Merge branch 'nullmodel-tweaks'

man/nullmodel.Rd

@@ -21,10 +21,9 @@ object without sampling (effective for sequential algorithms).
 \usage{
 nullmodel(x, method)
 \method{print}{nullmodel}(x, ...)
-\method{simulate}{nullmodel}(object, nsim = 1,
-seed = NULL, burnin = 0, thin = 1, ...)
-\method{update}{nullmodel}(object, nsim = 1,
-seed = NULL, ...)
+\method{simulate}{nullmodel}(object, nsim = 1, seed = NULL,
+    burnin = 0, thin = 1, ...)
+\method{update}{nullmodel}(object, nsim = 1, seed = NULL, ...)
 \method{print}{simmat}(x, ...)
 smbind(object, ..., MARGIN, strict = TRUE)
 }

man/oecosimu.Rd

@@ -266,6 +266,22 @@ foo <- function(x, n, nr, nc, ...) {
 cf <- commsim("myshuffle", foo, isSeq = FALSE, binary = FALSE, 
    mode = "double")
 oecosimu(dune, meandist, cf)
+
+## Use pre-built null model
+nm <- simulate(nullmodel(sipoo, "curveball"), 99)
+oecosimu(nm, nestedchecker)
+## Several chains of a sequential model -- this can be generalized
+## for parallel processing (see ?smbind)
+nm <- replicate(5, simulate(nullmodel(sipoo, "swap"), 99,
+   thin=10, burnin=100), simplify = FALSE)
+## nm is now a list of nullmodels: use smbind to combine these into one
+## nullmodel with several chains
+nm <- smbind(nm, MARGIN = 3)
+nm
+oecosimu(nm, nestedchecker)
+## After this you can use as.mcmc() and tools in the coda package to
+## analyse the chains (these will show that thin, burnin and nsimul are
+## all too low for real analysis).
 }
 \keyword{ multivariate }
 \keyword{ datagen }