how: How to define a permutation design?

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Utility functions to describe unrestricted and restricted permutation designs for time series, line transects, spatial grids and blocking factors.

Usage

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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)

Arguments

within, plots, blocks

Permutation designs for samples within the levels of plots (within), permutation of plots themselves, or for the definition of blocking structures which further restrict permutations (blocks). within and plots each require a named list as produced by Within and Plots respectively. blocks takes a factor (or an object coercible to a factor via as.factor), the levels of which define the blocking structure.

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 "free", "series", "grid" or "none". See Details.

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 nperm is lower than minperm, sampling is performed from the set of complete permutations to avoid duplicate permutations. See argument complete and Details, below.

all.perms

an object of class allPerms, the result of a call to allPerms.

make

logical; should check generate all possible permutations? Useful if want to check permutation design but not produce the matrix of all permutations, or to circumvent the heuristics governing when complete enumeration is activated.

observed

logical; should the observed permutation be returned as part of the set of all permutations? Default is FALSE to facilitate usage in higher level functions.

constant

logical; should the same permutation be used within each level of strata? If FALSE a separate, possibly restricted, permutation is produced for each level of strata.

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 as.factor, specifying the strata for permutation.

Details

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.

Value

For how a list with components for each of the possible arguments.

Author(s)

Gavin Simpson

References

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).

See Also

shuffle and shuffleSet for permuting from a design, and check, a utility function for checking permutation design described by how.

Examples

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## 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)

Example output

Permutation Design:

Blocks:
  Defined by: none

Plots:
  Plots: plts
  Permutation type: series
  Mirrored?: No

Within Plots:
  Permutation type: series
  Mirrored?: Yes
  Different permutation within each Plot?: Yes

Permutation details:
  Number of permutations: 199
  Max. number of permutations allowed: 9999
  Evaluate all permutations?: No.  Activation limit: 5040

Permutation Design:

Blocks:
  Blocks: blks

Plots:
  Plots: plts
  Permutation type: none
  Mirrored?: No

Within Plots:
  Permutation type: series
  Mirrored?: Yes
  Different permutation within each Plot?: Yes

Permutation details:
  Number of permutations: 199
  Max. number of permutations allowed: 9999
  Evaluate all permutations?: No.  Activation limit: 5040

permute documentation built on May 29, 2017, 11:21 a.m.