similar to: Michaelis-menten equation

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

Dear R users: I have a problem about catch the value from function. I have following two functions (part): sbolus1 <- function() { ....... for( i in 1:Subject) { kel<-par1 Vd<-par2 PKindex<-sbolus1.out(PKtime,kel,Vd,defun,par1,par2,Dose,i) } savefile(PKindex) } sbolus1.out<-function(PKtime,kel,Vd,defun,par1,par2,Dose,i) { time<-PKtime$time

Dear list, I'm migrating a project from Matlab to R, and I'm facing a relatively complicated problem for nls. My objective function is below: >> objFun <- function(yEx,xEx,tEx,gamma,theta,kappa){ yTh <- pdfDY(xEx,tEx,gamma,theta,kappa) sum(log(yEx/yTh)^2) } The equation is yTh=P(xEx,tEx) + noise. I collect my data in: >> data <-

Is it possible to use some form of robust regression with the breakpoints routine so that it is less sensitive to outliers? --Rich Richard Kittler Advanced Micro Devices, Inc. Sunnyvale, CA

Hi there, I am trying to use linear regression to solve the following equation - y <- c(0.2525, 0.3448, 0.2358, 0.3696, 0.2708, 0.1667, 0.2941, 0.2333, 0.1500, 0.3077, 0.3462, 0.1667, 0.2500, 0.3214, 0.1364) x2 <- c(0.368, 0.537, 0.379, 0.472, 0.401, 0.361, 0.644, 0.444, 0.440, 0.676, 0.679, 0.622, 0.450, 0.379, 0.620) x1 <- 1-x2 # equation lmFit <- lm(y ~ x1 + x2) lmFit Call:

Hi R-experts, There is lots of information about maximum likelihood estimation in R. However, I didn't came across anything about maximum likelihood with constraints. For example, estimation of parameters k(1) to k(20) with maximum likelihood, where sum(k(i)) = 0. Is there any standard function in R that can do this, or is this something that I should set up myself? Greetings, Church

I have an optimization question that I was hoping to get some suggestions on how best to go about sovling it. I would think there is probably a package that addresses this problem. This is an ordering optimzation problem. Best to describe it with a simple example. Say I have 100 "bins" each with a ball in it numbered from 1 to 100. Each bin can only hold one ball. This optimization is

Dear All,   Currently I am running the following code:   library(stats4) library(odesolve) library(rgenoud) Input<-data.frame(SUB=c(1),time=c(0.5,3,10,15),lev=c(2.05,12.08,9.02,8)) XD<-500 IT<-3 diffeqfun<-function(time, y, parms) {   if(time<=IT)      dCpdt <- (XD/IT)/parms["Vol"] -

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

I am working on minimization of sum of squared errors for a problem that has 2 box-constrained parameters. I got the solution for this problem using "L-BFGS-B" method in optim function using an R code as res<-optim(par=c(parInit), fn=myFunction, method = c("L-BFGS-B"), lower = parMin, upper = parMax,

Hi r-users, I want to use the sn package but I got the following message: > install.packages(repos=NULL,pkgs="c:\\Tinn-R\\sn_0.4-12.zip") Warning: package 'sn' is in use and will not be installed updating HTML package descriptions I did tried to do it a few times but it gives the same message.  ________________________________ From:

Dear sir,    How to do bilinear time series in R?Is there any functions or packages?  thank you! -----Sincerely yours Kuangnan Fang 方匡南 敬上 department of statistics ,Economics school,Xia men University. Fujian Province (361005) China Mobile Phone：15860721915 SKYPE: ruiqwy MSN Messenger: ruiqwy@hotmail.com QQ:39863401 --- 09年3月31日，周二, r-help-request@r-project.org