#-----------------------------------------------------------------------
## HT Gamma
## WinBUGS code for a hyperbolic tangent model with Gamma prior on alpha
#-----------------------------------------------------------------------
HTGamma <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- pow((exp(d[i]) / (exp(d[i]) + exp(-d[i]))), alpha)
}
alpha ~ dgamma(p1, p2)
}
#-----------------------------------------------------------------------
## HT Unif
## WinBUGS code for a hyperbolic tangent model with Uniform prior on alpha
#-----------------------------------------------------------------------
HTUnif <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- pow((exp(d[i]) / (exp(d[i]) + exp(-d[i]))), alpha)
}
alpha ~ dunif(p1, p2)
}
#-----------------------------------------------------------------------
## HT Lognormal
## WinBUGS code for a hyperbolic tangent model with Lognormal prior on alpha
#-----------------------------------------------------------------------
HTLogNormal <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- pow((exp(d[i]) / (exp(d[i]) + exp(-d[i]))), alpha)
}
prec <- 1/p2
alpha ~ dlnorm(p1, prec)
}
#-----------------------------------------------------------------------
## Logistic Gamma
## WinBUGS code for a one-parameter logistic model with Gamma prior on alpha (slope of logistic function)
## Intercept parameter is fixed at 3.0
#-----------------------------------------------------------------------
LogisticGamma <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- exp(3.0 + alpha * d[i]) / (1 + exp(3.0 + alpha * d[i]))
}
alpha ~ dgamma(p1, p2)
}
#-----------------------------------------------------------------------
## Logistic Uniform
## WinBUGS code for a one-parameter logistic model with Uniform prior on alpha (slope of logistic function)
## Intercept parameter is fixed at 3.0
#-----------------------------------------------------------------------
LogisticUnif <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- exp(3.0 + alpha * d[i]) / (1 + exp(3.0 + alpha * d[i]))
}
alpha ~ dunif(p1, p2)
}
#-----------------------------------------------------------------------
## Logistic Lognormal
## WinBUGS code for a one-parameter logistic model with Lognormal prior on alpha (slope of logistic function)
## Intercept parameter is fixed at 3.0
#-----------------------------------------------------------------------
LogisticLogNormal <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- exp(3.0 + alpha * d[i]) / (1 + exp(3.0 + alpha * d[i]))
}
prec <- 1/p2
alpha ~ dlnorm(p1, prec)
}
#-----------------------------------------------------------------------
## Power Gamma
## WinBUGS code for a power model with Gamma prior on alpha
#-----------------------------------------------------------------------
PowerGamma <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- pow(d[i], alpha)
}
alpha ~ dgamma(p1, p2)
}
#-----------------------------------------------------------------------
## Power Uniform
## WinBUGS code for a power model with Uniform prior on alpha
#-----------------------------------------------------------------------
PowerUnif <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- pow(d[i], alpha)
}
alpha ~ dunif(p1, p2)
}
## Power LogNormal
## WinBUGS code for a power model with LogNormal prior on alpha
#-----------------------------------------------------------------------
PowerLogNormal <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- pow(d[i], alpha)
}
prec <- 1/p2
alpha ~ dlnorm(p1, prec)
}
#-----------------------------------------------------------------------
## 2-parameter Logistic Bivariate Lognormal
## WinBUGS code for a two-parameter logistic model with Bivariate Lognormal prior on alpha[1] (intercept) and alpha[2] (slope of logistic function)
#-----------------------------------------------------------------------
TwoPLogisticLogNormal <- function(){
for (i in 1:N1){
s[i] ~ dbin(p[i], n[i])
p[i] <- l.pred[i]/(1 + l.pred[i])
l.pred[i] <- alpha[1]*exp(alpha[2] * d[i])
}
alpha[1] <- exp(log.alpha[1])
alpha[2] <- exp(log.alpha[2])
Omega[1:2, 1:2] <- inverse(p2[, ])
log.alpha[1:2] ~ dmnorm(p1[], Omega[, ])
}
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