Nothing

```
### Clarke's dispweight is based on the hypothesis that count data
### should follow Poisson distribution, and species overdispersed to
### the Poisson should be downweighted. The basic model assesses the
### expected values of species and their overdispersion wrt to class
### means for a single factor and then estimates the significance of
### the overdispersion using individual-based simulation within these
### same classes. Function gdispweight generalizes this by allowing a
### formula that specifies any fitted model, but estimates the
### significance of the overdispersion analytically from Pearson
### residuals.
`gdispweight` <-
function(formula, data, plimit = 0.05)
{
## We do not handle missing values (yet?)
op <- options(na.action = "na.fail")
on.exit(op)
## extract response data
comm <- eval(formula[[2]])
## extract rhs
if (missing(data))
data <- environment(formula)
x <- model.matrix(delete.response(terms(formula, data = data)),
data = data)
## Quasi-Poisson
family <- quasipoisson()
V <- family$variance
## fit models to all species separately and extract results
comm <- as.data.frame(comm)
mods <- lapply(comm, function(y) glm.fit(x, y, family = family))
y <- sapply(mods, '[[', "y")
mu <- sapply(mods, fitted)
wts <- sapply(mods, '[[', "prior.weights")
res <- (y-mu) * sqrt(wts) / sqrt(V(mu))
df <- sapply(mods, df.residual)
## the same stats as in Clarke's original, but parametrically
stat <- colSums(res^2)
p <- pchisq(stat, df, lower.tail = FALSE)
dhat <- stat/df
w <- ifelse(p < plimit, 1/dhat, 1)
## do not upweight underdispersed species
w <- ifelse(w > 1, 1, w)
## done
comm <- sweep(comm, 2, w, "*")
class(comm) <- c("dispweight", class(comm))
attr(comm, "D") <- dhat
attr(comm, "df") <- df
attr(comm, "p") <- p
attr(comm, "weights") <- w
attr(comm, "nsimul") <- NA
attr(comm, "nullmodel") <- NA
comm
}
```

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