Thanks!
I tried already. Still the same error message...
wishes
Gunther
----- Original Message -----
From: "ONKELINX, Thierry" <[EMAIL PROTECTED]>
To: "Gunther Höning" <[EMAIL PROTECTED]>; <[email protected]>
Sent: Tuesday, July 01, 2008 3:08 PM
Subject: RE: [R] Regression and fitting
Try to set your own starting values. Use the values that you would expect
for S0, mu and sigma.
HTH,
Thierry
----------------------------------------------------------------------------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
[EMAIL PROTECTED]
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Namens Gunther Höning
Verzonden: dinsdag 1 juli 2008 14:41
Aan: [email protected]
Onderwerp: [R] Regression and fitting
Dear list,
I have some values like
Time 2 4 8 24 48 72
UTR 82543 169105 207615 96633 31988 7005
UTRs 82687 172934 205541 101842 31898 6950
of a twice repeated meassurement.
I know that the underlying function is of :
f(x) = SO/(sqrt(2*pi)*sigma *x) * exp(-(S0*ln x - mu)^2/(2*sigma^2)).
How can I determine the value of S0, sigma and mu.
I tried nls:
lognormal <- function(x,S0,sigma,mu) {S0/(sqrt(2*pi)*sigma *x) *
exp(-(S0*log (x) - mu)^2/(2*sigma^2))}
x <- rep(c(2,4,6,24,48,72),2)
y <-
c(82543,169105,207615,96633,31988,7005,82687,172934,205541,101842,31898,6950
)
dat <- data.frame(x,y)
regfit <- nls(y ~ lognormal(x,S0,sigma,mu) ,data =dat)
Output:
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
Warning message:
No starting values specified for some parameters.
Intializing 'S0', 'sigma', 'mu' to '1.'.
Consider specifying 'start' or using a selfStart model in: nls(y ~
lognormal(x, S0, sigma, mu), data = dat)
Can anybody help on this topic please ?
Gunther
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and provide commented, minimal, self-contained, reproducible code.
