1 files changed, 23 insertions, 24 deletions

diff --git a/tests/examplefiles/example.bug b/tests/examplefiles/example.bug
index b5b2fe7f..9ccd531d 100644
--- a/tests/examplefiles/example.bug
+++ b/tests/examplefiles/example.bug
@@ -1,55 +1,54 @@
 # Alligators: multinomial - logistic regression 
 #  http://www.openbugs.info/Examples/Aligators.html
 model {
-	   
-   # PRIORS	
+   # PRIORS    
    alpha[1] <- 0; # zero contrast for baseline food
    for (k in 2 : K) { 
-       alpha[k] ~ dnorm(0, 0.00001) # vague priors
+     alpha[k] ~ dnorm(0, 0.00001) # vague priors
    } 
    # Loop around lakes:
    for (k in 1 : K){ 
-        beta[1, k] <- 0 
+      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
-	} 
+     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 
+     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
-       } 
+     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
+     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])
-	   }
-       } 
+       # 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
+         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
+         g[j, k] <- gamma[j, k] - mean(gamma[, k]); # sum to zero constraint
        }
    }
 }