similar to: DLSODA error

Displaying 5 results from an estimated 5 matches similar to: "DLSODA error"

2011 Mar 31
0
dfsane arguments
Hi there, I'm trying to solve 2 nonlinear equations in 2 unknowns using the BB package. The first part of my program solves 3 ODEs using the deSolve package. This part works. The output is used as parameter values in the functions I need to solve. The second part is to solve 2 equations in 2 unknowns. This does not work. I get the error message "unexpected end of input". So what
2013 Feb 10
4
A Hodgkin Huxley Model
Hi All It has been suggested to me that the folks in this list might be able to help me out of my misery. As part of my learning curve to building a rather detailed model of a small neurone complex I am implementing some existing models in R. For the moment I want to implement the Izhikevich model as described in his 2003 paper. The equations are as follows: v' = 0.04v^2 + 5v + 140 - u - I
2013 Feb 13
1
An extended Hodgkin-Huxley model that doesn't want to work.
Hi All I have been struggling with this model for some time now and I just can't get it to work correctly. The messages I get when running the code is: DLSODA- 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. In above message, R = [1] 0 0 DINTDY- T (=R1) illegal In above message, R = [1]
2013 Apr 10
0
Problem with ode
Hi, I am trying to run a 1D nutrient-phytoplankton-zooplankton model in R using the package 'deSolve'. The code is shown below: DEPTH = seq(2.5, 147.5, 5) NPZ = function(t, state, params){ with(as.list(params), { P <- state[1:NB] Z <- state[(NB + 1): (2*NB)] N <- state[(2*NB + 1): (3*NB)] F.I = function(z, hr){ I0 = function(hr){
2012 Mar 25
2
avoiding for loops
I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",