[R] MASS Book Exercise

[Ko-Kang Kevin Wang]

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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