data(mite)
x <- as.matrix(mite)[1:12, 21:30]
## non-sequential nullmodel
(nm <- nullmodel(x, "r00"))
(sm <- simulate(nm, nsim=10))
## sequential nullmodel
(nm <- nullmodel(x, "swap"))
(sm1 <- simulate(nm, nsim=10, thin=5))
(sm2 <- simulate(nm, nsim=10, thin=5))
## sequential nullmodel with burnin and extra updating
(nm <- nullmodel(x, "swap"))
(sm1 <- simulate(nm, burnin=10, nsim=10, thin=5))
(sm2 <- simulate(nm, nsim=10, thin=5))
## sequential nullmodel with separate initial burnin
(nm <- nullmodel(x, "swap"))
nm <- update(nm, nsim=10)
(sm2 <- simulate(nm, nsim=10, thin=5))
## combining multiple simmat objects
## stratification
nm1 <- nullmodel(x[1:6,], "r00")
sm1 <- simulate(nm1, nsim=10)
nm2 <- nullmodel(x[7:12,], "r00")
sm2 <- simulate(nm2, nsim=10)
smbind(sm1, sm2, MARGIN=1)
## binding subsequent samples from sequential algorithms
## start, end, thin retained
nm <- nullmodel(x, "swap")
nm <- update(nm, nsim=10)
sm1 <- simulate(nm, nsim=10, thin=5)
sm2 <- simulate(nm, nsim=20, thin=5)
sm3 <- simulate(nm, nsim=10, thin=5)
smbind(sm3, sm2, sm1, MARGIN=3)
## 'replicate' based usage which is similar to the output
## of 'parLapply' or 'mclapply' in the 'parallel' package
## start, end, thin are set, also noting number of chains
smfun <- function(x, burnin, nsim, thin) {
nm <- nullmodel(x, "swap")
nm <- update(nm, nsim=burnin)
simulate(nm, nsim=nsim, thin=thin)
}
smlist <- replicate(3, smfun(x, burnin=50, nsim=10, thin=5), simplify=FALSE)
smbind(smlist, MARGIN=3) # Number of permuted matrices = 30
if (FALSE) {
## parallel null model calculations
library(parallel)
if (.Platform$OS.type == "unix") {
## forking on Unix systems
smlist <- mclapply(1:3, function(i) smfun(x, burnin=50, nsim=10, thin=5))
smbind(smlist, MARGIN=3)
}
## socket type cluster, works on all platforms
cl <- makeCluster(3)
clusterEvalQ(cl, library(vegan))
clusterExport(cl, c("smfun", "x"))
smlist <- parLapply(cl, 1:3, function(i) smfun(x, burnin=50, nsim=10, thin=5))
stopCluster(cl)
smbind(smlist, MARGIN=3)
}
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