Dear all Bugs user,
Recently I am looking into the relationship between Shark's fin length
(fl) and regions, group, sex by using BMA. See my two Winbugs files
below, 18 different models were built up. One used the original data,
one used standardized data. The one used original data has the totally
different results from the one with standardized data has. Which one is
more proper? I found many examples standardized their data, but I have
no clue why? Really appreciate any one to help me solve this puzzle.
#### Original data
model
{
for(i in 1:M) {
fl[i] ~ dnorm(mu[i],tau)
mu[i] <- a1 + Ind.group*a2*group[i] + Ind.sex*a3*sex[i] +
Ind.region*a4*region[i] + Ind.groupbysex*a5*group[i]*sex[i] +
Ind.groupbyregion*a6*group[i]*region[i] +
Ind.sexbyregion*a7*sex[i]*region[i]
}
tau~dgamma(1,1)
tau.constant<- 1.0E-6
tau.group <- 1.0E-6+(Ind.group*1.0E-5)
tau.sex<-1.0E-6+(Ind.sex*1.0E-5)
tau.region<- 1.0E-6+(Ind.region*1.0E-5)
tau.group.sex <-1.0E-6+(Ind.groupbysex*1.0E-5)
tau.group.region<- 1.0E-6+(Ind.groupbyregion*1.0E-5)
tau.sex.region <-1.0E-6+(Ind.sexbyregion*1.0E-5)
a1 ~ dnorm(0, tau.constant)
a2~ dnorm(0, tau.group)
a3~ dnorm(0, tau.sex)
a4 ~ dnorm(0, tau.region)
a5~ dnorm(0, tau.group.sex)
a6~ dnorm(0, tau.group.region)
a7~ dnorm(0, tau.sex.region)
Model ~ dcat(p[])
for (i in 1:18)
{
p[i]<- 1/18
Ind.Model[i]<-equals(Model,i)
}
Ind.group<-equals(Model,2)+equals(Model,5)+equals(Model,6)+equals(Model,
8)+equals(Model,9)+equals(Model,10)+step(Model-11.5)
Ind.sex<-equals(Model,3)+equals(Model,5)+equals(Model,7)+equals(Model,8)
+equals(Model,9)+step(Model-10.5)
Ind.region<-equals(Model,4)+equals(Model,6)+equals(Model,7)+equals(Model
,8)+step(Model-9.5)
Ind.groupbysex<-equals(Model,9)+equals(Model,12)+equals(Model,15)+equals
(Model,16)+equals(Model,18)
Ind.groupbyregion<-equals(Model,10)+equals(Model,13)+equals(Model,15)+eq
uals(Model,17)+equals(Model,18)
Ind.sexbyregion<-equals(Model,11)+equals(Model,14)+step(Model-15.5)
}
Data click on one of the arrows to open the data
Initial values
list(tau=1, Model=1)
#`Results
node mean sd MC error 2.5% median 97.5%
start sample
Ind.Model[1] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[2] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[3] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[4] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[5] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[6] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[7] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[8] 0.004559 0.06736 0.002203 0.0
0.0 0.0 5001 20400
Ind.Model[9] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[10] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[11] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[12] 0.0 0.0 7.001E-13 0.0 0.0
0.0 5001 20400
Ind.Model[13] 0.8742 0.3316 0.01016 0.0 1.0 1.0
5001 20400
Ind.Model[14] 0.003039 0.05505 0.001531 0.0
0.0 0.0 5001 20400
Ind.Model[15] 0.05559 0.2291 0.006856 0.0 0.0
1.0 5001 20400
Ind.Model[16] 1.961E-4 0.014 1.541E-4 0.0
0.0 0.0 5001 20400
Ind.Model[17] 0.0602 0.2378 0.007913 0.0 0.0
1.0 5001 20400
Ind.Model[18] 0.002206 0.04691 6.672E-4 0.0
0.0 0.0 5001 20400
###Standardized data
model
{
mean.group<-mean(group[1:1153])
mean.sex<- mean(sex[1:1153])
mean.region<- mean(region[1:1153])
mean.groupsex<-mean(groupsex[1:1153])
mean.groupregion<-mean(groupregion[1:1153])
mean.sexregion<-mean(sexregion[1:1153])
sd.group<-sd(group[1:1153])
