Nothing
"humpfit" <-
function (mass, spno, family = poisson, start)
{
hump <- function(p, mass, spno, ...) {
x <- ifelse(mass < p[1], mass/p[1], p[1] * p[1]/mass/mass)
fv <- p[3] * log(1 + p[2] * x/p[3])
n <- wt <- rep(1, length(x))
dev <- sum(dev.resids(spno, fv, wt))
aicfun(spno, n, fv, wt, dev)/2
}
fam <- family(link = "identity")
aicfun <- fam$aic
dev.resids <- fam$dev.resids
if (missing(start))
p <- c(mean(mass), 100, 10)
else
p <- start
fit <- nlm(hump, p = p, mass = mass, spno = spno, hessian = TRUE)
p <- fit$estimate
names(p) <- c("hump", "scale", "alpha")
x <- ifelse(mass < p[1], mass/p[1], p[1] * p[1]/mass/mass)
fv <- p[3] * log(1 + p[2] * x/p[3])
res <- dev.resids(spno, fv, rep(1, length(x)))
dev <- sum(res)
residuals <- spno - fv
aic <- fit$minimum * 2 + 6
rdf <- length(x) - 3
out <- list(nlm = fit, family = fam, y = spno, x = mass,
coefficients = p, fitted.values = fv, aic = aic, rank = 3,
df.residual = rdf, deviance = dev, residuals = residuals,
prior.weights = rep(1, length(x)))
class(out) <- c("humpfit", "glm")
out
}
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