similar to: estimating the starting value within a ODE using nls and lsoda

Dear All, I am trying to use ode solver "rk4" to solve an ODE system, however, it keeps saying: Error in eval(expr, envir, enclos) : object "dIN" not found. The sample codes are enclosed as follows, please help me. Thank you very much! rm(list=ls()) library(odesolve) # The ODE system ode <- function(t,x,p){ with(as.list(c(x,p)),{

Dear All, I am trying to use ode solver "rk4" to solve an ODE system, however, it keeps saying: Error in eval(expr, envir, enclos) : object "dIN" not found. The sample codes are enclosed as follows, please help me. Thank you very much! rm(list=ls()) library(odesolve) # The ODE system ode <- function(t,x,p){ with(as.list(c(x,p)),{

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 The deSolve package allows you to specify the model code in C or Fortran. Thanks to the excellent vignette this works fine. However I have not yet managed to use forcing functions in C code. In pure R code this works very well with approxfun() specified outside the model: ############################################### #Model lvml <- function(t, x, parms) {

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:

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

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

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

Thanks to Dieter Menne and Spencer Graves I started to get my way through lsoda() Now I need to use it in with nls() to assess parameters I have a go with a basic example dy/dt = K1*conc I try to assess the value of K1 from a simulated data set with a K1 close to 2. Here is (I think) the best code that I've done so far even though it crashes when I call nls()

Dear Michael, One key is adjustment of nls optimizer tolerance. I notice it has to be higher than usual, but, I recovered your noisy "known" parameter values with an error of K1 (-7%) and k1 (-6%): #### Miller problem with Dalgaard modifications ## Linares 1/22/2004 ## Solution 1 nls(noisy ~ lsoda(xstart, time, one.compartment.model, c(K1=K1, k2=k2))[,2], data=C1.lsoda,

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

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())

Hi, I am trying to run a 1D nutrient-phytoplankton-zooplankton model in R using the package 'deSolve'. The code is shown below: DEPTH = seq(2.5, 147.5, 5) NPZ = function(t, state, params){ with(as.list(params), { P <- state[1:NB] Z <- state[(NB + 1): (2*NB)] N <- state[(2*NB + 1): (3*NB)] F.I = function(z, hr){ I0 = function(hr){

Hi all, I try to assess the parameters (K1,K2) of a model that describes the adsorption of a molecule onto on adsorbent. equation: dq/dt = K1*C*(qm-q)-K2*q I know the value of 'qm' and I experimentally measure the variables 'q', 'C', and the time 't'. t C q 1 0 144.05047 0.0000000 2 565 99.71492 0.1105625 3 988 74.99426

Hello, I am writing a package with a collection of several models. In order to allow users to play interactively with the models (in contrast to hacking lengthy scripts), I want to put all what is needed to run a particular model into a single list object for each model. Then there will be a collection of functions to run the model or to modify parameters, time steps, integration method ...,

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

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

Dear Colleague: I am glad to hear from you. I was going to contact you and ask if you have notes on the steps involved on how you implemented LSODA. I will gladly take the project on. In addition to dassl, I want to implement ODESSA also. In other words, my project is a "suite" of ode solvers for R. The application of these solvers will be to the study of the distribution of

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

Hi R help, I wanted to simulate two pool model (A&B) using deSolve package for time 0 to 12 by 1.? Initial values of the state variables are A=5, B=3. The fluxes are as follows1) Flux into A= 5 units per unit time?2) Flux from A to B= 0.33) Flux out of A=0.1?4) Flux from B to A=0.35) Flux out of B=0.3 Here is the R code I compiled to estimate the size of A and B and graph the output