fit standard Poisson regressions relative to baseline lambda[i, j] ~ dflat() # vague priors for (k in 1 : K) { # loop around foods X[i, j, k] ~ dpois(mu[i, j, k]) log(mu[i, j, k]) <- lambda[i, j] + alpha[k] + beta[i, k] + gamma[j, k] culmative.X[i, j, k] <- culmative(X[i, j, k], X[i, j, k]) } } } # TRANSFORM OUTPUT TO ENABLE COMPARISON # WITH AGRESTI'S RESULTS for (k in 1 : K) { # loop around foods for (i in 1 : I) { # loop around lakes b[i, k] <- beta[i, k] - mean(beta[, k]); # sum to zero constraint } for (j in 1 : J) { # loop around sizes g[j, k] <- gamma[j, k] - mean(gamma[, k]); # sum to zero constraint } } } ```
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