These functions are provided for compatibility with older versions of vegan only, and may be defunct as soon as the next release.
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x 
Community data for 
## commsimulator
method 
Null model method: either a name (character string) of
a method defined in 
thin 
Number of discarded null communities between two
evaluations of nestedness statistic in sequential methods

## density and densityplot
main, xlab, ylab, type, zero.line 
Arguments of

obs.line 
Draw vertical line for the observed
statistic. Logical value 
data 
Ignored. 
... 
Other arguments passed to functions. 
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.
The density
function can directly access permutation results
of the same function as permustats
. The density
function is identical to density.default
and takes all
its arguments, but adds the observed statistic to the result as item
"observed"
. The observed statistic is also put among the
permuted values so that the results are consistent with significance
tests. The plot
method is similar to the default
plot.density
, but can also add the observed statistic
to the graph as a vertical line. In adonis
it is also
possible to use direclty densityplot
function.
The deprecated density
and densityplot
methods are
replaced with similar methods for permustats
. The
permustats
offers more powerful analysis tools for
permutations, including summary.permustats
giving
z values (a.k.a. standardized effect sizes, SES), and QQ
plots (qqnorm.permustats
,
qqmath.permustats
. Below the old documentation:
The density methods are available for vegan functions
adonis
, anosim
, mantel
,
mantel.partial
, mrpp
,
permutest.cca
, and protest
. The
density
function for oecosimu
is documented
separately, and it is also used for adipart
,
hiersimu
and multipart
.
All vegan density
functions return an object of class
"vegandensity"
inheriting from density
, and can
be plotted with its plot
method. This is identical to the
standard plot
of densiy
objects, but can also add a
vertical line for the observed statistic.
Functions that can return several permuted statistics simultaneously
also have densityplot
method
(adonis
, oecosimu
and diversity
partitioning functions based on oecosimu
). The standard
density
can only handle univariate data, and a warning
is issued if the function is used for a model with several observed
statistics
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 presenceabsence 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.
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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