Hello All,
I am estimating a Latent Variable Model, of the sort Y[i,j] = b0[j]+b1[j]Z[i], where the Y's are observed and the Z's are not. The data are counts of sentences in party manifestoes over the past 50 years. The Z's are distributed normally with a party-specific mean for the first election and a random walk prior thereafter. Now, I would like to take the Z values and use them as the response variable in a regression, but I don't want the predictors in this second stage to influence the value, so I would need to use something like the cut function. So, here's the basic setup:
Y[i,j] ~ dnorm(mu.y[i,j], tau[j])
mu.y[i,j] <- b[j,1] + b[j,2]*Z[i]
then,
Z.cut[i] <- cut(Z[i])
Z.cut[i] ~ dnorm(mu.z[i], tau)
mu.z[i] <- gamma[1] + gamma[2]* X[i,1] + gamma[3]*X[i,2]
My problem is, that WinBUGS doesn't seem to want to let me say Z.cut[i] <- cut(Z[i]) AND Z.cut[i] ~ dnorm( , ). Does anyone know how I can get around this problem?
Thanks for your help,
Dave.
--
Dave Armstrong
University of Maryland
Dept of Government and Politics
3140 Tydings Hall
College Park, MD 20742
Office: 2103L Cole Field House
Phone: 301-405-9735
e-mail:
[log in to unmask]
web: www.davearmstrong-ps.com
Facts are meaningless. You can use facts to prove anything that's even remotely true. - Homer Simpson
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