similar to: pmodels in DRC

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

Dear all, I can use the very nice drc package (multdrc()) to model and plot a dataframe containing dose and response values. I can also use predict.drc() to yield response values given a dose. I need to do the opposite, estimate a dose given the response. The general predict documentation seems to say that this is possible, but it does not appear that predict.drc has that capability.

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 =

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 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

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. >

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

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)

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

Dear R community, I've been writing simple functions for the past year and half and have come across a similar problem several times. The execution of a function within my own function produces NaN's or fails to execute as intended. My conundrum is that I can execute the function outside of my function without error, so it's difficult for me, as a novice functioneer, to figure out

Hello everyone, I need to do a sample size calculation. The study two arms and two endpoints. The two arms are two different cancer drugs and the two endpoints reflect efficacy (based on progression free survival) and toxicity. Until now, I have been trying to understand this in terms of a one-arm design, where the acceptable rate of efficacy might be 0.40, the unacceptable rate of efficacy

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

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 =

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) #

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, 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 =

Dear all, I tried help.search("karber") and RSiteSearch("karber") and RSiteSearch("*karber*") to find whether the (trimmed) Spearman-Karber method for LD50 evaluation in toxicity data (e.g. according to Hamilton 1977) has been implemented in R. Or does this method feature under a different name? Of course logit and probit are doable, but Spearman-Karber seems to