similar to: deSolve: Problem solving ODE including modulo-operator

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) {

Hello, I have a syntactic problem with ode. How do I specify vectors of equations in ordinary differential equation systems. (i.e. in my case I want to simulate an a priory undefined number of species that have different parameters but the same behaviour) I demonstrate this using the Lotka Volterra example. The code below does not work and I have not a good idea how to specify this right. ##

Hi there R-helpers: I'm having problems with the function ode() found in the package deSolve. It seems that when my state variables are too numerous (>33000 elements), the function throws the following error: Error in vode(y, times, func, parms, ...) : cannot allocate memory block of size 137438953456.0 Gb In addition: Warning message: In vode(y, times, func, parms, ...) : NAs

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, 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

Hi, I have used DeSolve package for my ODE problem regarding infectious disease transmission and currently am trying to pass lots (roughly a thousand) of model parameters to the C compiled model (I have to use C compiled code instead of R code purely because of the speed). I can't go define it one by one as it gonna take ages to finish and also quite difficult to revise. I have read the

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 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

Hi All Having been pointed the use of events and roots in deSolve, I was able to implement the Izchikevich model of spiking neurons. However, I'm not too sure of defining the event. The deSolve documentation says: An event is triggered when the ball hits the ground (height = 0) Then velocity (y2) is reversed and reduced by 10 percent. The root function, y[1] = 0, triggers the event: >

Dear All wonder if you could provide some insights on the following: currently I have this code which produces the expected results: require(deSolve) pars <- list(k = 0.08,v=15) intimes <- c(0,0.5,12) input <- c(800,0,0) forc <- approxfun(intimes, input, method="constant", rule=2) derivs <- function(t, state, pars) { inp <- forc(t) dy1 <- - pars$k * state[1]

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 ?

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 <-

Dear all, I am using the DeSolve Package to simulate a system dynamics model. At the problematic point in the model, I basically want to decide how many products shall be produced to be sold. In order to determine the amount a basic forecasting model of using the average of the last 12 time periods shall be used. My code looks like the following. ? [?] # Time units in month START<-0;

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 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 R users, an improved version of package deSolve (version 1.3) is now available on CRAN. deSolve, the successor of R package odesolve, is a package to solve initial value problems (IVP) of: - ordinary differential equations (ODE), - differential algebraic equations (DAE) and - partial differential equations (PDE). The implementation includes stiff integration routines based on the ODEPACK