similar to: Starting NONMEM (nmfe6) from R

Hi I am having some problems running a covariate analysis with my colleage using R with the NonMem program we are using for a graduate school project. R and NonMem run fine without adding in the covariates, but the program is giving us a problem when the covariate analysis is added. We think the problem is with the R code to run the covariate data analysis. We have the control stream, R code

Dear R-helpers, This may sound simple to you, but I'm a beginner in this, so please be forgiving. I have a following problem: two analytes were measured in patient's blood on 4 occasions: ProteinA and ProteinB. How to correctly evaluate correlation between ProteinA and ProteinB? I tried: x <- data.frame(Patient.ID=rep(1:10, each=4), Visit=rep(c(1:4),10), ProteinA=rnorm(m=10,

Dear R-Helpers, I am a novice in survival analysis. I have the following code: for (i in 3:12) print(coxph(Surv(time, status)~a[,i], data=a)) I used it to fit the Cox Proportional Hazard models separately for every available parameter (columns 3:12) in my data set - with intention to compare the Hazard Ratios. However, some of my variables are in range 0.1 to 1.6, others in range 5000 to

Dear R-helpers, I am working with 'cph' models from 'rms' library. When I build simple survival models, based on 'Surv(time, event)', everything is fine and I can make nice plots using plot(Predict(f, time=3)). However, recently I tried to be more specific and used 'Surv(start, stop, event)' type model. Using this model 'plot(Predict(f))' works OK, but

Dear R-Helpers, I'm working with immunoassay data and 5PL logistic model. I wanted to experiment with different forms of weighting and parameter selection, which is not possible in instrument software, so I turned to R. I am using R 2.14.2 under Win7 64bit, and the 'nls' library to fit the model - I started with the same model and weighting type (1/y) as in the instrument to see

Dear R-helpers and graphics gurus, I have two problems with plotting confidence bands: 1. First is relatively simple. I am using the Passing-Bablok procedure to obtain "unbiased" regression coefficients. This procedure yields the "a" & "b" coefficient values along with their confidence intervals. I then plot the raw data with the regression line, but I would

Dear R-helpers I have a data set of roughly 10 million records, 7 columns. It has only about 500MB as a csv, so it fits in the memory. It's painfully slow to do anything with it, but it's possible. I also have another dataset of covariates that I would like to explore - with about 4GB of data... I would like to merge the two datasets and use lmer to build a mixed effects model. Is

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of

Dear R-helpers I am running a simple mixed effects model using lme(). The call looks like this: fit <- lme(Analyte~Sample, data=Data, random=~1 | Run) I am particularly interested in the estimated random effects. When I print the 'fit' object, it looks something like example below: (...) Random effects: Formula: ~1 | Run (Intercept) Residual StdDev: 3.483794 3.637523

Hello everyone, I am new to R, I have been using SAS for a while. Not surprisingly, I find R much better in graphics, which is publication ready right away. Recently, I have been trying to calculate some basic statistics using R. I have a dataset of multiple rows per subject. For example: Subject Date Factor1 Factor2 Factor3 P1 0.5 1 1 3 P1 1 3 2 5 P1 2 3 5 NA ... P2 0.5 1 6 4 P2 1 2 NA 7 P2

Dear group, I am trying to prepare a NONMEM friendly dataset for population PK analysis. My patient IDs are 10 digit long and NONMEM is losing precison and rouding the last couple of digits. I need to generate unique Patient IDs fromt he current 10-digit IDs. Ihave total 250 subjects so I appreciate if anybody can suggest me a way to code this in R. Regards, Ayyappa [[alternative HTML

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

Dear R-users, Do you know if we can use the function lme in R for log-normal distribution of parameters as used in Nonmem ? theta=theta0*exp(eta) In our model, the parameters follow the log-normal distribution so it's not reasonable to deal with normal distribution which gives us negative values in simulation Thanks for your help, Thu [[alternative HTML version deleted]]

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

Greetings, I used latex type code in my Rd files. The pdf version using R CMD Rd2dvi --output=PKtools.pdf --pdf --title="PKtools" PKtools man came out quite nice. However, my current HTML version does not tex the latex so there is latex code in the files and looks bad. example problem code: AIC$_c$ or \begin{itemize} \item NLME: \begin{itemize} \item population level:

Hello R-helpers, I have a problem with formatting a single number to show leading zeros. For example, I want "2" displayed as "002". My numbers have 1 to 3 digits and I would like them all to display 3 digits for printing. I know I could use "paste" in a loop with several "if"s, but I was wondering if there is a single function that can do this. I have

I think the argument supporting the use of bootstrap to determine coefficients, as opposed to just running linear regression on the whole dataset, is the comparison of Rsq and prediction errors between these two approaches - page 1502. There's a substantial difference in favor of the bootstrap approach. -- Michal J. Figurski Gustaf Rydevik wrote: > The url for the mentioned paper is

Hi Everyone: I am running NONMEM from R using following command. shell("nmfe72 TOT_EST_DOSE2.CTL DOSE2.OUT",wait=T) This command runs NONMEM successfully but does not show the progress. Usually, intermediate outputs are displayed on CMD console when this same command is run from CMD console. How can I view the intermediate outputs while running NONMEM from R?

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

Dear R-helpers, I have a dataset named "qu", organized as follows: Sample Run Replicate Value 1 1 1 25 1 1 2 40 1 1 3 33 1 1 4 29 1 2 1 37 1 2 2 44 1 2 3 45 1 3 1 25 1 3 2 40 1 4 1 33 1 4 2 29 1 4 3 25 2 ... Basically, a sample was run on an assay multiple times within a single day. Each of these results is "Replicate". Then run was repeated several times in