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Hello, I would like to use modFit and modCost from the package FME to find the optimal initial time t0 of a process. For simplicity, the process is either "off" (value 0) or "on" (value h). So I have a data vector with some zeros followed by some h's, e.g. > c(0,0,0,2,2,2,2,2,2,2) [1] 0 0 0 2 2 2 2 2 2 2 (hence h=...

...g FME. I have several spatially separate plots with time series growth data for each(eg, height, basal area, etc.). Thus 2 independent variables: time and plot. Is there a way that I can calculate a model cost that takes into account these different plots? I notice in the documentation for the modCost function that there is a note stating that using more than one independent variable should be possible in the future, and was wondering if anyone has managed this? Or if there is a way I can achieve this, it would be interesting to know how. Thanks, Jack -- The University of Edinburgh is a...

Dear R users, I'm trying to fit a set an ODE to an experimental time series. In the attachment you find the R code I wrote using modFit and modCost of FME package and the file of the time series. When I run summary(Fit) I obtain this error message, and the values of the parameters are equal to the initial guesses I gave to them. The problem is not due to the fact that I have only one equation (I tried also with more equations, but I still o...

...mailing lis. Again, please receive my sincere apologies for this. I re-send again the e-mail with .txt attachemnt in the hope someone an help me to solve my problem. I'm trying to fit a set an ODE to an experimental time series. In the attachment you find the R code I wrote using modFit and modCost of FME package and the file of the time series. When I run summary(Fit) I obtain this error message, and the values of the parameters are equal to the initial guesses I gave to them. The problem is not due to the fact that I have only one equation (I tried also with more equations, but I still o...

...,19.7,3,17.4,5.5,15.3,8,13.5,11.5,11.3,13,29.8,15.5,26.3,16.5,25,18,23.2,24,17.2,30,12.7,40,7.7,49.5,4.8)) colnames(observed) <- c("time", "y") obj <- function(x, parset = names(x)) {  pars[parset] <- x  tout <- seq(0, 50, by = 1)  out <- model(pars, tout)  return(modCost(obs = observed, model = out))  } prior <- function(p)   return( sum(((p-c(0.06,18))/c(0.04,6))^2 )) final <- modMCMC(p = c(k=0.06,v=18), f = obj, prior=prior,lower=c(0.0001,0.1),jump = NULL, niter = 1000,updatecov=100, wvar0 = 1,var0=NULL,burninlength = 50) summary(final) plot(final) the val...