similar to: how to estimate dose from respond given drc package result

R-helpers, I recently submitted a help request for the predict.drm function found in the drc package. I am still having issues with the function and I am submitting reproducible code hoping that somebody can help me figure out what is going on. -------- library(drc) # Fit a 4 parameter logistic model to ryegrass dataset fit <- drm(rootl ~ conc, data = ryegrass, fct = LL.4()) summary(fit) #

After a run multdrc comment in R program, show warning sign in this program. I attached the saving page of the script. I hope you could help me, please. Thanks a lot A. Rahbari -------------- next part -------------- R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.0 (2005-04-18), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are

Dear R Community, I am using the package DRC ( to fit a boltzman model to my data. I can fit the model and extract the lower limit, upper limit, and ED50 (aka V50), but I cannot figure out how to get the slope of the curve at ED50. Is there a simple way to do this? I've searched the mailing list and looked through the package documentation, but could not find anything. I am new to r, and

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

R-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

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

I have dose response data I have analyzed with the 'drc' package. Using plot() works great. I want to arrange my plots and source data on a single page. I think 'gridExtra' is the usual package for this. I could use plot() and par(mfrow=...), but then I can't put the source data table on the page. gridExtra provides grid.table() which makes nice graphical tables. It

On Fri, 18 May 2018, Ed Siefker wrote: > I have dose response data I have analyzed with the 'drc' package. > Using plot() works great. I want to arrange my plots and source > data on a single page. I think 'gridExtra' is the usual package for > this. > > I could use plot() and par(mfrow=...), but then I can't put the source > data table on the page. >

Hi! I want to use the DRC package in order to calculate the IC50 value of an enzyme inhibition assay. The problem is that the estimated ED50, is always out of the fitted curve. In the example below, I had a ED50 value of 2.2896, But when I predict the response level for this concentration I get a value of 45.71 instead of the expected value of 50. This is my data: #Dose unit is concentration

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) }

Dear R users, I'm a little lost on how to define pmodels for the DRC package. My goals are to produce isoboles of binary toxicity data. any tips? I really just need to know what pmodels refers to. Cheers, Pat -- View this message in context: http://r.789695.n4.nabble.com/pmodels-in-DRC-tp4190567p4190567.html Sent from the R help mailing list archive at Nabble.com.

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

Quiero comparar varias dosis letales 50% (LD50) usando análisis probit. He seguido un ejemplo que viene en paquete DRC, pero no obtengo el resultado esperado. Lo que quiero es saber si las LD50s, son diferentes y si la diferencias son estadísticamente significativas. Gracias de antemano. José Arturo e-mail. jafarfan@uady.mx <grejon@uady.mx> e-mail alterno. jafarfan@gmail.com

Hi every one, I am using the package 'drc' to model root elongation using dose response data. I don't know which model I should use. Though I don't know which model I should use, I tried the following codes given below. But it produced the error messages.Can any one tell me the code in 'drc' package to find out the EC (Effective Concentration) values and Confidence

Hi, I am currently trying to do dose-response curves using weighted 4-parameter model (4PL). The weighting was based on 1/(expected variance) derived from historical data. I tried both drm() from drc package, and nls(), found very different results derived from drm() vs. nls() using "weights=" argument. d1<-read.table("d1.txt",sep='\t',header=T,row.names=1)

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

Hi, the following R lines work fine in R 2.4.0 alpha (and older R versions), but not in R 2.4.0 beta (details below): library(drc) # to load the dataset 'PestSci' library(nlme) ## Starting values sv <- c(0.328919, 1.956121, 0.097547, 1.642436, 0.208924) ## No error m1 <- nlme(SLOPE ~ c + (d-c)/(1+exp(b*(log(DOSE)-log(e)))), fixed =

I have the following problem. I have measured a dose response curve (binary response, continuous dose) on a grid of x,y positions. I would like to produce a grey-level plot that shows the LD50 at each (x,y) position. I am thinking that I have to do something like fit<-glm(resp ~ x*y + dose, family = binomial) Corrections welcome. But from here I don't know how to get LD50, and certainly

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)

Hi, the following R lines work fine in R 2.4.0, but not in R 2.4.1 or any devel versions of R 2.5.0 (see below for details). library(drc) # to load the dataset 'PestSci' library(nlme) ## Setting starting values sv <- c(0.43355869, 2.49963220, 0.05861799, 1.73290589, 0.38153146, 0.24316978) ## No error m1 <- nlme(SLOPE ~ c + (d-c)/(1+exp(b*(log(DOSE)-log(e)))), fixed =