similar to: generating sets of parameters, when some parameters are vectors

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >

Hello, I am an R newbie, trying to use lsoda to solve standard Lotka-Volterra competition equations. My question is: how do I pass a parameter that varies with time, like say, phix <- 0.7 + runif(tmax) in the example below. # defining function lotvol <- function(t,n,p){ x <- n[1]; y <- n[2] rx <- p["rx"]; ry <- p["ry"] Kx <-

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 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 #####

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

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

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

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

Sorry to bother you and I hope this will be easy to solve. I am trying to run R scripts in batch, called from another programme, under Windows XP. I have R 2.4.1 I need to be able to pass a list of numeric arguments to the script as well. The Rscript and its input data file are attached. I have been trying the syntax below but it doesn't work: Rcmd BATCH -no-save

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

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

Hi. Can anyone suggest a simple way to obtain in R a list of vector coordinates of the following form? The code below is Mathematica. In[5]:= Flatten[Table[{i,j,k},{i,3},{j,4},{k,5}], 2] Out[5]= {{1,1,1},{1,1,2},{1,1,3},{1,1,4},{1,1,5},{1,2,1},{1,2,2},{1,2,3},{1 ,2,4},{1,2, 5},{1,3,1},{1,3,2},{1,3,3},{1,3,4},{1,3,5},{1,4,1},{1,4,2},{1,4,3}, {1,4,

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),

So I am working on an economic model and I need to change the parameters of the runif statement as time goes on. Ex. X <-runif(1:50,0,5) X <-runif(51:100,100,150) X <-runif(100:T, 1,2) Not sure how to go about entering this in to R properly. -- View this message in context: http://r.789695.n4.nabble.com/Runif-Help-same-variable-3-different-parameters-tp3073893p3073893.html Sent from

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

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,

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

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high