Stuart,
Your problem is that you have 14 x number of repondent betas not
p (698) so you need to reflect that in the priors. Try a loop after the
p loop. Something like this:
for(i in 1:num) {
beta[i,1:14]~dmnorm(mu.beta[],d[,]);
}
where num is the number of respondents.
rather than
beta[p,1:14]~dmnorm(mu.beta[],d[,]);
in the p loop.
Good luck!
--Brian
________________________________
Brian P. Griner, Ph.D.
Manager, Telemarketing Modeling
ORC International
PO Box 183
Princeton, NJ 08542
Phone: 908.281.3566
Fax: 908.281.5103
Email: [log in to unmask]
________________________________
-----Original Message-----
From: Stuart Drucker [mailto:[log in to unmask]]
Sent: Tuesday, November 03, 1998 7:10 PM
To: [log in to unmask]
Subject: "Precondition violated" message
Greetings all,
I'm trying to run a BUGS program for a logistic
regression model, and come
up with a "precondition violated" message when I get to
the compile stage in
Winbugs. I'd be very appreciative is someone could clue
me in on what's
wrong?
TIA,
Stuart Drucker
Drucker Analytics
Program follows below (I left out the data I have in
rectangular array
form):
# Model with n=698 company ratings
# Regression model with thirteen explanatory variables
and a constant
# Both x's and y's are dummy coded (1/0)
#model description
{
for(p in 1:698) {
r[p]~dbern( pr[p] );
pr[p]<- exp(mu[p])/( 1+exp(mu[p]) );
mu[p]<- beta[stud[p],1]*q15e[p]+
beta[stud[p],2]*q15j[p]+
beta[stud[p],3]*q15l[p]+
beta[stud[p],4]*q2[p]+
beta[stud[p],5]*q20[p]+
beta[stud[p],6]*q2108[p]+
beta[stud[p],7]*q2109[p]+
beta[stud[p],8]*q2110[p]+
beta[stud[p],9]*age[p]+
beta[stud[p],10]*sex[p]+
beta[stud[p],11]*marr[p]+
beta[stud[p],12]*educ[p]+
beta[stud[p],13]*sears[p]+
beta[stud[p],14] ;
beta[p,1:14]~dmnorm(mu.beta[],d[,]);
}
#distribution assumptions: multivariate normality for
beta[i]
mu.beta[1]~dnorm(0,0.001);
mu.beta[2]~dnorm(0,0.001);
mu.beta[3]~dnorm(0,0.001);
mu.beta[4]~dnorm(0,0.001);
mu.beta[5]~dnorm(0,0.001);
mu.beta[6]~dnorm(0,0.001);
mu.beta[7]~dnorm(0,0.001);
mu.beta[8]~dnorm(0,0.001);
mu.beta[9]~dnorm(0,0.001);
mu.beta[10]~dnorm(0,0.001);
mu.beta[11]~dnorm(0,0.001);
mu.beta[12]~dnorm(0,0.001);
mu.beta[13]~dnorm(0,0.001);
mu.beta[14]~dnorm(0,0.001);
d[1:14,1:14]~dwish(R[,],14);
R[1,1] <- 1.0; R[1,2] <- 0.0; R[1,3] <- 0.0;
R[1,4] <- 0.0;
R[1,5] <- 0.0; R[1,6] <- 0.0; R[1,7] <- 0.0;
R[1,8] <- 0.0; R[1,9]
<- 0.0; R[1,10] <- 0.0;
R[1,11] <- 0.0; R[1,12] <- 0.0; R[1,13] <- 0.0;
R[1,14] <- 0.0;
R[2,1] <- 0.0; R[2,2] <- 1.0; R[2,3] <- 0.0;
R[2,4] <- 0.0;
R[2,5] <- 0.0; R[2,6] <- 0.0; R[2,7] <- 0.0;
R[2,8] <- 0.0; R[2,9]
<- 0.0; R[2,10] <- 0.0;
R[2,11] <- 0.0; R[2,12] <- 0.0; R[2,13] <- 0.0;
R[2,14] <- 0.0;
R[3,1] <- 0.0; R[3,2] <- 0.0; R[3,3] <- 1.0;
R[3,4] <- 0.0;
