Description Usage Arguments Details Value Author(s) References See Also Examples
Utility functions to describe unrestricted and restricted permutation designs for time series, line transects, spatial grids and blocking factors.
1 2 3 4 5 6 7 8 9 10 11  how(within = Within(), plots = Plots(), blocks = NULL,
nperm = 199, complete = FALSE, maxperm = 9999,
minperm = 5040, all.perms = NULL, make = TRUE,
observed = FALSE)
Within(type = c("free","series","grid","none"),
constant = FALSE, mirror = FALSE,
ncol = NULL, nrow = NULL)
Plots(strata = NULL, type = c("none","free","series","grid"),
mirror = FALSE, ncol = NULL, nrow = NULL)

within, plots, blocks 
Permutation designs for samples within the
levels of 
nperm 
numeric; the number of permutations. 
complete 
logical; should complete enumeration of all permutations be performed? 
type 
character; the type of permutations required. One of

maxperm 
numeric; the maximum number of permutations to perform. Currently unused. 
minperm 
numeric; the lower limit to the number of possible
permutations at which complete enumeration is performed. When

all.perms 
an object of class 
make 
logical; should 
observed 
logical; should the observed permutation be returned
as part of the set of all permutations? Default is 
constant 
logical; should the same permutation be used within
each level of strata? If 
mirror 
logical; should mirroring of sequences be allowed? 
ncol, nrow 
numeric; the number of columns and rows of samples in the spatial grid respectively. 
strata 
A factor, or an object that can be coerced to a factor
via 
shuffle
can generate permutations for a wide range of
restricted permutation schemes. A small selection of the available
combinations of options is provided in the Examples section below.
Argument mirror
determines whether grid or series permutations
can be mirrored. Consider the sequence 1,2,3,4. The relationship
between consecutive observations is preserved if we reverse the
sequence to 4,3,2,1. If there is no inherent direction in your
experimental design, mirrored permutations can be considered
part of the Null model, and as such increase the number of possible
permutations. The default is to not use mirroring so you must
explicitly turn this on using mirror = TRUE
in how
.
To permute plots rather than the observations within plots (the
levels of strata
), use Within(type = "none")
and
Plots(type = foo)
, where foo
is how you want the plots
to be permuted. However, note that the number of observations within
each plot must be equal!
For some experiments, such as BACI designs, one might wish to use the
same permutation within each plot. This is controlled by
argument constant
. If constant = TRUE
then the same
permutation will be generated for each level of strata
. The
default is constant = FALSE
.
For how
a list with components for each of the possible arguments.
Gavin Simpson
shuffle()
is modelled after the permutation schemes of Canoco
3.1 (ter Braak, 1990); see also Besag & Clifford (1989).
Besag, J. and Clifford, P. (1989) Generalized Monte Carlo significance tests. Biometrika 76; 633–642.
ter Braak, C. J. F. (1990). Update notes: CANOCO version 3.1. Wageningen: Agricultural Mathematics Group. (UR).
shuffle
and shuffleSet
for
permuting from a design, and check
, a utility function
for checking permutation design described by how
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Set up factors for the Plots and Blocks
plts < gl(4, 10) ## 4 Plots of 10 samples each
blks < gl(2, 20) ## 2 Blocks of 20 samples each
## permutation design
h1 < how(within = Within(type = "series", mirror = TRUE),
plots = Plots(strata = plts, type = "series"),
blocks = blks)
## The design can be updated...
## ... remove the blocking:
update(h1, blocks = NULL)
## ... or switch the type of shuffling at a level:
#update(h1, plots = update(getPlots(h1), type = "none"))
plots2 < update(getPlots(h1), type = "none")
update(h1, plots = plots2)

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