dispweight(comm, groups, nsimul = 999, nullmodel = "c0_ind", plimit = 0.05)
gdispweight(formula, data, plimit = 0.05)
"summary"(object, ...)NA values are
not allowed.data or in the parent frame.dispweight or
gdispweight.NA when simulations were not performed.commsim null model, or
NA when simulations were not performed.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.
data(mite, mite.env)
## dispweight and its summary
mite.dw <- with(mite.env, dispweight(mite, Shrub, nsimul = 99))
summary(mite.dw)
## generalized dispersion weighting
mite.dw <- gdispweight(mite ~ Shrub + WatrCont, data = mite.env)
rda(mite.dw ~ Shrub + WatrCont, data = mite.env)
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