sflist2stanfit {rstan} | R Documentation |
Merge a list of stanfit objects into one
Description
This function takes a list of stanfit
objects and returns a
consolidated stanfit
object. The stanfit
objects to be merged
need to have the same configuration of iteration, warmup, and thin, besides
being from the same model. This could facilitate some parallel usage of RStan.
For example, if we call stan
by parallel and it returns a list of
stanfit
objects, this function can be used to create one stanfit
object from the list.
Usage
sflist2stanfit(sflist)
Arguments
sflist |
A list of |
Value
An S4 object of stanfit
consolidated from all the input stanfit
objects.
Note
This function should be called in rare circumstances because
sampling
has a cores
argument that allows multiple
chains to be executed in parallel. However, if you need to depart from that,
the best practice is to use sflist2stanfit
on a list of stanfit
objects created with the same seed
but different chain_id
(see
example below). Using the same seed but different chain_id can make sure
the random number generations for all chains are not correlated.
This function would do some check to see if the stanfit
objects in the input list
can be merged. But the check is not sufficient. So generally, it is the
user's responsibility to make sure the input is correct so that the merging
makes sense.
The date in the new stanfit
object is when it is merged.
get_seed
function for the new consolidated stanfit
object only returns
the seed used in the first chain of the new object.
The sampler such as NUTS2 that is displayed in the printout by print
is the sampler used for the first chain. The print
method assumes the samplers
are the same for all chains.
The included stanmodel
object, which includes the compiled model,
in the new stanfit
object is from the first element of the input list.
References
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. https://mc-stan.org/.
See Also
Examples
## Not run:
library(rstan)
scode <- "
data {
int<lower=1> N;
}
parameters {
array[N] real y1;
array[N] real y2;
}
model {
y1 ~ normal(0, 1);
y2 ~ double_exponential(0, 2);
}
"
seed <- 123 # or any other integer
foo_data <- list(N = 2)
foo <- stan(model_code = scode, data = foo_data, chains = 1, iter = 1)
f1 <- stan(fit = foo, data = foo_data, chains = 1, seed = seed, chain_id = 1)
f2 <- stan(fit = foo, data = foo_data, chains = 2, seed = seed, chain_id = 2:3)
f12 <- sflist2stanfit(list(f1, f2))
## parallel stan call for unix-like OS
library(parallel)
if (.Platform$OS.type == "unix") {
sflist1 <-
mclapply(1:4, mc.cores = 2,
function(i) stan(fit = foo, data = foo_data, seed = seed,
chains = 1, chain_id = i, refresh = -1))
f3 <- sflist2stanfit(sflist1)
}
if (.Platform$OS.type == "windows") { # also works on non-Windows
CL <- makeCluster(2)
clusterExport(cl = CL, c("foo_data", "foo", "seed"))
sflist1 <- parLapply(CL, 1:4, fun = function(cid) {
require(rstan)
stan(fit = foo, data = foo_data, chains = 1,
iter = 2000, seed = seed, chain_id = cid)
})
fit <- sflist2stanfit(sflist1)
print(fit)
stopCluster(CL)
} # end example for Windows
## End(Not run)