similar to: olesolve: stepsize

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 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

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)

Dear All, I have a pair of .cpp and .def file can be compiled using VC++ and works perfectly well in S-PLUS. I wanted to do the same for R; so I followed the guidline given in "Building from a source-code library under Windows" as much as possible and manage to compile them using VC++ and call it from R. But it gives different answer from the one called from S-Plus. I know that I did

Dear Hank, Last question first: really, only you can say for sure if 4e-281 and 5e-11 are small enough; it depends on the units you measure your state variables in. However, this strategy cannot get the state variables to exactly 0. Obviously, you could get closer to 0.0 faster by setting the derivatives even larger in absolute value. You may run into problems with the solver when the

Hi there, I have two series of data. plotting the density function of both gives me an idea about the difference of the data. But I would like to quantify the difference I see. a <- rnorm(100) b <- rnorm(100) da <- density(a) db <- density(b) The problem is that da$x and db$x are different and so I have difficulties to compare them... Is there any way to force the density

Dear R users, I hope this mailing list be the right place for my question. Usaully performance criterion of curve fitting like as Directinal Symmetry (DS) and Mean Absolute Percentage Error (MAPE) are correctly used with absolute time series which includes only positive values. But how it is possible to use DS and MAPE with other time series like as return series (for example return of a

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 ...,

Dear useRs, I have written an ecological model, based on the epidemiology SIR model. I've been trying to simulate it in R. However, I can't simulate it properly. Two guesses: my script isn't right; I'm not setting the parameters properly I have uploaded an image to the model here: http://img24.imageshack.us/img24/743/imagemutr.jpg The script I am using is as it follows:

To whomever may be of help, I am a student in a graduate modeling class at the University of North Carolina at Wilmington. I am trying to get a STELLA model converted into R. I am in the process of trying to 're-write' the script into R, but I seem to be missing pieces (i.e. parm values) that are keeping me from being able to replicate this model into R. Does anyone have an idea of a

hey can anybody help me? i have to simulate the richardson Arms race model on R.. for my simulation class...

I'm fairly new to R, and I'm trying to write out a population model that satisfies the following; the system consists of s species, i= 1, 2,...,s network of interactions between species is specified by a (s x s) real matrix, C[i,j] x[i] being the relative population of the "ith" species (0 =< x[i] =< 1, sum(x[i]=1) the evolution rule being considered is as follows;

Dear R-users, I am currently modifying a previously developed predator prey model and was curious if there was a way to add in a disturbance to the model (let's say at time t=100). The disturbance can be the introduction of 40 prey (N=40) and 10 predators (Pred = 10). I would like to see my model go from a state of equilibrium (up to t = 99), show this disturbance (at t = 100) and then

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

Hello! I'm interested to fit parameters (to data) in a grid-based (individual) model. If I understood well, simecol library has the fitOdeModel function but it is only suited to odeModels (differential equation). Alternatively, FME package has several functions able to perform this procedure but all examples are for differential eq. models. It is mentioned in the that such functions could

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and that access to the client should be denied. > James > > ----- Original Message ----- > From: "Alexander Lobodzinski" <lobo@mental.com> > To: <samba@lists.samba.org> > Sent: Monday, August 20, 2001 10:17 AM > Subject: Can I have only certain users log in to a Samba DC client? > > > > A machine should be member of a domain controlled by