Description Usage Arguments Details Value Author(s) References Examples
Transform abundance data downweighting species that are overdispersed to the Poisson error.
1 2 3 4 5  dispweight(comm, groups, nsimul = 999, nullmodel = "c0_ind",
plimit = 0.05)
gdispweight(formula, data, plimit = 0.05)
## S3 method for class 'dispweight'
summary(object, ...)

comm 
Community data matrix. 
groups 
Factor describing the group structure. If missing, all
sites are regarded as belonging to one group. 
nsimul 
Number of simulations. 
nullmodel 
The 
plimit 
Downweight species if their pvalue is at or below this limit. 
formula, data 
Formula where the lefthand side is the
community data frame and righthand side gives the explanatory
variables. The explanatory variables are found in the data frame
given in 
object 
Result object from 
... 
Other parameters passed to functions. 
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 Chisquare), 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 Chisquare 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.
Function returns transformed data with the following new attributes:
D 
Dispersion statistic. 
df 
Degrees of freedom for each species. 
p 
pvalue of the Dispersion statistic D. 
weights 
weights applied to community data. 
nsimul 
Number of simulations used to assess the pvalue,
or 
nullmodel 
The name of 
Eduard Szöcs eduardszoesc@gmail.com wrote the original
dispweight
, Jari Oksanen significantly modified the code,
provided support functions and developed gdispweight
.
Clarke, K. R., M. G. Chapman, P. J. Somerfield, and H. R. Needham. 2006. Dispersionbased weighting of species counts in assemblage analyses. Marine Ecology Progress Series, 320, 11–27.
1 2 3 4 5 6 7 
Loading required package: permute
Loading required package: lattice
This is vegan 2.54
Dispersion Weight Df Pr(Disp.)
Brachy 9.6908 0.1031909 67 0.01 **
PHTH 3.2809 0.3047900 49 0.01 **
HPAV 6.5263 0.1532264 67 0.01 **
RARD 6.0477 0.1653525 49 0.01 **
SSTR 2.2619 0.4421053 49 0.01 **
Protopl 5.4229 0.1844031 49 0.01 **
MEGR 4.5354 0.2204860 67 0.01 **
MPRO 1.2687 1.0000000 67 0.07 .
TVIE 2.5956 0.3852706 67 0.01 **
HMIN 10.0714 0.0992906 67 0.01 **
HMIN2 7.5674 0.1321466 49 0.01 **
NPRA 2.6743 0.3739344 67 0.01 **
TVEL 9.6295 0.1038474 49 0.01 **
ONOV 11.3628 0.0880064 67 0.01 **
SUCT 8.7372 0.1144533 67 0.01 **
LCIL 129.4436 0.0077254 67 0.01 **
Oribatl1 4.1250 0.2424248 67 0.01 **
Ceratoz1 1.7150 0.5830768 67 0.01 **
PWIL 2.2943 0.4358538 67 0.01 **
Galumna1 2.8777 0.3474943 49 0.01 **
Stgncrs2 3.8242 0.2614953 49 0.01 **
HRUF 1.7575 0.5690021 67 0.01 **
Trhypch1 14.9225 0.0670128 67 0.01 **
PPEL 1.3628 1.0000000 49 0.06 .
NCOR 2.5875 0.3864771 67 0.01 **
SLAT 2.7857 0.3589744 49 0.01 **
FSET 4.8901 0.2044944 49 0.01 **
Lepidzts 1.6577 0.6032360 49 0.02 *
Eupelops 1.4611 0.6844033 67 0.02 *
Miniglmn 1.6505 0.6058733 49 0.01 **
LRUG 12.0658 0.0828790 67 0.01 **
PLAG2 3.2403 0.3086090 67 0.01 **
Ceratoz3 3.5125 0.2846947 67 0.01 **
Oppiminu 3.1680 0.3156525 67 0.01 **
Trimalc2 10.5927 0.0944046 67 0.01 **

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Based on 99 simulations on 'c0_ind' nullmodel
Call: rda(formula = mite.dw ~ Shrub + WatrCont, data = mite.env)
Inertia Proportion Rank
Total 38.1640 1.0000
Constrained 9.2129 0.2414 3
Unconstrained 28.9511 0.7586 35
Inertia is variance
Eigenvalues for constrained axes:
RDA1 RDA2 RDA3
7.986 0.748 0.480
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
5.886 3.634 2.791 2.592 1.932 1.573 1.210 1.078
(Showing 8 of 35 unconstrained eigenvalues)
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