The package 'VGAM' (all caps) has a betabinomial model fitting facility
in it.
(I doubt this will save your soul, by the way, but it may save your
butt. I think that is probably your most immediate concern, though.)
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Zaihra T
Sent: Thursday, 20 March 2008 2:52 AM
To: r-help at stat.math.ethz.ch
Subject: [R] betabinomial model
Hi,
can anyone help me fit betabinomial model to the following dataset
where
each iD is a cluster in itself , if i use package aod 's betabinom
model it
gives an estimate of zero to phi(the correlation coeficient ) and if
i fix
it to the anova type estimate obtained from icc( in package aod) then
it
says system is exactly singular. And when i try to fit my
loglikelihood by
writing a function directly for loglikelihood and try to optimise
using
optim it gives warnings as well as error and if i use NLMINB
then
convergence is false.
Can someone save my soul..... pl!!!!!!!!z below is my program
script
..............
#ovarian cancer data and parity among 769 probands and their mothers
#cancer in proband
y1<-c(1,1,1,1,1,1,1,rep(1,29),rep(1,36),rep(1,41),r!
ep(1,85),rep(1,105),rep(1,84),
1,rep(0,22),rep(0,33),rep(0,38),rep(0,50),rep(0,103),rep(0,135))
#cancer in mother
y2<-c(1,1,1,1,1,1,1,rep(0,29),rep(0,36),rep(0,41),rep(0,85),rep(0,105),r
ep(0
,84),
1,rep(0,22),rep(0,33),rep(0,38),rep(0,50),rep(0,103),rep(0,135))
# total events in each cluster
y<-y1+y2
n<-rep(2,769)
z<-rep(1,length(y))
n1<-length(y)
# number of childbirths in mother (0 for 1-2,1 for 3+)
z1<-c(0,0,1,1,1,1,1,rep(0,29),rep(0,36),rep(0,41),rep(1,85),rep(1,105),r
ep(1
,84),
1,rep(0,22),rep(0,33),rep(0,38),rep(1,50),rep(1,103),rep(1,135))
# of child births in mother three catag 2 dummy variables z2=1(1-2)
else
0,z3=1(o birth)else0so if z2
# z3 both r zero its 3 births catageroy
z2<-c(0,1,0,0,0,1,0,rep(0,29),rep(1,36),rep(0,41),rep(0,85),rep(1,105),r
ep(0
,84),
0,rep(0,22),rep(1,33),rep(0,38),rep(0,50),rep(1,103),rep(0,135))
z3<-c(1,0,1,1,1,0,0,r!
ep(1,29),rep(0,36),rep(0,41),rep(1,85),rep(0,105),rep(0,84),
1,r ep(1,22),rep(0,33),rep(0,38),rep(1,50),rep(0,103),rep(0,135))
cancer<-as.data.frame(cbind(y1,y2,y,z,z1,z2,n))
cancer
# fitting betabinomial model accounting for icc-----------------
beta.binomial<- betabin(cbind(y, n - y) ~ z1 +z2+z3, ~ 1, data
=cancer)
beta.binomial
# calculating ICC using REML/ML method-------------
intra.clus<-icc(n,y,data=cancer)
intra.clus
a1<-0
a2<-0
#loglikelihood function for ordinary betabinomial model------------
b.bin<-function(th){
beta0<-th[1]
beta1<-th[2]
beta2<-th[3]
beta3<-th[4]
rho<-th[5]
x<-cbind(z,z1,z2,z3)
beta<-c(beta0,beta1,beta2,beta3)
p<-exp(x%*%beta)
q<-(1+x%*%beta)
for(i in 1:n1)
{
for (j in 0:y[i]-1)
{
a1<-a1+log(ifelse(y[i]-1<0,1,p[i]+rho*q*j))
#a1<-a1+log(ifelse(y[i]-1<0 ||p[i]+rho*q*j<=0,1,p[i]+rho*q*j! ))
}
}
for(i in 1:n1)
{
for (j in 0:1-y[i])
{
a2<-a2+log(ifelse(1-y[i]<0,1,1+rho*q*j))
#a2<-a2+log(ifelse(1-y[i]<0 ||1+rho*q*j<=0 ,1,1+rho*q*j))
}
}
out<--(a1+a2-n1*log(ifelse(1+rho<=0,1,1+rho)))
}
d<--b.bin(c(-1.2628,-0.0936,0.3485,0.6155,-.2918))
+sum(log(factorial(2)/factorial(y)*factorial(2-y)))
d
betabinom<- optim(c(-1.2628,-0.0936,0.3485,0.6155,-.2918),b.bin,
method "BFGS")
betabinom<-nlminb(start=c(-1.2628,-0.0936,0.3485,0.6155,-.2918),b.bin)
betabinom
beta.binomial<- betabin(cbind(y, n - y) ~ z1 +z2+z3, ~ 1,
data
=cancer,fixpar=list(5,-.2918))
beta.binomial
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