Hi Benoit, another way of making Petr's point is by looking at the profile log likelihood function for b; that is, only estimating the a parameter for a grid of b values:
## Defining mean function for fixed b lgma <- function(b){ function(C0, m, V, a){ (V + b * m * a + C0 * V * b - ((C0 * V * b)^2 + 2 * C0 * b * V^2 - 2 * C0 * V * m * a * b^2 + V^2 + 2 * V * m * a * b + (b * m * a)^2)^(1/2))/(2 * b * m)} } ## Defining profile log likelihood function logLikb <- function(b) { logLik(nls(Qe~(lgma(b))(C0, m, V, a),data = bois.DATA,start = list(a=300))) } logLikb2 <- Vectorize(logLikb, "b") # vectorising the function ## Plotting the profile function plot(x<-10^(seq(-4, 0, length.out=50)), logLikb2(x), type="l") Essentially any b value from 0.2 upwards results in the same model fit. Christian ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.