## self-starting logistic model
SSlogis <- selfStart(~ Asym/(1 + exp((xmid - x)/scal)),
function(mCall, data, LHS)
{
xy <- sortedXyData(mCall[["x"]], LHS, data)
if(nrow(xy) < 4) {
stop("Too few distinct x values to fit a logistic")
}
z <- xy[["y"]]
if (min(z) <= 0) { z <- z + 0.05 * max(z) } # avoid zeroes
z <- z/(1.05 * max(z)) # scale to within unit height
xy[["z"]] <- log(z/(1 - z)) # logit transformation
aux <- coef(lm(x ~ z, xy))
parameters(xy) <- list(xmid = aux[1], scal = aux[2])
pars <- as.vector(coef(nls(y ~ 1/(1 + exp((xmid - x)/scal)),
data = xy, algorithm = "plinear")))
setNames(c(pars[3], pars[1], pars[2]),
mCall[c("Asym", "xmid", "scal")])
}, c("Asym", "xmid", "scal"))
# 'first.order.log.model' is a function object defining a first order
# compartment model
# 'first.order.log.initial' is a function object which calculates initial
# values for the parameters in 'first.order.log.model'
# self-starting first order compartment model
SSfol <- selfStart(first.order.log.model, first.order.log.initial)
## Explore the self-starting models already available in R's "stats":
pos.st <- which("package:stats" == search())
mSS <- apropos("^SS..", where = TRUE, ignore.case = FALSE)
(mSS <- unname(mSS[names(mSS) == pos.st]))
fSS <- sapply(mSS, get, pos = pos.st, mode = "function")
all(sapply(fSS, inherits, "selfStart")) # -> TRUE
## Show the argument list of each self-starting function:
str(fSS, give.attr = FALSE)
Run the code above in your browser using DataLab