similar to: Using string vectors as for loop arguments

I'm stumbling my way through manipulating data in multiply imputed datasets, and have run into a problem translating code I used to run on my pre-imputed dataset to multiple datasets. The imputation runs just fine, as does the reading of the mi data sets into an imputationList. I run into trouble, though, when I try to construct a scale across all the data sets. Is there a simple way to do

Hi, I need to create a text file in the following format, > 1 100.0 0 > 0 0 > 1 1 > 0 0 > 1 1 > # > 1 100.0 0 > 0 0 > 0 1 > 1 0 > 1 1 ... where # is part of the format and not a R comment. Each block (delimited by #) consists of a first line with three values, call it dose, and a list of (x,y) coordinates which are a matrix or data.frame, >

Hallo Everybody How do you specify arguments for a function used within another function? Here is my problem: I am reconstructing a calculator for the burden of disease due to air pollution from publications and tools published by the WHO. The calculations make use of published dose-response relationships for particular health end-points. This is then applied to populations with known or

As part of a larger function I have code similar to the reduced example below. The user is instructed to choose a file, which gets read using read.csv. In this example, I just have the name of the file print out. When I call this function with choosefile(), the file dialog box appears before the first cat line is printed. After I choose a file, both cat lines are printed. choosefile

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.

Hi i would like to use some graphs or tables to explore the data and make some sensible guesses of what to expect to see in a glm model to assess if toxin concentration and sex have a relationship with the kill rate of rats. But i cant seem to work it out as i have two predictor variables~help?Thanks.:) Here's my data. >

Hi, I have a data set in which the variable 'dose' is time-varying. Currently, the data set is in a long format, with 1 row for each time unit of follow-up for each individual "Id". It looks like this: orig.data <- cbind(Id = c(rep(1,4), rep(2,5)), time = c(1:4, 1:5), dose = c(1,1,1,0,1,0,1,1,0)) orig.data Id time dose [1,] 1 1 1 [2,] 1 2 1 [3,] 1

All, I am constructing a pharmacokinetic dataset and have hit a snag. The dataset can be demonstrated in the following way: myData <- data.frame( evid = c(1, 0, 0, 0, 1, 0, 1, 1, 1, 0), time = 1:10, last.dose.time = c(1, 1, 1, 1, 5, 5, 7, 8, 9, 9) ) The evid field is an indicator variable for whether the associated observation is a dosing record (when it takes value 1) or an

Dear R-user, I'm having some difficulty with working PFIM 3.2, a package for implementing population PK/PD in R. I wish to evaluate the determinant of Fisher information matrix each time with successive dose from a pre defined sequence of doses and want to store those values in a vector. It's important to note that in my 'stdin.r' file, dose<-c(u) and each time u is to be

Hi, I am working with R version 2.1.0, and I seem to have run into what looks like a bug. I get the same error message when I run R on Windows as well as when I run it on Linux. When I call anova to do a LR test from inside a function, I get an error. The same call works outside of a function. It appears to not find the right environment when called from inside a function. I have provided

Dear R-experts, I have the following "long" data frame: ID <-

hi List and Manuel, I have encounter the following problem with the function "lineplot.CI".? I'm running R 2.10.1, sciplot 1.0-7 on Win XP.? It seems like it's a scoping issue, but I couldn't figure it out. Thanks! ...Tao > lineplot.CI(x.factor = dose, response = len, data = ToothGrowth)??? ## fine > lineplot.CI(x.factor = dose, response = len, data = ToothGrowth,

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