oecosimu {vegan} | R Documentation |
Null models generate random communities with different criteria to study the significance of nestedness or other community patterns. The function only simulates binary (presence/absence) models with constraint for total number of presences, and optionally for numbers of species and/or species frequencies.
oecosimu(comm, nestfun, method, nsimul = 99, burnin = 0, thin = 1, statistic = "statistic", ...) commsimulator(x, method, thin=1)
comm, x |
Community data. |
nestfun |
Function to analyse nestedness. Some functions are
provided in vegan, but any function can be used if it accepts the
community as the first argument, and returns either a plain number or
the result in list item with the name defined in argument
statistic . See Examples for defining your own functions. |
method |
Null model method. See details. |
nsimul |
Number of simulated null communities. |
burnin |
Number of null communities discarded before proper
analysis in sequential methods "swap" and "tswap" . |
thin |
Number of discarded null communities between two
evaluations of nestedness statistic in sequential methods
"swap" and "tswap" . |
statistic |
The name of the statistic returned by
nestedfun |
... |
Other arguments to functions. |
Function oecosimu
is a wrapper that evaluates a nestedness
statistic using function given by nestfun
, and then simulates a
series of null models using commsimulator
, and evaluates the
statistic on these null models. The vegan packages contains some
nestedness functions that are described separately
(nestedchecker
, nesteddisc
,
nestedn0
, nestedtemp
), but many other
functions can be used as long as they are meaningful with binary
community models. An applicable function must return either the
statistic as a plain number, or as a list element "statistic"
(like chisq.test
), or in an item whose name is given in
the argument statistic
. The statistic can be a single number
(like typical for a nestedness index), or it can be a vector. The
vector indices can be used to analyse site (row) or species (column)
properties, see treedive
for an example.
Function commsimulator
implements 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 bactkracking
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.
Function oecosimu
returns the result of nestfun
with one added component called oecosimu
. The oecosimu
component contains the simulated values of the statistic (item
simulated
), the name of the method
, two-sided P
value and z-value of the statistic based on simulation. The
commsimulator
returns a null model matrix or a swap of the
input matrix.
Functions commsimulator
and oecosimu
do not have
default nestfun
nor default method
, because there is
no clear natural choice. If you use these methods, you must be able
to choose your own strategy. The choice of nestedness index is
difficult because the functions seem to imply very different
concepts of structure and randomness. The choice of swapping method
is also problematic. Method r00
has some heuristic value of
being really random. However, it produces null models which are
different from observed communities in most respects, and a
“significant” result may simply mean that not all species are
equally common (r0
is similar with this respect). It is also
difficult to find justification for r2
. The methods
maintaining both row and column totals only study the community
relations, but they can be very slow. Moreover, they regard marginal
totals as constraints instead of results of occurrence patterns. You
should evaluate timings in small trials (one cycle) before launching
an extensive simulation. One swap is fast, but it changes data only
little, and you may need long burnin
and strong
thin
ning in large matrices. You should plot the simulated
values to see that they are more or less stationary and there is no
trend. Method quasiswap
is implemented
in C and it is much faster than backtrack
. Method
backtrack
may be removed from later releases of vegan
because it is slow, but it is still included for comparison.
If you wonder about the name of oecosimu
, look at journal
names in the References (and more in nestedtemp
).
Jari Oksanen
Gotelli, N.J. & Entsminger, N.J. (2001). Swap and fill algorithms in null model analyis: 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.
r2dtable
generates table with given marginals but
with entries above one. Functions permatfull
and
permatswap
generate Null models for count data.
Function rndtaxa
(labdsv package) randomizes a community table. See also
nestedtemp
(that also discusses other nestedness
functions) and treedive
for another application.
## Use the first eigenvalue of correspondence analysis as an index ## of structure: a model for making your own functions. data(sipoo) caeval <- function(x) decorana(x, ira=1)$evals out <- oecosimu(sipoo, caeval, "swap", burnin=100, thin=10) out ## Inspect the swap sequence matplot(t(out$oecosimu$simulated), type="l")