Deprecated Functions in vegan package
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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Community data for
Null model method: either a name (character string) of
a method defined in
Number of discarded null communities between two
evaluations of nestedness statistic in sequential methods
## density and densityplot
Draw vertical line for the observed
statistic. Logical value
Other arguments passed to functions.
commsimulator is replaced with
make.commsim which defines the Null models, and
simulate.nullmodel that check the input data and
generate the Null model communities. Function
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
simulate.nullmodel. Approximately the same
documentation for these models is found in
make.commsim. (However, the random number sequences
r0 differ, and you must use
method = "r0_old"
make.commsim to reproduce the
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
r2 maintain the site (row) frequencies. Method
fills presences anywhere on the row with no respect to species (column)
r1 uses column marginal
frequencies as probabilities, and
r2 uses squared column
r2 try to simulate original species
frequencies, but they are not strictly constrained. All these methods
are reviewed by Wright et al. (1998). Method
species frequencies, but does not honour site (row) frequencies (Jonsson
The other methods maintain both row and column frequencies.
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.
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
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
tswap or trial swap. Function
only one trial swap in time (which probably does nothing),
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
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).
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
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.
density function can directly access permutation results
of the same function as
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
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 methods are
replaced with similar methods for
permustats offers more powerful analysis tools for
z values (a.k.a. standardized effect sizes, SES), and Q-Q
qqmath.permustats. Below the old documentation:
The density methods are available for vegan functions
density function for
oecosimu is documented
separately, and it is also used for
density functions return an object of class
"vegandensity" inheriting from
density, and can
be plotted with its
plot method. This is identical to the
densiy objects, but can also add a
vertical line for the observed statistic.
Functions that can return several permuted statistics simultaneously
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
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.
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