Problem with logarithmic nonlinear model using nls() from the `stats' package
On Sat, Oct 1, 2011 at 5:28 AM, Casper Ti. Vector
<caspervector at gmail.com> wrote:
Example:
f <- function(x) { 1 + 2 * log(1 + 3 * x) + rnorm(1, sd = 0.5) }
y <- f(x <- c(1 : 10)); y
?[1] 4.503841 5.623073 6.336423 6.861151 7.276430 7.620131 7.913338 8.169004 ?[9] 8.395662 8.599227
nls(x ~ a + b * log(1 + c * x), start = list(a = 1, b = 2, c = 3), trace = TRUE)
37.22954 : ?1 2 3 Error in numericDeriv(form[[3L]], names(ind), env) : ?Missing value or an infinity produced when evaluating the model In addition: Warning message: In log(1 + c * x) : NaNs produced What's wrong here? Am I handling this problem in the wrong way? Any suggestions are welcome, thanks :)
Its linear given c so calculate the residual sum of squares using lm (or lm.fit which is faster) given c and optimize over c: set.seed(123) # for reproducibility # test data x <- 1:10 y <- 1 + 2 * log(1 + 3 * x) + rnorm(1, sd = 0.5) # calculate residual sum of squares for best fit given c fitc <- function(c) lm.fit(cbind(1, log(1 + c * x)), y) rssvals <- function(c) sum(resid(fitc(c))^2) out <- optimize(rssvals, c(0.01, 10)) which gives:
setNames(c(coef(fitc(out$minimum)), out$minimum), letters[1:3])
a b c 0.7197666 2.0000007 2.9999899
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