similar to: ED50 calculation in drc package

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

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

I am using semiparametric Model  library(mgcv) sm1=gam(y~x1+s(x2),family=binomial, f) How should I  find out standard error for ed50 for the above model ED50 =( -sm1$coef[1]-f(x2)) / sm1$coef [2]   f(x2) is estimated value for non parametric term.   Thanks [[alternative HTML version deleted]]

Hi R-Users, I have plotted a region whose polygon coordinates are given in shp format ED50 UTM (zone=30) ) using "readShapePoly" in library(maptools). Now I need to plot a set of points in that region (my.dataframe, with X and Y geographic coordinates), which have been read using GPS in Longitud-Latitud form (using WGS84 system), so I first need to convert these Longitud-Latitud data

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 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, 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]]

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

Hello everyone, I have biological data from a competition experiment where a free ligand is titrated against the binding of a protein. Now, I would like to fit a standard on-site binding curve to this data in order to obtain the IC50 and Kd values. Unfortunately I have not been able to find a package/function which allows such a fitting and calculates the results. Does anyone know if there is

Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple. In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two

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)

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

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(

Hi, I was wondering if someone could please help me. I am doing a logistic regression to compare size at maturity between 3 seasons. My model is: fit <- glm(Mature ~ Season * Size - 1, family = binomial, data=dat) where Mature is a binary response, 0 for immature, 1 for mature. There are 3 Seasons. The Season * Size interaction is significant. I would like to compare the size at 50%

Classification: UNCLASSIFIED Caveats: NONE A similar question has been posted in the past but never answered. My question is this: for probit analysis, how do you program a 95% confidence interval for the LD50 (or LC50, ec50, etc.), including a heterogeneity factor as written about in "Probit Analysis" by Finney(1971)? The heterogeneity factor comes into play through the chi-squared

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. I used coxph(surv(start,end,event)~~event,data) to deal with interval censor data. Does anyone know similar samples using coxph(surv(start,end,event)~~event,data)? If you knows, can you tell me? I'll really appreciate it. Thank you very much R learner.

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

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

G'day, My problem is I'm not sure how to extract effect sizes from a nonlinear regression model with a significant interaction term. My data sets are multiple measurements of force response to an agonist with two superimposed treatments each having two levels. This is very similar to the Ludbrook example in Venables and Ripley. The experiment is that a muscle is exposed to an agonist