**
****
**Rats: a normal hierarchical model

This example is taken from section 6 of Gelfand
*et al
* (1990), and concerns 30 young rats whose weights were measured weekly for five weeks. Part of the data is shown below, where Y
_{ij
}_{
}is the weight of the ith rat measured at age x
_{j
}.

A plot of the 30 growth curves suggests some evidence of downward curvature.

The model is essentially a random effects linear growth curve

Y
_{i
}_{j
} ~ Normal(
a
_{i
} +
b
_{i
}(x
_{j
} - x
_{bar
}),
t
_{c
})

a
_{i
} ~ Normal(
a
_{c
},
t
_{a
})

b
_{i
} ~ Normal(
b
_{c
},
t
_{b
})

where x
_{ba
}_{r
} = 22, and
t
represents the
*precision
* (1/variance) of a normal distribution. We note the absence of a parameter representing correlation between
a
_{i
} and
b
_{i
} unlike in Gelfand
*et al
* 1990. However, see the
Birats
example in Volume 2 which does explicitly model the covariance between
a
_{i
} and
b
_{i
}. For now, we standardise the x
_{j
}'s around their mean to reduce dependence between
a
_{i
} and
b
_{i
} in their likelihood: in fact for the full balanced data, complete independence is achieved. (Note that, in general, prior independence does not force the posterior distributions to be independent).

a
_{c
} ,
t
_{a
}_{
},
b
_{c
}_{
},
t
_{b
}_{
},
t
_{c
}_{
}are given independent ``noninformative'' priors. Interest particularly focuses on the intercept at zero time (birth), denoted
a
_{0
} =
a
_{c
} -
b
_{c
} x
_{bar
}.

*Graphical model for rats example:
*

BUGS
* language for rats example:
*

model

{

for( i in 1 : N ) {

for( j in 1 : T ) {

Y[i , j] ~ dnorm(mu[i , j],tau.c)

mu[i , j] <- alpha[i] + beta[i] * (x[j] - xbar)

culmative.Y[i , j] <- culmative(Y[i , j], Y[i , j])

post.pv.Y[i , j] <- post.p.value(Y[i , j])

prior.pv.Y[i , j] <- prior.p.value(Y[i , j])

replicate.post.Y[i , j] <- replicate.post(Y[i , j])

pv.post.Y[i , j] <- step(Y[i , j] - replicate.post.Y[i , j])

replicate.prior.Y[i , j] <- replicate.prior(Y[i , j])

pv.prior.Y[i , j] <- step(Y[i , j] - replicate.prior.Y[i , j])

}

alpha[i] ~ dnorm(alpha.c,alpha.tau)

beta[i] ~ dnorm(beta.c,beta.tau)

}

tau.c ~ dgamma(0.001,0.001)

sigma <- 1 / sqrt(tau.c)

alpha.c ~ dnorm(0.0,1.0E-6)

alpha.tau ~ dgamma(0.001,0.001)

beta.c ~ dnorm(0.0,1.0E-6)

beta.tau ~ dgamma(0.001,0.001)

alpha0 <- alpha.c - xbar * beta.c

}

Note the use of a very flat but conjugate prior for the population effects: a locally uniform prior could also have been used.

__Data
__
( click to open )

__Inits
__
( click to open )

(Note: the response data (Y) for the rats example can also be found in the file ratsy.odc in rectangular format. The covariate data (x) can be found in S-Plus format in file ratsx.odc. To load data from each of these files, focus the window containing the open data file before clicking on "load data" from the "Specification" dialog.)

Results

A 1000 update burn in followed by a further 10000 updates gave the parameter estimates:

These results may be compared with Figure 5 of Gelfand
*et al
* 1990 --- we note that the mean gradient of independent fitted straight lines is 6.19.

Gelfand
*et al
* 1990 also consider the problem of missing data, and delete the last observation of cases 6-10, the last two from 11-20, the last 3 from 21-25 and the last 4 from 26-30. The appropriate data file is obtained by simply replacing data values by NA (see below). The model specification is unchanged, since the distinction between observed and unobserved quantities is made in the data file and not the model specification.

__Data
__
( click to open )

Gelfand
*et al
* 1990 focus on the parameter estimates and the predictions for the final 4 observations on rat 26. These predictions are obtained automatically in
*BUGS
* by monitoring the relevant Y[] nodes. The following estimates were obtained:

We note that our estimate 6.58 of
b
_{c
} is substantially greater than that shown in Figure 6 of Gelfand
*et
*
*al
*1990. However, plotting the growth curves indicates some curvature with steeper gradients at the beginning: the mean of the estimated gradients of the reduced data is 6.66, compared to 6.19 for the full data. Hence we are inclined to believe our analysis. The observed weights for rat 26 were 207, 257, 303 and 345, compared to our predictions of 204, 250, 295 and 341.