Transform abundance data downweighting species that are overdispersed to the Poisson error.
dispweight(comm, groups, nsimul = 999, nullmodel = "c0_ind",
plimit = 0.05)
gdispweight(formula, data, plimit = 0.05)
# S3 method for dispweight
summary(object, ...)Function returns transformed data with the following new attributes:
Dispersion statistic.
Degrees of freedom for each species.
\(p\)-value of the Dispersion statistic \(D\).
weights applied to community data.
Number of simulations used to assess the \(p\)-value,
or NA when simulations were not performed.
The name of commsim null model, or
NA when simulations were not performed.
Community data matrix.
Factor describing the group structure. If missing, all
sites are regarded as belonging to one group. NA values are
not allowed.
Number of simulations.
The nullmodel used in
commsim within groups. The default
follows Clarke et al. (2006).
Downweight species if their \(p\)-value is at or below this limit.
Formula where the left-hand side is the
community data frame and right-hand side gives the explanatory
variables. The explanatory variables are found in the data frame
given in data or in the parent frame.
Result object from dispweight or
gdispweight.
Other parameters passed to functions.
Eduard Szöcs eduardszoesc@gmail.com wrote the original
dispweight, Jari Oksanen significantly modified the code,
provided support functions and developed gdispweight.
The dispersion index (\(D\)) is calculated as ratio between variance and expected value for each species. If the species abundances follow Poisson distribution, expected dispersion is \(E(D) = 1\), and if \(D > 1\), the species is overdispersed. The inverse \(1/D\) can be used to downweight species abundances. Species are only downweighted when overdispersion is judged to be statistically significant (Clarke et al. 2006).
Function dispweight implements the original procedure of Clarke
et al. (2006). Only one factor can be used to group the sites and to
find the species means. The significance of overdispersion is assessed
freely distributing individuals of each species within factor
levels. This is achieved by using nullmodel
"c0_ind" (which accords to Clarke et al. 2006), but other
nullmodels can be used, though they may not be meaningful (see
commsim for alternatives). If a species is absent in
some factor level, the whole level is ignored in calculation of
overdispersion, and the number of degrees of freedom can vary among
species. The reduced number of degrees of freedom is used as a divisor
for overdispersion \(D\), and such species have higher dispersion
and hence lower weights in transformation.
Function gdispweight is a generalized parametric version of
dispweight. The function is based on glm with
quasipoisson error family. Any
glm model can be used, including several factors or
continuous covariates. Function gdispweight uses the same test
statistic as dispweight (Pearson Chi-square), but it does not
ignore factor levels where species is absent, and the number of
degrees of freedom is equal for all species. Therefore transformation
weights can be higher than in dispweight. The
gdispweight function evaluates the significance of
overdispersion parametrically from Chi-square distribution
(pchisq).
Functions dispweight and gdispweight transform data, but
they add information on overdispersion and weights as attributes of
the result. The summary can be used to extract and print that
information.
Clarke, K. R., M. G. Chapman, P. J. Somerfield, and H. R. Needham. 2006. Dispersion-based weighting of species counts in assemblage analyses. Marine Ecology Progress Series, 320, 11–27.
data(mite, mite.env)
## dispweight and its summary
mite.dw <- with(mite.env, dispweight(mite, Shrub, nsimul = 99))
## IGNORE_RDIFF_BEGIN
summary(mite.dw)
## IGNORE_RDIFF_END
## generalized dispersion weighting
mite.dw <- gdispweight(mite ~ Shrub + WatrCont, data = mite.env)
rda(mite.dw ~ Shrub + WatrCont, data = mite.env)
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