similar to: Fit initial time with modFit and modCost

* Apologies for multiple posting * I attached to my previous e-mail a .r file, and it was not permitted by the rules of the 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

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

I am attempting to calibrate a forest growth model using 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

Dear all, I used modFit of the package FME to fit a set of ODE to a ste of eperiemntal data. The summary of this fit give me the following error > summary(Fit) Residual standard error: 984.1 on 452 degrees of freedom Error in cov2cor(x$cov.unscaled) : 'V' is not a square numeric matrix In addition: Warning message: In summary.modFit(Fit) : Cannot estimate covariance; system is

Dear All,   please help with some thoughts on overcoming the following issues, if possible:   #R Code   require(deSolve) require(FME) pars <- list(k = 0.06,v=18) intimes <- c(0,0.5,12,12.5,50) input   <- c(800,0,800,0,0) forc <- approxfun(intimes, input, method="constant") model <- function(pars, times=seq(0, 50, by = 1)) {   derivs <- function(t, state, pars) {     

  Hi, All,   This is the first time I seriously use this package. However, I am confused that the result is quite unstable. Maybe I wrote something wrong in the code? So could anybody give me some hint? Many thanks.   My test model is really simple. Y_t = X_t * a_t + noise(V)，(no Intercept here) a_t = a_{t-1} + noise(W)   I first run the following code: (I shall provide data at the end of the

Hi, all I can not figure this out, please have a look and help me out. thank you! Note: this is in SPLUS, not R. I have following code *********************************** modfit<-function(yir,yew, ft) { n<-length(yew) yew<-yew[1:(n-1)] yy<-yir-ft xx<-yew-ft n<-length(xx) xx0<-xx[2:n] yy0 <-yy [2:n] xx1<-xx[1:(n-1)] fit <- garch(yy0~xx0 + xx1+var.in.mean,

Howdy, Last week I got some great help on why I was getting an error code when trying to run this model, thanks everyone!  I was able to get the code up and running beautifully for several data sets.  Now I am getting different errors with this new data set.  I can't figure out why, I have more data points with this species, and it is ordered exactly the same as the other species I have been

Questions about the port of Xen to ARM with virt extensions are best posted to xen-devel, the xen-arm list focuses on the PV port. Adding xen-devel since it seems you are mainly asking about the w/-virt-extensions port. On Fri, 2012-05-18 at 12:38 +0100, Krishna Pavan wrote: > Please inform the status of Xen-ARM for Cortex-A8 CPU''s on FME from > ARM. AFAIK no one has tried the

I'm trying to use nls() to fit an exponential decay with an unknown offset in the time (independent variable). (Perhaps this is inherently very difficult?). > decay.pl <- nls (amp ~ expn(b0,b1,tau,t0,t), data = decay, + start = c(b0=1, b1=7.5, tau=3.5, t0=0.1), trace=T) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates

  Hello All,   I am working with dlm for the purpose of estimating and forecasting with a Kalman filter model. I have succesfully set up the model and started generating results. Of course, I need to somehow be sure that the results make sense. Without any apparent target to compare with, my natural selection is the results by odinary least square. The idea being that if I choose a diffuse prior,

I finally got (mostly) what I wanted. In an attempt to figure out how to get nls to deal with a non-differentiable function, I had (stupidly) 'simplified' the problem until it became singular. Can I do something to make optim() less sensitive to my initial guess? For this example, I get a lousy solution if I make the initial guess for t0 = min(t) = 0.05. Thanks again, -- Robert Merithew

Author: jrutt Repository: /hg/zfs-crypto/gate Revision: 41d7d68d4ab013616c499ed0e497bffb54c8cc73 Log message: 6232309 need configurable limit on number of open FMEs Files: update: usr/src/cmd/fm/modules/common/eversholt/eft.c update: usr/src/cmd/fm/modules/common/eversholt/eft.conf update: usr/src/cmd/fm/modules/common/eversholt/fme.c update: usr/src/cmd/fm/modules/common/eversholt/fme.h

Hello, I will start out with the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. Nevertheless, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset there are two Type categories and four Tissue categories, thus giving me 8 datasets in total. The dataset is in

This is not so much a bug as an infelicity in the code that can easily be fixed. The initialize expression in the quasi family function is, (uniformly for all links and all variance functions): initialize <- expression({ n <- rep.int(1, nobs) mustart <- y + 0.1 * (y == 0) }) This is inappropriate (and often fails) for variance function "mu(1-mu)".

Hi, I have great problems with my work in R. I look for to model the growth of fish. I have "Longitudinal data", a serie of repeated measures for each individual. Using the corresponding packages "nlme" in R. I treat to fit to the data different growth functions, wich were entered by me. Unfortunately for no it was arrived at the convergence, several error messages appeared. I

hi I am a student in FME in Greece and with my friends, we try to make a project in computer lesson. We try to make a grid with some Pentium III and we use Knoppix Cluster. But we have a surius problem.We can not make the other pc to boot over the network because the bios of the pc do not support the network boot order.So we thought to make some boot loaders but the think is that we dont know

Hello, I'm trying to do some non-linear regression with 2 cell types and 4 tissue type treatments using selfStart models Following Ritz and Streibig (2009), I wrote the following routines: ##Selfstart expDecayAndConstantInflowModel <- function(Tb0, time, aL, aN, T0){ exp(-time*aL)*(T0*aL+(-1+exp(time * aL))*Tb0 * aN)/aL } expDecayAndConstantInflowModelInit <- function(mCall, LHS,

Hello, I'm having problems creating and using a selfStart model with nlme. Briefly, I've defined the model, a selfStart object, and then combined them to make a selfStart.default model. If I apply getInitial to the selfStart model, I get results. However, if I try usint it with nls or nlsList, these routines complain about a lack of initial conditions. If someone could point out

I come across a situation that I am having a hard time to understand. When I compile the following code : char *tst( char *dest, const char *src, unsigned int len ) { for (int i=0 ; i<len ; i++) { dest[i] = src[i]; } return dest; } Clang generates this for the ‘for’ body: for.body: ; preds = %for.cond %arrayidx = getelementptr inbounds i8,