R: Deprecated Functions in vegan package

vegan-deprecated {vegan} | R Documentation

## Deprecated Functions in vegan package

### Description

These functions are provided for compatibility with older versions of vegan only, and may be defunct as soon as the next release.

### Usage

    commsimulator(x, method, thin=1)

### Arguments

`x` | Community data.
`method` | Null model method: either a name (character string) of a method defined in `make.commsim` or a `commsim` function.
`thin` | Number of discarded null communities between two evaluations of nestedness statistic in sequential methods `"swap"` and `"tswap"` (ignored with non-sequential methods)

## density and densityplot

`...` | Other arguments passed to functions.

### Details

Function `commsimulator` is replaced with `make.commsim` which defines the Null models, and functions `nullmodel` and `simulate.nullmodel` that check the input data and generate the Null model communities. Function `commsimulator` was used to generate a single Null model for presence/absence (binary) data. Below is a copy of its original documentation in `oecosimu`, where it is now replaced with `make.commsim`, `nullmodel` and `simulate.nullmodel`. Approximately the same documentation for these models is found in `make.commsim`. (However, the random number sequences for model `r0` differ, and you must use `method = "r0_old"` in `make.commsim` to reproduce the `commsimulator` results.)

Function `commsimulator` implements binary (presence/absence) null models for community composition. The implemented models are `r00` which maintains the number of presences but fills these anywhere so that neither species (column) nor site (row) totals are preserved. Methods `r0`, `r1` and `r2` maintain the site (row) frequencies. Method `r0` fills presences anywhere on the row with no respect to species (column) frequencies, `r1` uses column marginal frequencies as probabilities, and `r2` uses squared column sums. Methods `r1` and `r2` try to simulate original species frequencies, but they are not strictly constrained. All these methods are reviewed by Wright et al. (1998). Method `c0` maintains species frequencies, but does not honour site (row) frequencies (Jonsson 2001).

The other methods maintain both row and column frequencies. Methods `swap` and `tswap` implement sequential methods, where the matrix is changed only little in one step, but the changed matrix is used as an input if the next step. Methods `swap` and `tswap` inspect random 2x2 submatrices and if they are checkerboard units, the order of columns is swapped. This changes the matrix structure, but does not influence marginal sums (Gotelli & Entsminger 2003). Method `swap` inspects submatrices so long that a swap can be done. Miklós & Podani (2004) suggest that this may lead into biased sequences, since some columns or rows may be more easily swapped, and they suggest trying a fixed number of times and doing zero to many swaps at one step. This method is implemented by method `tswap` or trial swap. Function `commsimulator` makes only one trial swap in time (which probably does nothing), but `oecosimu` estimates how many submatrices are expected before finding a swappable checkerboard, and uses that ratio to thin the results, so that on average one swap will be found per step of `tswap`. However, the checkerboard frequency probably changes during swaps, but this is not taken into account in estimating the `thin`. One swap still changes the matrix only little, and it may be useful to thin the results so that the statistic is only evaluated after `burnin` steps (and `thin`ned).

Methods `quasiswap` and `backtracking` are not sequential, but each call produces a matrix that is independent of previous matrices, and has the same marginal totals as the original data. The recommended method is `quasiswap` which is much faster because it is implemented in C. Method `backtracking` is provided for comparison, but it is so slow that it may be dropped from future releases of vegan (or also implemented in C). Method `quasiswap` (Miklós & Podani 2004) implements a method where matrix is first filled honouring row and column totals, but with integers that may be larger than one. Then the method inspects random 2x2 matrices and performs a quasiswap on them. Quasiswap is similar to ordinary swap, but it also can reduce numbers above one to ones maintaining marginal totals. Method `backtracking` implements a filling method with constraints both for row and column frequencies (Gotelli & Entsminger 2001). The matrix is first filled randomly using row and column frequencies as probabilities. Typically row and column sums are reached before all incidences are filled in. After that begins “backtracking”, where some of the points are removed, and then filling is started again, and this backtracking is done so may times that all incidences will be filled into matrix. The `quasiswap` method is not sequential, but it produces a random incidence matrix with given marginal totals.

### References

Gotelli, N.J. & Entsminger, N.J. (2001). Swap and fill algorithms in null model analysis: rethinking the knight's tour. Oecologia 129, 281–291.

Gotelli, N.J. & Entsminger, N.J. (2003). Swap algorithms in null model analysis. Ecology 84, 532–535.

Jonsson, B.G. (2001) A null model for randomization tests of nestedness in species assemblages. Oecologia 127, 309–313.

Miklós, I. & Podani, J. (2004). Randomization of presence-absence matrices: comments and new algorithms. Ecology 85, 86–92.

Wright, D.H., Patterson, B.D., Mikkelson, G.M., Cutler, A. & Atmar, W. (1998). A comparative analysis of nested subset patterns of species composition. Oecologia 113, 1–20.

### See Also

`Deprecated`

[Package vegan version 2.4-3 Index]