similar to: Problem defining a system of odes as a C library with lsoda

Hello R Developers, This is my first foray into using c-code with R, so please forgive my foolishness. I had a look at the archives and did not find anything on this, so hopefully I am not doubling up. I have tried to use R cmd to create an object file from the odesolve dynload example. I am using windows and have just installed rtools, and have the latest version of stable R (2..7.2). This is

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

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:

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

I'm solving the differential equation dy/dx = xy-1 with y(0) = sqrt(pi/2). This can be used in computing the tail of the normal distribution. (The actual solution is y(x) = exp(x^2/2) * Integral_x_inf {exp(-t^2/2) dt} = Integral_0_inf {exp (-xt - t^2/2) dt}. For large x, y ~ 1/x, starting around x~2.) I'm testing both lsoda and rk4 from the package odesolve. rk4 is accurate using step

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

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

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 =

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,

Hello there, Suppose you want to solve the following system of ODE's (a simple Lotka-Volterra predator prey model) dP/dt = beta*P*V - mu*P dV/dt = r*V - beta*P*V where P and V are the numbers of predators and prey. Now, this is easy to do, but suppose you have a system of equations like this, dP1/dt = beta1*P1*V1 - mu1*P1 dP2/dt = beta2*P2*V2 - mu2*P2 dV1/dt = r1*V1 - beta1*P1*V1

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

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 =

Hello, I am an R newbie, trying to use lsoda to solve standard Lotka-Volterra competition equations. My question is: how do I pass a parameter that varies with time, like say, phix <- 0.7 + runif(tmax) in the example below. # defining function lotvol <- function(t,n,p){ x <- n[1]; y <- n[2] rx <- p["rx"]; ry <- p["ry"] Kx <-

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

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 <-

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

Hi All, I am running R 2.2.0 on Mac OS 10.4.2, dual G5 processors with 8 Gig RAM. I am running a simulation with lsoda that requires ~378 s to complete one set of time intervals. I need to optimize the parameters, and so need to considerably speed up the simulation. I have tried to figure out how to change the appropriate memory allocation and have search R help and Introductory

Dear All,   Currently I am running the following code:   library(stats4) library(odesolve) library(rgenoud) Input<-data.frame(SUB=c(1),time=c(0.5,3,10,15),lev=c(2.05,12.08,9.02,8)) XD<-500 IT<-3 diffeqfun<-function(time, y, parms) {   if(time<=IT)      dCpdt <- (XD/IT)/parms["Vol"] -

Bonjour, Je cherche à installer le package odesolve du logiciel de statistique R sous Ubuntu Linux. C'est un package qui contient des fonctions appelant du code en Fortran. A l'installation sous R via le shell, j'obtiens l'erreur suivante: Hi, I tried to install odesolve package of R under Ubuntu Linux. But I got the following error: ghislain@ghislain-laptop:~$ sudo R [sudo]

Hi, I am trying to solve a model that consists of rather stiff ODEs in R. I use the package ODEsolve (lsoda) to solve these ODEs. To speed up the integration, the jacobian is also specified. Basically, the model is a one-dimensional advection-diffusion problem, and thus the jacobian is a tridiagonal matrix. The size of this jacobian is 100*100. In the original package