similar to: Plotting multiple curves with lattice graphs

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

Hi all, I am trying to import some functions into a script and am having difficulty in doing so. I am able to import a series of functions from a .tex file into my script, and call on each function by column name, however R reads them as data rather than as functions and I am struggling with the syntax to make R read them as a function instead. Below is a small set of the things I have tried:

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 define,(i want to specify number of observations as well). I have also been able to do simple rendom sample.   data.frame(ID=c(1,2,3),mu=c(10000,34000,50000),sigma=c(2000,3000,5000)) curve(dnorm(x,mean=parms$mu[1],sd=parms$sigma[1]),from=2000, to=80000,

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 define. I have also been able to do simple rendom sample. If possible can someone help here is the code for the graph pleae help   data.frame(ID=c(1,2,3),mu=c(10000,34000,50000),sigma=c(2000,3000,5000)) curve(dnorm(x,mean=parms$mu[1],sd=parms$sigma[1]),from=2000,

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

Dear all, I ran into problems with the function "optim" when I tried to do an mle estimation of a simple lognormal regression. Some warning message poped up saying NANs have been produced in the optimization process. But I could not figure out which part of my code has caused this. I wonder if anybody would help. The code is in the following and the data is in the attachment. da <-

Hi, I??m trying to use the Win2BUGS package from R and I have a similar problem that reurns with the message: Error in FUN(X[[1]], ...) : symbol print-name too long But, there is no stray ` character in the file ( Sugestions given by: Duncan Temple Lang <duncan> Date: Mon, 26 Sep 2005 07:31:08 -0700 ) The progam in R is: library(R2WinBUGS) library(rbugs) dat <-

Dear useRs, I have a complicated function to be optimized with optim(), and whose parameters are passed to another function within its evaluation. This function allows for the parameters to enter as arguments to various probability distribution functions. However, I am violating some scoping convention, as somewhere within the hierarchy of calls a variable is not visible. I will give a

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

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,