similar to: optim() argument scoping: passing parameter values into user's subfunction

All, I have been able to successfully use the optim( ) function with "L-BFGS-B" to find reasonable parameters for a one-compartment open pharmacokinetic model. My loss function in this case was squared error, and I made no assumptions about the distribution of the plasma values. The model appeared to fit pretty well. Out of curiosity, I decided to try to use nlminb( ) applied to a

Hi List, I would like to plot multiple curves (parametric density curves) in one plot. For example: # parameters for three normal density curves parms = data.frame(ID=c(1,2,3),mu=c(50,55,60),sigma=c(10,12,15)) # I can easily draw three normal density curves using curve(): curve(dnorm(x,mean=parms$mu[1],sd=parms$sigma[1]),from=0, to=150, ylab="density", col="red")

Dear R users: I encountered difficulties in michaelis-menten equation. I found that when I use right model definiens, I got wrong Km vlaue, and I got right Km value when i use wrong model definiens. The value of Vd and Vmax are correct in these two models. #-----right model definiens-------- PKindex<-data.frame(time=c(0,1,2,4,6,8,10,12,16,20,24),

Hi, I have a system of ODE's I can solve with lsoda. Model=function(t,x,parms) { #parameter definitions lambda=parms[1]; beta=parms[2]; d = parms[3]; delta = parms[4]; p=parms[5]; c=parms[6] xdot[1] = lambda - (d*x[1])- (beta*x[3]*x[1]) xdot[2] = (beta*x[3]*x[1]) - (delta*x[2]) xdot[3] = (p*x[2]) - (c*x[3]) return(list(xdot)) } I want

Dear R gurus, I have a function "goftests" that receives the following arguments: * a vector "x" of data values; * a distribution name "dist"; * the dots list ("...") containing a list a parameters to pass to CDF function; and calls several goodness-of-fit tests on the given data values against the given distribution. That is: ##### BEGIN CODE SNIP #####

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: >

Hi, Is there another way to pass arguments via a vector to arbitrary functions as in the following code example without using a series of if else statements? f <- test(func, x, parms, fargs1, fargs2, ...) { # parms is a vector of parameters to func. # ... is for use by f, not by func. n <- length(parms) if (n == 0) y <- func(x) else if (n == 1) y <- func(x,

If I name the elements of the vector of initial values passed to optim(), then it attaches the names to the final result, e.g. > f <- function(parms) (parms[1]-1)^2+(parms[2]-2)^2 > optim(c(x=3,y=4), f) $par x y 0.9999635 2.0003241 $value [1] 1.063637e-07 $counts function gradient 65 NA $convergence [1] 0 $message NULL However, the vector that gets

Hi, The code below is giving me this error message: Error in while (err > eps) { : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In dnbinom(x, size, prob, log) : NaNs produced 2: In dnbinom(x, size, prob, log) : NaNs produced I know from the help files that for dnbinom "Invalid size or prob will result in return value NaN, with a warning", but I am not able

I just updated rpart to the latest version (3.1.0). There are a number of changes between this and previous versions, and some of the code I've been using with earlier versions (e.g. 3.0.2) no longer work. Here is a simple illustration of a problem I'm having with xpred.rpart. iris.test.rpart<-rpart(iris$Species~., data=iris[,1:4], parms=list(prior=c(0.5,0.25, 0.25))) + ) >

Hi All I am getting the above mentioned error when I run the code below. I don't know why because I have implemented the function and I'm calling the function with a parameter. I'm obviously missing the plot ... Can someone perhaps point out the error of my ways? Error: > out<-ode(y=init, times=times, func=G1999, parms=parms, method="lsoda") Error in m_Na(v) : attempt

Solved it: test <- list(a=1,b=2,c=3) new <- c(4,5,6) hold <- as.list(new) updated_test <- replace(test,c(1:3),hold) $a [1] 4 $b [1] 5 $c [1] 6 mean.parms <- as.list(mean.parms) mm.parms <- replace(far.parms,c(1:length(far.parms)),mean.parms) On 9/22/2017 10:34 AM, Evan Cooch wrote: > Suppose I have the following: > > test <- list(a=1,b=2,c=3) > > I

A couple of minor points about the odesolve package (which I am otherwise enjoying very much): 1. "scalar" is misspelled as "scaler" in the definitions of the rtol and atol parameters 2. it is possible to crash R by doing something dumb, e.g failing to read the documentation carefully enough and (a) returning only a vector of derivatives and not a list of (derivatives,

Hello there, Suppose you want to solve the following system of ODE's (a simple Lotka-Volterra predator prey model) dP/dt = beta*P*V - mu*P dV/dt = r*V - beta*P*V where P and V are the numbers of predators and prey. Now, this is easy to do, but suppose you have a system of equations like this, dP1/dt = beta1*P1*V1 - mu1*P1 dP2/dt = beta2*P2*V2 - mu2*P2 dV1/dt = r1*V1 - beta1*P1*V1

Dear list - Does anyone have any ideas / comments about why I am receiving the following warning when I run lsoda: 1: lsoda-- at t (=r1), too much accuracy requested in: lsoda(start, times, model, parms) 2: for precision of machine.. see tolsf (=r2) in: lsoda(start, times, model, parms) I have tried changing both rtol and atol but without success. I saw the thread in the

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

Dear people, I'm trying to do some analysis of a data using the models by Royle & Donazio in their fantastic book, particular the following function: http://www.mbr-pwrc.usgs.gov/pubanalysis/roylebook/panel4pt1.fn that applied to my data and in the console is as follows: > `desman.y` <- structure(c(3L,4L,3L,2L,1L), .Names = c("1", "2", "3",

Sorry about the subject --- On Thu, 24/9/09, KABELI MEFANE <kabelimefane@yahoo.co.uk> wrote: From: KABELI MEFANE <kabelimefane@yahoo.co.uk> Subject: Re: [R] Multiply Normal Curves To: R-help@r-project.org Date: Thursday, 24 September, 2009, 11:48 AM R -helpers   i have been trying to do this problem without must success,i managed to do a graph for x, but it is not what i want to

Dear R-users, I'm running an MLE procedure where some ODEs are solved for each iteration in the maximization process. I use mle2 for the Maximum likelihood and deSolve for the ODEs. The problem is that somewhere along the way the ODE solver crashes and I get the following error message: DLSODA- Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next

Well, that's a bit like driving from Boston to New York by way of Chicago. See ?structure test <- list(a=1,b=2,c=3) new <- c(4,5,6) test.new <- structure(as.list(new), names=names(test)) test.new $a [1] 4 $b [1] 5 $c [1] 6 Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka