search for: dosing

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

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 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

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

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"

Hi folks, Win7 64bit R 1.12.0 I run following command on R:- > ToothGrowth > attach(ToothGrowth) > plot(dose,len) > matrics=lm(len~dose) > abline(metrics) Error in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...) : plot.new has not been called yet Only a grey diagram is displayed without content > plot(abline(metrics)) Error in int_abline(a = a, b = b, h = h, v

...et 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 observation (where it takes value 0). The time field is a date-time record for the associated dose / observation event. I'm trying to calculate the time since the last dose for each observation event - to support that, the data I'd like to end up with is...

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

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. >

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

Buenos dias R-help-es, Estoy interesado en estimar una curva dosis-respuesta para un conjunto de datos y para ello, estoy utilizando la libreria "drm". Hasta ahi todo bien. Me gustaria automatizar algunas cosas y el primer paso para ello es la estimacion del modelo. Si la estimacion funciona, todo lo demas funciona; de lo contrario, todo fallara. Tengo algunas lineas que mitigan un

Dear all, I can use the very nice drc package (multdrc()) to model and plot a dataframe containing dose and response values. I can also use predict.drc() to yield response values given a dose. I need to do the opposite, estimate a dose given the response. The general predict documentation seems to say that this is possible, but it does not appear that predict.drc has that capability.

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, I used the glm function in R to fit a dose-response relationship and then have been using dose.p to calculate the LC50, however I would like to calculate the NOEL (no observed effect level), ie the lowest dose above which responses start occurring. Does anyone know how to do this? [[alternative HTML version deleted]]

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

I'm analysing some ID50 data for 2 different groups and had already calculated this by hand using Reed-Muench formula, when I came across the dose.p function in R. I have 2 queries: 1) dose.p gives me a different answer to Reed-Muench, and actually I suspect wrong answer, given that the dose.p result dosage stated to infect 50% is actually stronger than the dose used in my experiments caused

Dear all I need to obtain an estimate of the 50% lethal dose (LD50) from a logistic regression model obtained by applying the glm procedure to some binomial data. The model appears to fit the data very well. I used dope.p from MASS to try and find LD50. The following output appears: > dose.p(iso.glm.logit, cf = c(1,3), p = 1:3/4) Error in dose.p(iso.glm.logit, cf = c(1, 3), p = 1:3/4) :

I tried to post this a few times last week and it seems to have got stuck somehow so I'm trying from a different email in the hope that works. If somehow this has appeared on the list 20 tiems and I never saw any of them I apologize ;-) I'm basically an R-newbie. But I am VERY computer literate. But this has me stumped... All the examples for using the rmeta package to create a

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