similar to: Problem with 'nls' fitting logistic model (5PL)

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, 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

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

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

Hello, I have made an R script that prepares a NONMEM dataset and I would like to start the NONMEM run right after the dataset is ready. I am using windows XP, R 2.9.1 and NONMEM 6. I have prepared a run.bat file that looks like this: ---------------------------------------- call K:\nmvi\NMdirectories.bat call K:\nmvi\nmfe6 "path\control.txt" "path\output.txt"

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

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

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-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

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

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 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

HI I'm a student in chemical engineering, and i have to implement an algoritm about FIVE PARAMETERS INTERPOLATION for a calibration curve (dose, optical density) y = a + (c - a) /(1+ e[-b(x-m]) where x = ln(analyte dose + 1) y = the optical absorbance data a = the curves top asymptote b = the slope of the curve c = the curves bottom asymptote m = the curve X intercept Have you never seen

Dear R-Helpers, I have a problem with displaying the greek "beta" symbol in PDF files using Cairo library - it displays as an empty box. The same also happens for a dash symbol in subscript. Both symbols are displayed correctly if the plot is produced on screen (outside of CairoPDF). The syntax that I use for it in plot command is:

Dear group, Here is my df : pose16 <- structure(list(DESCRIPTION = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 12L), .Label = c("COPPER May/10", "COTTON NO.2 Jul/10", "CRUDE OIL miNY May/10", "GOLD Jun/10", "ROBUSTA COFFEE (10) Jul/10", "SOYBEANS Jul/10", "SUGAR NO.11 Jul/10", "SUGAR NO.11 May/10",

Hello, I can`t figure out how to include a text file for use in an example in the .Rd help files of an R package I am writing. I want the examples section of one help file to look like this: \examples{ # Load the JAGS model mod <- jags.model("model.txt") } The function jags.model tries to read in the file model.txt. So far every time I run R CMD check, I get an error message:

If you are interested in the following position, please contact Vladimir Svetnik at Vladimir_svetnik at merck.com. Date: Nov 18, 2015 Location: Rahway, NJ, US Associate Principal Scientist, Biostatistics-BIO004396 Description Merck & Co., Inc. Kenilworth, N.J., U.S.A. known as Merck in the United States and Canada, is a global health care leader with a diversified portfolio of prescription

Hello, I am trying to fit my Elisa results (absorbance readings) to a standard curve. To create the standard curve model, I performed a 4-parameter logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me the following: > FourP A 'drc' model. Call: drm(formula = Response ~ Expected, data = SC, fct = LL.4()) Coefficients: b:(Intercept) c:(Intercept)