How to use a distinct data set per chain in Stan? - r

Question

I have a data set with many missing observations and I used the Amelia package to create imputed data sets. I'd like to know if it's possible to run the same model in parallel with a different data set per chain and combine the results into a single Stan object.
# Load packages
library(Amelia)
library(rstan)
# Load built-in data
data(freetrade)
# Create 2 imputed data sets (polity is an ordinal variable)
df.imp <- amelia(freetrade, m = 2, ords = "polity")
# Check the first data set
head(df.imp$imputations[[1]])
# Run the model in Stan
code <- '
data {
int<lower=0> N;
vector[N] tariff;
vector[N] polity;
}
parameters {
real b0;
real b1;
real<lower=0> sigma;
}
model {
b0 ~ normal(0,100);
b1 ~ normal(0,100);
tariff ~ normal(b0 + b1 * polity, sigma);
}
'
# Create a list from the first and second data sets
df1 <- list(N = nrow(df.imp$imputations[[1]]),
tariff = df.imp$imputations[[1]]$tariff,
polity = df.imp$imputations[[1]]$polity)
df2 <- list(N = nrow(df.imp$imputations[[2]]),
tariff = df.imp$imputations[[2]]$tariff,
polity = df.imp$imputations[[2]]$polity)
# Run the model
m1 <- stan(model_code = code, data = df1, chains = 1, iter = 1000)
My question is how to run the last line of code on both data sets at the same time, running 2 chains and combining the output with the same stan() function. Any suggestions?

Answer 1

You can run the models separately, and then combine them using sflist2stanfit().
E.g.
seed <- 12345
s1 <- stan_model(model_code = code) # compile the model
m1 <- sampling(object = s1, data = df1, chains = 1,
seed = seed, chain_id = 1, iter = 1000)
m2 <- sampling(object = s1, data = df2, chains = 1,
seed = seed, chain_id = 2, iter = 1000)
f12 <- sflist2stanfit(list(m1, m2))

Answer 2

You will have to use one of the packages for Parallel computing in R.
According to this post, it should then work:
Will RStan run on a supercomputer?
Here is an example that may work (I use this code with JAGS, will test it with Stan later):
library( doParallel )
cl <- makeCluster( 2 ) # for 2 processes
registerDoParallel( cl )
library(rstan)
# make a function to combine the results
stan.combine <- function(...) { return( sflist2stanfit( list(...) ) ) }
mydatalist <- list(df1 , df2)
myseeds <- c(123, 456)
# now start the chains
nchains <- 2
m_both <- foreach(i=1:nchains ,
.packages = c( 'rstan' ),
.combine = "stan.combine") %dopar% {
result <- stan(model_code = code,
data = mydatalist[[i]], # use the right dataset
seed=myseeds[i], # use different seeds
chains = 1, iter = 1000)
return(result) }
Let me know whether it works with Stan. As I said, I haven't tested it yet.