Hi,
In the third edition of the MASS book, Chapter 8, Page 252 ~ 253.
I tried applying the same technique on using a negative exponential model
(as suggested in Exercise 1), and trying to get a confidence interval.
What I have changed is follows:
expn <- function(b0, b1, th, x) {
temp <- exp(-x/th)
model.func <- b0 + b1 * temp
# Differentiate temp
derive.negexp <- (x * temp) / th^2
Z <- cbind(1, temp, derive.negexp)
dimnames(Z) <- list(NULL,
c("b0","b1","th"))
attr(model.func, "gradient") <- Z
model.func
}
wtloss.gr <- nls(Weight ~ expn(b0, b1, th, Days),
data = wtloss, start = wtloss.st, trace = F)
expn2 <- deriv(~b0 + b1*((w0 - b0)/b1)^(x/d0),
c("b0","b1","d0"), function(b0, b1,
d0, x, w0) {})
wtloss.init <- function(obj, w0) {
p <- coef(obj)
# Modified d0 for neg exp model
d0 <- - log((w0 - p["b0"])/p["b1"]) *
p["th"]
c(p[c("b0", "b1")], d0 = as.vector(d0))
}
However when running the above codes in R, it stopped at wtloss.gr with
the error:
Error in nls(Weight ~ expn(b0, b1, th, Days), data = wtloss, start wtloss.st, :
step factor 0.000488281 reduced below `minFactor' of 0.000976562
Execution halted
Any hints on where I did wrong would be greatly appreciated,
Ko-Kang Wang
------------------------------------------------------------------------------
Ko-Kang Kevin Wang
Postgraduate PGDipSci Student
Department of Statistics
University of Auckland
New Zealand
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