# Could anyone help convert this BUGS code to stan code

Tanks a lot for help convert this BUGS code to stan code!!!

``````model{
for(i in 1:N){
# Data model (or likelihood) for the observed NPP data:
NPP[i] ~ dnorm(mu[i], tau)
# Generate “replicated data” to evaluate model fit.
NPP.rep[i] ~ dnorm(mu[i],tau)
# Define model for latent (mean) NPP; Event[,k] represents the amount
# of precipitation received in different size classes, where k indexes
# the even size class (k=1 for < 5 mm; k=2 for 5-15 mm; k=3 for 15
# 30 mm; k=4 for >30 mm); convert antecedent precipitation (antX) from
# inches to mm.
mu[i] <- a[1] + a[2]*antX[YearID[i]]*25.4 + a[3]*Event[i,1] + a[4]*Event[i,2] +
a[5]*Event[i,3] + a[6]*Event[i,4]
# Some of the precipitation event data are missing, so specify a simple
# data model for the Event data for the purpose of estimating the
# missing data:
for(k in 1:4){
Event[i,k] ~ dnorm(mu.ev[k], tau.ev[k])
}
}
# Dirichlet prior for monthly precipitation weights (due to restrictions
# on when the built-in dirichlet distribution can be used, we are required
# to use the relationship between the gamma distribution and the dirichlet
# to assign the dirichlet prior. For each time block into the past, assign
# the unnormalized weight (deltaX) a gamma(1,1) prior:
for(j in 1:Nblocks){
deltaX[j] ~ dgamma(1,1)
}
for(t in 1:Nlag){
# Compute the yearly weights:
yr.w[t] <- sum(weight[,t])
alphad[t] <- 1
for(m in 1:12){
# Redefine the unnormalized monthly weights to account for post-ANPP
# harvest period; i.e., 2nd part involving equals and step functions
# sets weight = 0 if in year 1 and in Oct, Nov, or Dec (i.e., post
# ANPP harvest).
delta[m,t] <- (deltaX[block[t,m]])*(1-equals(t,1)*step(m-9.5))
# Compute normalized monthly weights, which will be between 0 and 1,
# and will some to one.
weight[m,t] <- delta[m,t]/sumD
# Reorder the weights to go from most recent month (Dec of current
# year) to “oldest” month (Jan at past year = Nlag).
weightOrdered[(t-1)*12 + (12-m+1)] <- weight[m,t]
# For each time into the past, compute the weighted precipitation
# variable.
for(i in Nlag:Nyrs){
antX1[i,m,t] <- weight[m,t]*ppt[i-t+1,m]
}
}
}
# Compute sum of deltas (unnormalized weights), to be used to compute
# the normalized antecedent weights:
for(t in 1:Nlag){
sumD1[t] <- sum(delta[,t])
}
sumD <- sum(sumD1[])

# Compute the cumulative monthly weights:
for(t in 1:(12*Nlag)){
cum.weight[t] <- sum(weightOrdered[1:t])
}
# Compute the month within year weights (alpha’s = wP,m in Box 1 in main
# text); that is, these weights sum to 1 within each past year
for(m in 1:12){
for(t in 1:Nlag){
alpha[m,t] <- delta[m,t]/sum(delta[,t])
}
}
# Compute antecedent precipitation by summing the weighted precipitation
# variable over months and past years:
for(i in Nlag:Nyrs){
for(t in 1:Nlag){
ant.sum1[i,t] <- sum(antX1[i,,t])
}
antX[i] <- sum(ant.sum1[i,])
}
# Assign priors to the ANPP regression parameters (covariate effects):
for(k in 1:6){
a[k] ~ dnorm(0,0.0000001)
}
# Prior for residual (observation) standard deviation, and compute
# associated precision:
sig ~ dunif(0,100)
tau <- pow(sig,-2)
# Priors for parameters in the Event missing data model:
for(k in 1:4){
mu.ev[k] ~ dunif(0,500)
sig.ev[k] ~ dunif(0,500)
tau.ev[k] <- pow(sig.ev[k],-2)
}
}
``````

somebody help please QAQ

I recommend first reading through the Stan User’s Guide, and then start using the Stan Functions Reference to find the equivalent Stan commands for the BUGS commands here. If you understand the BUGS code, this should not be a huge leap as they are very similar languages. Once you do that, you can attempt to compile it, which will tell you if you have any errors in your code. Finally, you can then test it with simulated data and do a parameter recovery. This will expose any warnings or initialization failures you need to address, and, ultimately, tell you if the model is working correctly in Stan.

Once you have your model built out in Stan language, you can also return to the forums for advice if you’re stuck.

2 Likes

Thank you very much for your patient reply.