R[3,5] <- 0.0; R[3,6] <- 0.0; R[3,7] <- 0.0;
R[3,8] <- 0.0; R[3,9]
<- 0.0; R[3,10] <- 0.0;
R[3,11] <- 0.0; R[3,12] <- 0.0; R[3,13] <- 0.0;
R[3,14] <- 0.0;
R[4,1] <- 0.0; R[4,2] <- 0.0; R[4,3] <- 0.0;
R[4,4] <- 1.0;
R[4,5] <- 0.0; R[4,6] <- 0.0; R[4,7] <- 0.0;
R[4,8] <- 0.0; R[4,9]
<- 0.0; R[4,10] <- 0.0;
R[4,11] <- 0.0; R[4,12] <- 0.0; R[4,13] <- 0.0;
R[4,14] <- 0.0;
R[5,1] <- 0.0; R[5,2] <- 0.0; R[5,3] <- 0.0;
R[5,4] <- 0.0;
R[5,5] <- 1.0; R[5,6] <- 0.0; R[5,7] <- 0.0;
R[5,8] <- 0.0; R[5,9]
<- 0.0; R[5,10] <- 0.0;
R[5,11] <- 0.0; R[5,12] <- 0.0; R[5,13] <- 0.0;
R[5,14] <- 0.0;
R[6,1] <- 0.0; R[6,2] <- 0.0; R[6,3] <- 0.0;
R[6,4] <- 0.0;
R[6,5] <- 0.0; R[6,6] <- 1.0; R[6,7] <- 0.0;
R[6,8] <- 0.0; R[6,9]
<- 0.0; R[6,10] <- 0.0;
R[6,11] <- 0.0; R[6,12] <- 0.0; R[6,13] <- 0.0;
R[6,14] <- 0.0;
R[7,1] <- 0.0; R[7,2] <- 0.0; R[7,3] <- 0.0;
R[7,4] <- 0.0;
R[7,5] <- 0.0; R[7,6] <- 0.0; R[7,7] <- 1.0;
R[7,8] <- 0.0; R[7,9]
<- 0.0; R[7,10] <- 0.0;
R[7,11] <- 0.0; R[7,12] <- 0.0; R[7,13] <- 0.0;
R[7,14] <- 0.0;
R[8,1] <- 0.0; R[8,2] <- 0.0; R[8,3] <- 0.0;
R[8,4] <- 0.0;
R[8,5] <- 0.0; R[8,6] <- 0.0; R[8,7] <- 0.0;
R[8,8] <- 1.0; R[8,9]
<- 0.0; R[8,10] <- 0.0;
R[8,11] <- 0.0; R[8,12] <- 0.0; R[8,13] <- 0.0;
R[8,14] <- 0.0;
R[9,1] <- 0.0; R[9,2] <- 0.0; R[9,3] <- 0.0;
R[9,4] <- 0.0;
R[9,5] <- 0.0; R[9,6] <- 0.0; R[9,7] <- 0.0;
R[9,8] <- 0.0; R[9,9]
<- 1.0; R[9,10] <- 0.0;
R[9,11] <- 0.0; R[9,12] <- 0.0; R[9,13] <- 0.0;
R[9,14] <- 0.0;
R[10,1] <- 0.0; R[10,2] <- 0.0; R[10,3] <- 0.0;
R[10,4] <- 0.0;
R[10,5] <- 0.0; R[10,6] <- 0.0; R[10,7] <- 0.0;
R[10,8] <- 0.0;
R[10,9] <- 0.0; R[10,10] <- 1.0;
R[10,11] <- 0.0; R[10,12] <- 0.0; R[10,13] <- 0.0;
R[10,14] <- 0.0;
R[11,1] <- 0.0; R[11,2] <- 0.0; R[11,3] <- 0.0;
R[11,4] <- 0.0;
R[11,5] <- 0.0; R[11,6] <- 0.0; R[11,7] <- 0.0;
R[11,8] <- 0.0;
R[11,9] <- 0.0; R[11,10] <- 0.0;
R[11,11] <- 1.0; R[11,12] <- 0.0; R[11,13] <- 0.0;
R[11,14] <- 0.0;
R[12,1] <- 0.0; R[12,2] <- 0.0; R[12,3] <- 0.0;
R[12,4] <- 0.0;
R[12,5] <- 0.0; R[12,6] <- 0.0; R[12,7] <- 0.0;
R[12,8] <- 0.0;
R[12,9] <- 0.0; R[12,10] <- 0.0;
R[12,11] <- 0.0; R[12,12] <- 1.0; R[12,13] <- 0.0;
R[12,14] <- 0.0;
R[13,1] <- 0.0; R[13,2] <- 0.0; R[13,3] <- 0.0;
R[13,4] <- 0.0;
R[13,5] <- 0.0; R[13,6] <- 0.0; R[13,7] <- 0.0;
R[13,8] <- 0.0;
R[13,9] <- 0.0; R[13,10] <- 0.0;
R[13,11] <- 0.0; R[13,12] <- 0.0; R[13,13] <- 1.0;
R[13,14] <- 0.0;
R[14,1] <- 0.0; R[14,2] <- 0.0; R[14,3] <- 0.0;
R[14,4] <- 0.0;
R[14,5] <- 0.0; R[14,6] <- 0.0; R[14,7] <- 0.0;
R[14,8] <- 0.0;
R[14,9] <- 0.0; R[14,10] <- 0.0;
R[14,11] <- 0.0; R[14,12] <- 0.0; R[14,13] <- 0.0;
R[14,14] <- 1.0;
sigma2.beta[14,14]<-inverse(d[,],14,14);
}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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