search for: ld50

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for...

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...

At a snail's pace I keep on translating an introduction to R into italian; I have reached the section describing the glm() function, in which some example code is presented. The very last line of code, before the beginning of the section on Poisson models is: ldp <- ld50(coef(fmp)); ldl <- ld50(coef(fmp)); c(ldp, ldl) which of course gives results 43.663 and 43.663; the correct code should be: ldp <- ld50(coef(fmp)); ldl <- ld50(coef(fml)); c(ldp, ldl) which gives the expected results 43.663 and 43.601 -- Michele Alzetta -.-.-.-.-.-.-.-.-.-.-.-.-....

At a snail's pace I keep on translating an introduction to R into italian; I have reached the section describing the glm() function, in which some example code is presented. The very last line of code, before the beginning of the section on Poisson models is: ldp <- ld50(coef(fmp)); ldl <- ld50(coef(fmp)); c(ldp, ldl) which of course gives results 43.663 and 43.663; the correct code should be: ldp <- ld50(coef(fmp)); ldl <- ld50(coef(fml)); c(ldp, ldl) which gives the expected results 43.663 and 43.601 -- Michele Alzetta -.-.-.-.-.-.-.-.-.-.-.-.-....

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 test for homogeneity and is equal to h=chi^2/(k-2), where k is the number of doses and k-2 are the degrees of fr...

Hi All! I am desperately needing some help figuring out how to calculate LD50 with a GLMM (probit link) or, more importantly, the standard error of the LD50. I conducted a cold temperature experiment and am trying to assess after how long 50% of the insects had died (I had 3 different instars (non significant fixed effect) and several different blocks (I did 4 replicate...

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 the limits. Response<-c(0,7,26,27,0,5,13,29,0,4,11,25) Tot<-rep(30.5,12) Dose<-rep(c(10,40,160,640),3) probit<-glm(formula = Respons...

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 250µ...

...om 20 pigs, each of which is tested at crossed 9 doses (logdose -4:4) and 3 skin treatment substances when exposed to a standard polluted environment. So there are 27 patches on each pig. The response is irritation=yes/no. I want to determine "equally effective 50% doses" (similar to old LD50), and to test the treatments against each other. I am looking for something like dose.p in MASS generalized to lmer (or glmmPQL or whatever). The direct as output by lmer are not useful, because saying "30% irritation with A and 40% with B at dose xx" has less meaning than giving "eq...

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...

Hi, I'm not yet familar with GLM and still learning. How can I perform a BNL (to estimate LDp values) with matrixes like this (N indicates the observed objects): data from: Kerr D, Meador J. 1996. "Modeling Dose Response Using Generalized Linear Models". Environ Toxicol Chem 15(3) conc respond n 0 0 20 0 1 19 0 1 20 0 0 20 13.5 5 23 13.5 2 20 29 9 20 29 6 20 53 12 20 53 15 20 85

Dear all I need to obtain an estimate of the 50% lethal dose (LD50) from a logistic regression model obtained by applying the glm procedure to some binomial data. The model appears to fit the data very well. I used dope.p from MASS to try and find LD50. The following output appears: > dose.p(iso.glm.logit, cf = c(1,3), p = 1:3/4) Error in dose.p(iso.glm.lo...

The core team has decided to release R 1.5.0 on April 29th. Somewhat earlier than maybe expected, but we realized that we needed a phase-shift away from our usual cycle with main releases in June and December since it was placing feature freezes just when several members were in a creative phase due to end of teaching. The roadmap is as follows April 8 feature freeze on r-base April 15 code

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 not at each x,y, position. Thanks very much for any help. Bill

Hello, I am trying to determine LD50 and LD95 using dose.p in MASS however some of the Residual variance is larger than the degrees of freedom. Please can anyone help with any advice as to how i can correct for this? Here is the model as inputted into R y<-cbind(dead,n-dead) model<-glm(y~log(conc),binomial) summary(model)...

Hello, I am trying to fit a model to some "death over time" data that does not fit the criteria for the usual LD50 type models (the counts are too large). I am using a simple linear model in an attempt to plot a nice line on a scatter plot and calculate some LD values to use in designing an experiment. Here is the basic idea of what I'm doing: head(mort) Time Density 0 2233333333 0 21000...

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

Hello to all, I'm a biologist trying to tackle a "fish" (Poisson Regression) which is just too big for my modest understanding of stats!!! Here goes... I want to find good literature or proper mathematical procedure to calculate a confidence interval for an inverse prediction of a Poisson regression using R. I'm currently trying to analyse a "dose-response"

Hello all, I have a very simple set of data and I would like to analyze them with probit analysis. The data are: X Event Trial 100 8 8 75 8 8 50 6 8 25 4 8 10 2 8 0 0 8 I want to estimate the value of X that will give a 95% hit rate (Event/Trial) and the corresponding 95% CI. Anyone can offer some help? Thanks!! -

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 me to be the way I should go with our data. Thanks for all your help, Susanne Ins...