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