similar to: LSODA not accurate when RK4 is; what's going on?

I'm trying to use odesolve for integrating various series of coupled 1st order differential equations (derived from a system of enzymatic catalysis and copied below, apologies for the excessively long set of parameters). The thing that confuses me is that, whilst I can run the function rk4: out <- rk4(y=y,times=times,func=func, parms=parms) and the results look not unreasonable:

Hello, I wanted to test the odesolve package and tried to use compiled C-code. But when I do: erg <- lsoda(y, times, "mond", parms, rtol, atol, tcrit=NULL, jacfunc=NULL, verbose=FALSE, dllname="mond", hmin=0, hmax=Inf) I get the error message: Error in lsoda(y, times, "mond", parms, rtol, atol, tcrit = NULL, jacfunc =

I am running R 1.2.3 with ESS5.1.18 with Windows 98. I am trying to use lsoda in the odesolve apckage and am having problems. Question: The return value of the function of the system of ode's has to be a list that includes first, the ode's and second, "a vector (possibly with a `names' attribute) of global values that are required at each point in `times'." I

All- I am interested in estimating a parameter that is the starting value for an ODE model. That is, in the typical combined fitting procedure using nls and lsoda (alternatively rk4), I first defined the ODE model: minmod <- function(t, y, parms) { G <- y[1] X <- y[2] with(as.list(parms),{ I_t <- approx(time, I.input, t)$y dG <- -1*(p1 + X)*G +p1*G_b dX <-

I have been trying to make use of the odesolve library on my university's Linux grid - currently R version 2.0.1 is installed and the system runs 64-bit Scientific Linux based on Redhat. I cannot seem to get lsoda working when I define the model as a shared C library. For example, the following snippet uses the mymod.c example bundled with the package: ### START rm(list=ls())

Dear All, I was using rk4 and lsoda to solve a ODE system. However, both of them gave bad accurate solutions, especially compared with Matlab solver ODE45. For example, ODE45 gave solutions that can go to a stable level (about 1.6) when time goes to infinity, however, the solutions from lsoda are decreasing to very very small (about 1e-130) numbers. Does R have more accurate ODE solvers as

Dear list - Does anyone have any ideas / comments about why I am receiving the following warning when I run lsoda: 1: lsoda-- at t (=r1), too much accuracy requested in: lsoda(start, times, model, parms) 2: for precision of machine.. see tolsf (=r2) in: lsoda(start, times, model, parms) I have tried changing both rtol and atol but without success. I saw the thread in the

Dear Colleagues, Our group is also working on implementing the use of R for pharmacokinetic compartmental analysis. Perhaps I have missed something, but > fit <- nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=0.5, k2=0.5)), + data=C1.lsoda, + start=list(K1=0.3, k2=0.7), + trace=T + ) Error in eval(as.name(varName), data) : Object

Dear List, Wonder if you have some thoughts on the following question using lsoda in desolve: I have the following data and function: require(deSolve) times <- c(0:24) tin  <- 0.5 D <- 400 V    <- 26.3 k <-0.056 k12  <- 0.197118 k21  <- 0.022665 yini <- c(dy1 = 0,dy2 = 0)  events <- data.frame(var = "dy1",time = c(10,15),value = c(200,100),method =

Dear R users: I encountered difficulties in michaelis-menten equation. I found that when I use right model definiens, I got wrong Km vlaue, and I got right Km value when i use wrong model definiens. The value of Vd and Vmax are correct in these two models. #-----right model definiens-------- PKindex<-data.frame(time=c(0,1,2,4,6,8,10,12,16,20,24),

Hi - I am getting different results when I run the numerical integrator function lsoda (odesolve package) on a Mac and a PC. I am trying to simulating a system of 10 ODE's with two exogenous pulsed inputs to the system, and have had reasonably good success with many model parameter sets. Under some parameter sets, however, the simulations fail on the Mac (see error message below). The

R help list subscribers, I am a new user of R. I am attempting to use R to explore a set of equations specifying the dynamics of a three trophic level food chain. I have put together this code for the function that is to be evaluted by LSODA. My equations Rprime, Cprime, and Pprime are meant to describe the actual equation of the derivative. When I run LSODA, I do not get the output that

Hi, I'm new to R and have a problem with a little test program (see below). Why doesn't <<- in function rk4 assign the new value to y so that it is seen in rktest. I thought that <<- does exactly this. But it seems that I didn't get it right. I would be very appreciative for an explanation of that behaviour of <<-. I know how to write the whole thing so that it

Dear Hank, Last question first: really, only you can say for sure if 4e-281 and 5e-11 are small enough; it depends on the units you measure your state variables in. However, this strategy cannot get the state variables to exactly 0. Obviously, you could get closer to 0.0 faster by setting the derivatives even larger in absolute value. You may run into problems with the solver when the

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

A couple of minor points about the odesolve package (which I am otherwise enjoying very much): 1. "scalar" is misspelled as "scaler" in the definitions of the rtol and atol parameters 2. it is possible to crash R by doing something dumb, e.g failing to read the documentation carefully enough and (a) returning only a vector of derivatives and not a list of (derivatives,

Dear All, I like to simulate a physiologically based pharmacokinetics model using R but am having a problem with the daspk routine. The same problem has been implemented in Berkeley madonna and Winbugs so that I know that it is working. However, with daspk it is not, and the numbers are everywhere! Please see the following and let me know if I am missing something... Thanks a lot in advance,

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

The information at http://cran.r-project.org/doc/manuals/R-admin.html#The-MinGW-compilers and http://www.murdoch-sutherland.com/Rtools/ is slightly inconsistent about the compiler used to build Windows binary packages available through cran. The 'candidate' package of the recommended MinGW-5.0.0.exe installs g++/g77 3.4.4 (as does the updated installer MinGW-5.0.2.exe). "An

Installing odesolve in Raqua 1.8.0 or 1.8.1 under MacOSX gives the following message: Warning message: Installation of package odesolve had non-zero exit status in: install.packages(ui.pkgs, CRAN = getOption(where), lib = .libPaths()[1]) Moreover, in the source of odesolve is no makefile. Does anyone know how to get a proper installation? Maartje