similar to: Re membering the last time an event occurred within a dataframe

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 All, I like to simulate a physiologically based pharmacokinetics model using R but am having a problem with the daspk routine. The same problem has been implemented in Berkeley madonna and Winbugs so that I know that it is working. However, with daspk it is not, and the numbers are everywhere! Please see the following and let me know if I am missing something... Thanks a lot in advance,

My apriori apologies if this is felt to be the wrong list for this issue - although it starts with R, it's a combination of programs that creates the problem. Currently we are using windows metafile format for in-text tables for reports created in Word. However, we've discovered some artifactual lines being created in our final output once the Word document is changed to PDF. The process

Dear All, I have a pair of .cpp and .def file can be compiled using VC++ and works perfectly well in S-PLUS. I wanted to do the same for R; so I followed the guidline given in "Building from a source-code library under Windows" as much as possible and manage to compile them using VC++ and call it from R. But it gives different answer from the one called from S-Plus. I know that I did

Playing with ggplot, something I'd promised myself I'd get around to. I've the following scenario: library(lattice) library(ggplot2) myData <- data.frame( x = rnorm(100), y = rnorm(100), group = 1:4 ) xyplot(y ~ x | factor(group), data = myData, layout = c(2, 2)) qplot(x, y, facets = ~ group, data = myData) In this code the lattice example gives me a 2x2 layout, whereas

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

Thank you Frank and all for your advices. Here I attach the raw data from the Pawinski's paper. I have obtained permission from the corresponding Author to post it here for everyone. The only condition of use is that the Authors retain ownership of the data, and any publication resulting from these data must be managed by them. The dataset is composed as follows: patient number / MMF dose in

Hi, I am using the integrate function in some simulations in R (tried ver 2.12 and 2.15). The problem I have is that the last few rows do not integrate correctly. I have pasted the code I used. The column named "integral" shows the output from the integrate function. The last few rows have no integration results. I tried increasing the doses, number of subjects, etc.... this error occurs

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

I would like to be able, inside a function, to create a new function, and use it as part of a formula as an argument to, say, gnls or nlme. for example: MyTop <- function(data=dta) { Cexp <- function(dose,A,B,m){...} Model <- as.formula(paste("y","~ Cexp(",paste(formals(Cexp),collapse =", "),")")) MyCall <-

Dear All, I want to create a dataset for a NONMEM simulation. I have a dataframe with individual PK parameters and want to create a dosing sceinario in a second dataframe. I want to merge them both so that every individiual's PK parameters are combined with the dosing scenario into one. I do not have a common variable to merge and cbind is giving error that lengths are different. I

Consider the following: library(lattice) library(latticeExtra) temp <- expand.grid( subject = factor(paste('Subject', 1:3)), var = factor(paste('Variable', 1:3)), time = 1:10 ) temp$resp <- rnorm(nrow(temp), 10 * as.numeric(temp$var), 1) ylimits <- by(temp$resp, temp$var, function(x) range(pretty(x))) useOuterStrips(xyplot( resp ~ time

Dear All,   #I have the following code   Dose<-1000 Tinf <-0.5 INTERVAL <-8 TIME8 <-matrix(c((0*INTERVAL):(1*INTERVAL))) TIME7 <-matrix(c((0*INTERVAL):(2*INTERVAL))) TIME6 <-matrix(c((0*INTERVAL):(3*INTERVAL))) TIME5 <-matrix(c((0*INTERVAL):(4*INTERVAL))) TIME4 <-matrix(c((0*INTERVAL):(5*INTERVAL))) TIME3 <-matrix(c((0*INTERVAL):(6*INTERVAL))) TIME2

Hey, everyone, I am using proportional odds model for ordinal responses in dose-response experiments. For some samll data, SAS can successfully provide estimators of the parameters, but the built-in function polr() in R fails. Would you like to tell me how to make some change so I can use polr() to obtain the estimators? Or anyone can give me a hint about the conditions for the existance of MLE

Hi, ?I have a list of files in one of my working directories: "chr17.chunk1.dose.fvd" "chr17.chunk1.dose.fvi" "chr17.chunk1.prob.fvd"? "chr17.chunk1.prob.fvi"? ........... ......... ........ "chr17.chunk10.dose.fvd" "chr17.chunk10.dose.fvi" "chr17.chunk10.prob.fvd" "chr17.chunk10.prob.fvi" And I am

It is frustrating to see the labels I want in the dimensions of a list but not be able to extract those labels into titles for plots generated from component objects. If someone could set me straight, I would appreciate it. For your amusement, I have provided an example of the Byzantine code I am currently using to avoid loops: # Simulate ANOVA type test data sex<-c(rep(1,8),rep(0,8))

Dear list, This may sound silly. What is the right way to change the names of a dataframe? Let's say I have this data frame (dose) with four columns with names "ID", "DOSE", "TIME" "CMT". I want to change "DOSE" to "AMT". So I did names(dose[2])<-'AMT' But nothing happened. The name of the second column is still

I have a dataset with event=death time (from medical examination until death/censoring) dose (given at examination time) Two groups are considered, a non-exposed group (dose=0), an exposed group (dose between 5 and 60). For some reason there is a theory of the dose increasing its effect over time (however it was only given (and measured) once = at the time of examination). I tested a model:

Hello to all, I'm a biologist trying to tackle a "fish" (Poisson Regression) which is just too big for my modest understanding of stats!!! Here goes... I want to find good literature or proper mathematical procedure to calculate a confidence interval for an inverse prediction of a Poisson regression using R. I'm currently trying to analyse a "dose-response"

Dear R-users, I am facing problem with integrating in R a likelihood function which is a function of four parameters. It's giving me the result at the end but taking more than half an hour to run. I'm wondering is there any other efficient way deal with. The following is my code. I am ready to provide any other description of my function if you need to move forward.