similar to: Differential Equations

Dear R-users Problem: Given the following system of ordinary differential euqations dM/dt = (-n)*M-h*M dS/dt = n*M-h*S+u*R dA/dt = h*S-q*A dI/dt = q*A-p*I dJ/dt = h*M-v*J dR/dt=p*I+v*J-u*R where M,S,A,I,J,R are state variables and n,h,u,q,p,v parameters. I'm able to calculate the likelihood value based on the solutions M,S,A,I,J,R of the ODE's given the data, but without an explicit

Hello! I would like to know if R can solve Differential Equations... I don't think so because, in my point, I see R like a Statistical System, not a Math System. Am I wrong? Thank you very much. M??rcio de Medeiros Ribeiro Graduando em Ci??ncia da Computa????o Departamento de Tecnologia da Informa????o - TCI Universidade Federal de Alagoas - UFAL Macei?? - Alagoas - Brasil Projeto CoCADa

Hi, I would like to solve a system of coupled ordinary differential equations, where there is a delay (time lag) term. I would like to use the "lsoda" function "odesolve" package. However, I am not sure how to specify the delay term using the syntax allowed by odesolve. Here is an example of the kind of problem that I am trying to solve: > library(odesolve)

To whom it may concern, I am a student at Macaleste College, and next semester Macalester is going to offer a course for CellBio that is mainly statistically based. For the most part the students will be using R for analysis. The problem is there will be some simple differential equations for the students to solve. The committee that in charge of the classes corriculam would like only to

Is there any R code available for solving ODE's? I am considering building an interface to ODEPACK, but don't want to duplicate any existing effort. -- Tom Wainwright NOAA/NMFS/NWFSC 2030 S Marine Science Dr Newport, OR 97365 USA thomas.wainwright at noaa.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, Does somebody knows of the existence of a library of functions for solving differential equations, I need a solver for a system of non linear first order differential equations. I think there was something in IMSL but that was a long time ago and probably there are some new algorithms in the new now. Thanks for any pointers. . R. Heberto Ghezzo Ph.D. Meakins-Christie Labs McGill

Hello, I want to know if there are some functions or packages to solve differential and integral equation using R. Thanks. Shao chunxuan. [[alternative HTML version deleted]]

I am an initial user of R. Could you give me some explanations or examples on how to solve the first order differential equations by the first-order Runge-Kutta method? Thank you very much Kind regards

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

Hello list I have a theoretical differential equation model (two coupled differential equations) describing the acidification of dairy cultures in milk: dX/dt = f(H, param)*X dH/dt = g(param)*dX/dt pH = -log10(H) I also have actual data of the same. I would like to estimate the parameters of the theoretical model from real data, but don't know how to go about it in R (I'm fairly new

Hi all, My apologies if this message is incredibly inept but I am very new to both computer programming and to R. I am working with the odesolve add-on and have the following function defined RVF_Single <- function(t, x, p) within the script I also have the following functions defined: T1<-function(t) {T1<-27.5-12.5*cos(2*pi*t/365)} and B1<-function(T1,t)

e1 <- function(x,b,t){ d<-(x)*(b^t) plot(d) } e1(2, 2,seq(from=0, to=6, by=1)) Is there a way to do this with a change in time. I would like to use differential equations. I am trying to model a population with an initial value, fecundity per time step, and a death rate. The above simply shows an exponential growth rate. I would like to model species response like the famous

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

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"] -

Dear R-users, I am trying to perform some simulations from a model defined by ordinary differential equations. I would be grateful if someone could indicate some functions/packages/examples I could look at. Thank you in advance. Sebastien

It seems that the method `iteration` in the function `ode` can be useful in my case: > Method "iteration" is special in that here the function func should > return the new value of the state variables rather than the rate of > change. This can be used for individual based models, for difference > equations, or in those cases where the integration is performed within >

> On Jun 21, 2017, at 12:48 PM, Marine Regis <marine.regis at hotmail.fr> wrote: > > 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

Hello Everybody, I am running R 2.2.0 with Windows XP i am trying to fit nonlinear differential equation to data sets which looks like this: Week N C 0 1 1 1 5 6 2 6.2 12.2 3 59 71.2 4 39 110.2 5 38 148.2 6 44 192.2 7 20.4 212.6 8 19.4 232 9 34.2 266.2 10 35.4 301.6 and i need to fit these data to the following diff equation: dNdt=a*N-b*N*C, dCdt=N^2, Where a=birth rate, b=death rate and N=

I have a package that solves systems of ordinary differential equations coded as an R function (odesolve, on CRAN). The function is passed to R code, and c and Fortran code called by it uses lang4() and eval() to evaluate the R function inside a compiled c function to use in a compiled ODE solver. This works quite well, but can be slow. I'd like to be able to supply the name of a compiled

Hello, I am using odesolve to simulate a group of people moving through time and transmitting infections to one another. In Matlab, there is a NonNegative option which tells the Matlab solver to keep the vector elements of the ODE solution non-negative at all times. What is the right way to do this in R? Thanks, Jeremy P.S., Below is a simplified version of the code I use to try to do this,