We would like to fit parameters using a simulation with stochastic processes as theoretical values. We generate a simple exemple with nls.lm to see the logic and the problem:
First without stochasticity (it is a dummy example, the fited value is simple the mean of a set of 10 numbers): #Ten numbers x <- 1:10 #Generate 10 Gaussian random number with mean=3 sd=1 simy <- rnorm(length(x), mean=3, sd=1) #theoretical value for each of the 10 numbers=the adjust value y1 <- function(pp,xx) {rep(pp$a, length(xx))} #Residual resid <- function(pp,observed,xx) {observed-y1(pp,xx)} #Starting parameter pStart <- list(a=0.1) #non-linear fit library(minpack.lm) nls.lm.test <- nls.lm(par=pStart, fn=resid, observed=simy, xx=x, control=nls.lm.control(nprint=1)) It works fine: It. 0, RSS = 86.2811, Par. = 0.1 It. 1, RSS = 5.69735, Par. = 2.93873 It. 2, RSS = 5.69735, Par. = 2.93873 Now let the function generating the theoretical values returns also a little bit noise, as observed from the output of a simulation with stochasticity: y1 <- function(pp,xx) {rep(pp$a, length(xx))+rnorm(length(xx), mean=0, sd=0.01)} Then the fit failed: It. 0, RSS = 86.1011, Par. = 0.1 It. 1, RSS = 86.4468, Par. = 0.1 Similar problem is observed for nls Has someone a solution ? Thanks a lot [we use previously Profit software (macosx software) for such a fit and it works there. But r is more portable and we will prefer to use it. Thanks] ______________________________________________ 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.