similar to: External signal in ODE written in C (using deSolve and approx1?)

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 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

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 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 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

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 developers, we have currently a 'mysterious' test problem with one package that successfully passed the tests on all platforms, with the only exception of Solaris x86 where obviously one of our help examples breaks the CRAN test. As we don't own such a machine I want to ask about a possibility to run a few tests on such a system: r-patched-solaris-x86 An even more recent

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,

Hello, I am new to R and I am having problems trying to model logistic population growth with the deSolve package. I would like to run the model for four populations with the same initial population and carrying capacity but with different growth rates and put the results into a data frame. When I run the following lines of code I get unexpected results from "output" but the format is

I need to solve a system of non-linear fourth-order differential equations. Is there a command which solves this system? Thanks in advance.

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 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]

This is applicable to either using optim or the differential equation solver or any similar solver Suppose I want to use the differential equation solver and this is my code d<-y[2] vdpol<-function(t,y) { list(c(1, d, 3, 4 ) } stiff<-ode(y=rep(0,4),times=c(0,1),func=dvdpol,parms=1) The thing is I want d to be composed of one of state variables in 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 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