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# model-060b.bug - model for means and variances of axial and laterial sway data
# as a bivariate for a proper multivariate model - this fits a different mean for
# each direction of sway for each agegroup
data{
D <- dim(movement)
n <- D[1] # n rows
m <- D[2] # m columns
# t is the prior for the covariance matrix
t[1, 1] <- 0.5 # variance of axial
t[1, 2] <- 0.3 # axial/lateral covariance
t[2, 1] <- 0.3 # axial/lateral covariance
t[2, 2] <- 0.5 # variance of lateral
}
model{
for (i in 1:n) {
movement[i, ] ~ dmnorm(c(b0[1] + b1[age[i]], b0[2] + b2[age[i]]), tau)
}
# b1 is bit added to axial movement
for (j in 1:1) {
b1[j] ~ dnorm(0, tau.1)
}
# setting category corresponding to younger ages as the reference category
b1[2] <- 0
# b2 is bit added to lateral movement
for (l in 1:1) {
b2[l] ~ dnorm(0, tau.1)
}
# setting category corresponding to younger ages as the reference category
b2[2] <- 0
# for the mean in each dimension
for (j in 1:m) {
b0[j] ~ dnorm(0, tau.1)
}
# convert precision to covariance
cov <- inverse(tau) * 2
# prior for multivariate covariance matrix is dwishert
tau ~ dwish(t, n)
# prior for the variances of the means is dgamma
tau.1 ~ dgamma(2.5, 2.5)
}
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