similar to: Converting a data set from 'long' format to 'interval' format

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, >

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

R-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

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. >

Classification: UNCLASSIFIED Caveats: NONE A similar question has been posted in the past but never answered. My question is this: for probit analysis, how do you program a 95% confidence interval for the LD50 (or LC50, ec50, etc.), including a heterogeneity factor as written about in "Probit Analysis" by Finney(1971)? The heterogeneity factor comes into play through the chi-squared

Hi Everyone, I am trying to understand the unstack() function but after struggling for two days, I have given up. More specifically, I am trying the exercises at the end of Chapter 1 of Data Analysis and Graphics Using R by Maindonald and Braun, 2nd ed. Exercise 18 (p. 41) asks to unstack the Rabbit data frame from the MASS package to get a certain data frame that is shown in the exercise.

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,