similar to: one-site competition data // curve fitting

Dear Group, I am an economics student starting with PhD work in London. As preparation I would like to get to know R a little bit better. For Stata there are tons of books, however, can you recommend a book for R? I have some substantiated econometrics knowledge, so it should be more a how-to book. Best regards Thiemo --- Thiemo Fetzer, Economist http://freigeist.devmag.net

Dear list, Has anyone created a version of SSmicmen that allows testing for group differences? The basic Michaelis-Menten equation is: (Bmax * X) / (Kd + X). The nlme package allows modeling of random effects for Bmax and Kd as needed, but I curious how I can build in group differences? I have receptor binding data for strains of mice, and following Pinheiro and Bates' lead in their

I'm attempting to calculate a regression in R that I normally use Prism for, because the formula isn't pretty by any means. Prism presents the formula (which is in the Prism equation library as Heterologous competition with depletion, if anyone is curious) in these segments: KdCPM = KdnM*SpAct*Vol*1000 R=NS+1 S=(1+10^(X-LogKi))*KdCPM+Hot a=-1*R b=R*S+NS*Hot+BMax c = -1*Hot*(S*MS+BMax) Y

Hello to the whole group. I am a newbie to R, but I got my way through and think it is a lot easier to handle than other software packages (far less clicks necessary). However, I have a problem with respect to the summary of regression results. The summary function gives sth like: Residuals: Min 1Q Median 3Q Max -0.46743 -0.09772 0.01810 0.11175 0.42252

Hi, I am trying to use drc package to calculate IC50 value. The ED50 calculated in some models (LL4 for example) as a response half-way between the upper and lower limit, which is the definition of the relative IC50 value. Does that mean the ED50 in drc package is IC50? How the ED function in drc package distinguish to estimate ED or IC values? Thanks a lot [[alternative HTML version

Hello, I was wondering if there is an R-package to automatically calculate the IC50 value (concentration of a substrance that inhibits cell growth to 50%) for some measurements. kind regards, Arne [[alternative HTML version deleted]]

Hi, I am newbie of R. I a currently using multdrc object to generate fitting curve and IC50. My 384 well format raw data contains multi dose response curves. My script goes through set of data then produce curve and ic50. Here is my sudo code: For (plateid in platelist) { Input data (plateid) as matrix Curve fitting model4logistic <- multdrc(rdata ~ ld, logDose=10) }

Hello dear R users! I know this question is not strictly R-help, yet, maybe some of the guru's in statistics can help me out. I have a sample of data all from the same "population". Say my regression equation is now this: m1 <- lm(y ~ x1 + x2 + x3) I also regress on m2 <- lm(y ~ x1 + x2 + x3 + x4) The thing is, that I want to study the effect of

Halo i'm studying chemistry, today we made an experiment and i have to draw a titration kurve for my mess data. we can do it on a mm paper, or we can also use a programe. people from chemistry recomend "R" last year i studied civil eng. and we used Matlab, as I see, R ist very similar to it, but its got other comands. But i think R would be a good help for some exercises. so my

Hello, We use drc to fit dose-response curves, recently we discovered that there are quite different standard error values returned for the same dataset depending on the drc-version / R-version that was used (not clear which factor is important) On R 2.9.0 using drc_1.6-3 we get an IC50 of 1.27447 and a standard error on the IC50 of 0.43540 Whereas on R 2.7.0 using drc_1.4-2 the IC50 is

Respected Sir/Madam, I have a question regarding calculation of LD50 (Lethal Dose) and IC50 (50% inhibitory concentration) of an antimicrobial experiment. I have used a compound isolated from a plant and observed its effect on the fungus *Fusarium oxysporum* by the food poisoning method. Solutions of the compound at concentrations of 0, 50, 100, 150, 200 and 250µg/ ml were added to

Hi R users, I have a question about adding uncertainty bars to stacked bar plots. DF: year A B C Amin Amax Bmin Bmax Cmin Cmax 2009 40 45 15 30 61 23 56 14 17 2010 36 41 23 26 54 22 51 22 24 I use the code below: DF.refm = melt(subset(DF[,c(1:4)]),id.vars='year',variable_name='Legend') fig1 =

> > Hi, > > I used > GDALinfo("MOD13Q1.A2001049.h13v11.005.2007002215512.250m_16_days_EVI.tif") and > got the results: > > rows 10 > columns 11 > bands 1 > origin.x 150701.4 > origin.y 7744897 > res.x 250 > res.y 250 > ysign -1 > oblique.x 0 > oblique.y 0 > driver GTiff >

If anyone has a list of application areas where there is extensive use of R, I'd like to hear of it. My current short list is: Bioinformatics Epidemiology Geophysics Agriculture and crop science John Maindonald Mathematical Sciences Institute, Australian National University. john.maindonald at anu.edu.au

On 18/06/17 12:10, lily li wrote: > Hi R users, > > I have a question about adding uncertainty bars to stacked bar plots. > > DF: > year A B C Amin Amax Bmin Bmax Cmin Cmax > 2009 40 45 15 30 61 23 56 14 17 > 2010 36 41 23 26 54 22 51 22 24 > > I use the code below: > >

Hi all! I'm developing a shiny app and I have problems when I wanna write a .txt file. First of all, I change the directory in order to work in a temporal one: wd <- tempdir() setwd( wd ) res.path <- paste0( wd, "/OUT/" ) dir.create( res.path ) Just before calling the function that fails, I remove, if exist, the old files of the directory: file.remove( paste0(

I hope you will forgive me this simple question on titration. I'm trying to find very small values from the algorithm below, which I believe is correctly formatted, and the constants are also correct. When SID goes over ATOT, fitted vales are much too low compared to the literature. I guess I must be using optimize in a wrong way but cannot find out how to improve it. I'm on windows, R

To estimate saturation binding parameters Bmax and Kd in a receptor saturation binding experiment, I use the following nonlinear equation and the nls() function: bmax*X*dummy ------------ + ns*X + background = total binding kd+X where X is concentration, and dummy is an indicator to allow shared estimation of the nonspecific binding parameter ns. This equation describes two fitted

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Hi Andi, This is a set of updates for the firstfloor patch queue. Quick rundown: revert-mm-x86_64-mm-account-for-module-percpu-space-separately-from-kernel-percpu.patch separate-module-percpu-space.patch Update the module percpu accounting patch fix-ff-allow-percpu-variables-to-be-page-aligned.patch Make sure the percpu memory allocation is page-aligned