Parallel Processing in the sspse Package
As the estimation requires MCMC,
sspse can take
advantage of multiple CPUs or CPU cores on the system on which it runs, as
well as computing clusters. It uses package
to facilitate this, and supports MPI cluster type and likely PSOCK.
scale; Number of threads in which to run the sampling. Defaults to 1 (no parallelism).
API to use for parallel processing. Supported values are
integer; random number integer seed. Defaults to
Names of packages in which load to get the package to run functions in addition to those autodetected. This argument should not be needed outside of very strange setups.
logical; if this is
The number of nodes used and the parallel API are controlled using the
parallel package is used with PSOCK
clusters by default, to utilize multiple cores on a system. The number of
cores on a system can be determined with the
This method works with the base installation of R on all platforms, and does not require additional software.
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## Not run: # Uses 2 SOCK clusters for MCMLE estimation N0 <- 200 n <- 100 K <- 10 # Create probabilities for a Waring distribution # with scaling parameter 3 and mean 5, but truncated at K=10. probs <- c(0.33333333,0.19047619,0.11904762,0.07936508,0.05555556, 0.04040404,0.03030303,0.02331002,0.01831502,0.01465201) probs <- probs / sum(probs) # Look at the degree distribution for the prior # Plot these if you want # plot(x=1:K,y=probs,type="l") # points(x=1:K,y=probs) # # Create a sample # set.seed(1) pop<-sample(1:K, size=N0, replace = TRUE, prob = probs) s<-sample(pop, size=n, replace = FALSE, prob = pop) out <- posteriorsize(s=s,interval=10,parallel=2) plot(out, HPD.level=0.9,data=pop[s]) summary(out, HPD.level=0.9) # Let's look at some MCMC diagnostics plot(out, HPD.level=0.9,mcmc=TRUE) ## End(Not run)
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