similar to: Samba4 + NFS server

Hi everyone Version 4.0.0alpha18-GIT-bfc7481 openSUSE 12.1 Conventional nfs4 export works fine, but I'm having trouble kerberizing it for Samba 4 for my Samba 4 users. I've setup the nfs4 pseudo stuff like this: hh3:/ # mkdir /export hh3:/ # mkdir /export/home hh3:/ # mount --bind /home /export/home Here is /etc/exports: /export

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

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

Hello, I'm trying to reproduce some SAS result wit R (after I got suspicious with the result in R). I struggle with the contrasts in a linear model. I've got three factors > d$dose <- as.factor(d$dose) # 5 levels > d$time <- as.factor(d$time) # 2 levels > d$batch <- as.factor(d$batch) # 3 levels the data frame d contains 82 rows. There are 2 to 4 replicates of

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

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))

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 am fitting a Poisson mixed effects model. I have the number of eggs (Eggs) laid by a quail and looking at the effect of dosage of a chemical (Dose) in the study. I have counts of eggs laid by week of the study, so I am including the week number (Week) as a random effect. I'm using the lme4 package. I have, > mod1 <- lmer(Eggs~Dose + (1|Week),family=poisson) >

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

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

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 <-

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"

This relates to a message from Christophe Pallier to r-help some time ago. Like myself, he finds aggregate very useful, but the interface a little cumbersome. I've implemented a more compact formula interface, found at the bottom of this message: data(ToothGrowth) # I used to aggregate like this: aggregate(list(len=ToothGrowth$len),

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 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,

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. >

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 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