Nothing
"gaussgradient" <-
function (x, grad, family = poisson, xlab, freq.lim = 5, cex.species = 0.7,
axes = TRUE, ...)
{
x <- as.data.frame(x)
x <- x[, colSums(x>0) >= freq.lim]
mod <- lapply(x, function(x) glm(x ~ grad + I(grad^2), family=family(link=log)))
opt <- sapply(mod, function(x) if((p <- coef(x))[3] < 0) -p[2]/2/p[3] else NA)
tol <- sapply(mod, function(x) if((p <- coef(x))[3] < 0) sqrt(-1/2/p[3]) else NA)
narrow <- min(tol, na.rm=TRUE)
ord <- order(opt, na.last = NA)
x <- x[,ord]
if (missing(xlab))
xlab <- deparse(substitute(grad))
ylab <- names(x)
plot(range(grad), c(1,length(ord)+1), xlab = xlab, ylab="", axes = FALSE, type="n")
if (axes) {
box()
axis(1)
axis(2, at=1:length(ord), ylab, las=1, cex.axis = cex.species)
}
xv <- seq(min(grad), max(grad), len=101)
for (i in 1:length(ord)) {
fv <- predict(mod[[ord[i]]], new=list(grad=xv), type="response")
top <- predict(mod[[ord[i]]], new=list(grad=opt[ord[i]]),type="response")
fv <- 0.95*fv/top*narrow/tol[ord[i]] + i
fv <- c(i, fv, i)
polygon(c(xv[1], xv, xv[length(xv)]), fv, ...)
}
invisible(list(opt=opt, tol=tol))
}
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