similar to: ode() tries to allocate an absurd amount of memory

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 have a problem integrating the 'standard map' ( http://en.wikipedia.org/wiki/Standard_map http://en.wikipedia.org/wiki/Standard_map ) with deSolve: By using the modulo-operator '%%' with 2*pi in the ODEs (standardmap1), the resulting values of P and Theta, should not be greater than 2pi. Because this was not the case, i was thinking that the function 'ode' has some

Hello List, Can you recommend me if odeSolve can handle stiff delay differential equations with discontinuities? Or any other package? Best, -m

I'm just getting to grips with using ode function and have used the examples and vignettes to produce a small model of a one-pool, michaelis-menten, enzyme kinetic reaction. The rate of flux of substrate into pool A is constant (fluxoa) however the rate of flux out of pool A is controlled by the HMM equation (v = Vmax/ ( 1 + (Km / Concentration A )) ). This function works fine and

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 am trying to to write a wrapper function for the ode solver (under the package desolve) to enable it to take multivariate arrays. I know how to do it for 1 dimension arrays but my code breaks down when I try to do it for 2 dimensional arrays. Here is my code diffwrap<-function(t,y,mu)vdpol(t=t,A[1:3,1:4]<-y[1:12],B[1:12]<-y[13:24],mu=mu) vdpol<-function(t,A,B,mu) { list(c(mu,

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

Dear R-users, I'm running an MLE procedure where some ODEs are solved for each iteration in the maximization process. I use mle2 for the Maximum likelihood and deSolve for the ODEs. The problem is that somewhere along the way the ODE solver crashes and I get the following error message: DLSODA- Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next

Hello, I am trying to get the output from the numerical simulation of a system of ordinary differential equations for a range of values for three parameters. I am using the ode() function (deSolve package) to run the numerical simulation and apply() to run the simulation function for each set of parameter values. I am having trouble getting the apply() function to work. Here is an

I tried to use lsode with precompiled C code instead of an R function defining the derivatives. No problem so far. However, now I need to implement an ODE that contains spline functions, i.e. the derivatives at given time points depend on the value of a spline function at this time point. What is an efficient way to implement this in precompiled C code? -- Daniel Kaschek <daniel.kaschek at

I am trying to use the package ode and periodically it will come up with this error message Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next step (H = step size). Solver will continue anyway. And then the program just take very long to run. Is there anyway to get the program to terminate when this warning is issued instead of continuing to run ?

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,

Hi, I have a data-matrix: > PID sato hrs fim health 214 3 4.376430 6.582958 5 193 6 4.361825 3.138525 6 8441 6 4.205771 3.835886 7 7525 6 4.284489 3.245139 6 6806 7 4.168926 2.821833 7 5682 7 1.788707 1.212653 7 5225 6 1.651463 1.436980 7 4845 6 1.692710 1.267359 4 4552 5 1.686448 1.220539 6

Hello All, I'm struggling to solve this ODE using R, vdpol <- function (h, v, t) ( list(c ( -0.1*v/(pi*(2*10*h-h^2)), (v = (-0.1*v/(pi*(2*10*h-h^2))^2) + 2*9.81*h)) )) library(deSolve) yini <- (c(h = 20, v=0)) nonstiff <- ode(y = yini, func = vdpol, times= seq(0, 30, by =

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 <-

Hello, I am solving a set of ODEs using deSolve and have run into a problem I would appreciate some advice on. One of the parameters (m) in the ODEs changes between two states when one of the variables (D) crosses a threshold (D_T) for the first time in either direction. Additionally, when the variable crosses the threshold (either by increasing or decreasing), there is a time delay (delay)

Hello, I'm used to working with R in estimation of distributions and statistical models. I'd like to estimate a simple model of an output y(t) based on a concentration as independent variable, using a timed series of experimental data; I've seen this I think somewhere in R? I'd appreciate to hear if anybody has used this with success. regards, S

Hello, I am developing an agent-based model to simulate the spread of infectious diseases in heterogeneous landscapes composed of habitat polygons (or clumps of connected cells). To simplify the model, I consider a habitat grid (or raster) containing the polygon ID of each cell. In addition, I have epidemiological parameters associated with each polygon ID. At each time step, the parameter values

Hi, I've solved a simple differential equation describing the degradation of amino acid carbon (THAA-C) using deSolve. Code is a follows: # Input of model parameters, a and b describes form of curve, i is apparent initial age of Org. C. parameters <- c(a = a, b = b, i=i) # Initial value of the model, G state = c(G = G) #specifies the function degradation as a function of

> On Apr 18, 2018, at 1:04 AM, Hannah Meredith <hrmeredith12 at gmail.com> wrote: > > Hello, > > I am solving a set of ODEs using deSolve and have run into a problem I > would appreciate some advice on. One of the parameters (m) in the ODEs > changes between two states when one of the variables (D) crosses a > threshold (D_T) for the first time in either direction.