sd.sex<-sd(sex[1:1153])
sd.region<- sd(region[1:1153])
sd.groupsex<-sd(groupsex[1:1153])
sd.groupregion<-sd(groupregion[1:1153])
sd.sexregion<-sd(sexregion[1:1153])
for(i in 1:M) {
groupregion[i]<-group[i]*region[i]
groupsex[i]<-group[i]*sex[i]
sexregion[i]<-sex[i]*region[i]
G[i]<-(group[i]-mean.group)/sd.group
S[i]<-(group[i]-mean.sex)/sd.sex
R[i]<-(region[i]-mean.region)/sd.region
GS[i]<-(groupsex[i]-mean.groupsex)/sd.groupsex
GR[i]<-(groupregion[i]-mean.groupregion)/sd.groupregion
SR[i]<-(sexregion[i]-mean.sexregion)/sd.sexregion
}
for(i in 1:M) {
fl[i] ~ dnorm(mu[i],tau)
mu[i] <- a1 + Ind.group*a2*G[i] + Ind.sex*a3*S[i] +
Ind.region*a4*R[i] + Ind.groupbysex*a5*GS[i] +
Ind.groupbyregion*a6*GR[i] + Ind.sexbyregion*a7*SR[i]
}
tau~dgamma(1,1)
tau.constant<- 1.0E-6
tau.group <- 1.0E-6+(Ind.group*1.0E-5)
tau.sex<-1.0E-6+(Ind.sex*1.0E-5)
tau.region<- 1.0E-6+(Ind.region*1.0E-5)
tau.group.sex <-1.0E-6+(Ind.groupbysex*1.0E-5)
tau.group.region<- 1.0E-6+(Ind.groupbyregion*1.0E-5)
tau.sex.region <-1.0E-6+(Ind.sexbyregion*1.0E-5)
a1 ~ dnorm(0, tau.constant)
a2~ dnorm(0, tau.group)
a3~ dnorm(0, tau.sex)
a4 ~ dnorm(0, tau.region)
a5~ dnorm(0, tau.group.sex)
a6~ dnorm(0, tau.group.region)
a7~ dnorm(0, tau.sex.region)
Model ~ dcat(p[])
for (i in 1:18)
{
p[i]<- 1/18
Ind.Model[i]<-equals(Model,i)
}
Ind.group<-equals(Model,2)+equals(Model,5)+equals(Model,6)+equals(Model,
8)+equals(Model,9)+equals(Model,10)+step(Model-11.5)
Ind.sex<-equals(Model,3)+equals(Model,5)+equals(Model,7)+equals(Model,8)
+equals(Model,9)+step(Model-10.5)
Ind.region<-equals(Model,4)+equals(Model,6)+equals(Model,7)+equals(Model
,8)+step(Model-9.5)
Ind.groupbysex<-equals(Model,9)+equals(Model,12)+equals(Model,15)+equals
(Model,16)+equals(Model,18)
Ind.groupbyregion<-equals(Model,10)+equals(Model,13)+equals(Model,15)+eq
uals(Model,17)+equals(Model,18)
Ind.sexbyregion<-equals(Model,11)+equals(Model,14)+step(Model-15.5)
}
Data click on one of the arrows to open the data
Initial values
list(tau=1, Model=1)
#`Results
node mean sd MC error 2.5% median 97.5%
start sample
Ind.Model[1] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[2] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[3] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[4] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[5] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[6] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[7] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[8] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[9] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[10] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[11] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[12] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[13] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[14] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[15] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[16] 0.007037 0.08359 0.001292 0.0
0.0 0.0 15001 750000
Ind.Model[17] 0.0 0.0 5.164E-14 0.0 0.0
0.0 15001 750000
Ind.Model[18] 0.993 0.08359 0.001292 1.0 1.0
1.0 15001 750000
___________________________
Guojing YANG
Research Fellow
Wildlife & Landscape Sciences Theme
School for Environmental Research
Charles Darwin University
Darwin NT 0909
Ph: (08) 8946 6646
Fax: (08) 8946 7720
http://www.cdu.edu.au/ser/GuojingYangProfile.htm
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