similar to: Apparant bug in binomial model in GLM (PR#13434)

I have some questions about the use of weights in binomial glm as I am not getting the results I would expect. In my case the weights I have can be seen as 'replicate weights'; one respondent i in my dataset corresponds to w[i] persons in the population. From the documentation of the glm method, I understand that the weights can indeed be used for this: "For a binomial GLM prior

I wish to generate a vector of uniformly distributed data with a defined correlation to another vector The only function I have been able to find doing something similar is corgen from the library ecodist. The following code generates data with the desired correlation to the vector x but the resulting vector y is normal and not uniform distributed library(ecodist) x <- runif(10^5) y

I ran logistic regression models last week using glm (...,family=binomial) and got a set of results. Since then I have loaded the Epi package for ROC analysis. Now when I run those same models I get completely different results, with most being: Warning message: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart,

I am analysing parasite egg count data and am having trouble with glm with a negative binomial family. In my first data set, 55% of the 3000 cases have a zero count, and the non-zero counts range from 94 to 145,781. Eventually, I want to run bic.glm, so I need to be able to use glm(family= neg.bin(theta)). But first I ran glm.nb to get an estimate of theta: > hook.nb<- glm.nb(fh,

Hi all, Could somebody be so kind to explain to me what is the saturated model on which deviance and degrees of freedom are calculated when fitting a binomial glm? Everything makes sense if I fit the model using as response a vector of proportions or a two-column matrix. But when the response is a factor and counts are specified via the "weights" argument, I am kind of lost as far as

In an effort to select the most appropriate number of clusters in a mixture analysis I am comparing the expected and actual membership of individuals in various clusters using the Fisher?s exact test. I aim for the model with the lowest possible p-value, but I frequently get p-values below 2.2e-16 and therefore does not get exact p-values with standard Fisher?s exact tests in R. Does anybody know

Hello, I am trying to run the APE program COMPAR.GEE with a model containing a categorical response variable and a mixture of continuous and categorical independent variables. The model runs when I have categorical (binary) response and two continuous independent variables (VAR1 and VAR2), but when I include a categorical (binary) independent variable (VAR3), I receive the following output with

Hi I can get all my features by doing this: > logistic.model = glm(similarity ~ ., family=binomial, data = cData[3001:3800,]) I can get the product of all my features by this: logistic.model = glm(similarity ~ . ^ 2, family=binomial, data = cData[3001:3800,]) I don't seem to be able to get polys by doing this: logistic.model = glm(similarity ~ poly(.,2), family=binomial, data

Dear friends, I'm doing a simulation on logistic regression model, but the programs can't work well,please help me to correct it and give some suggestions. My programs: data<-matrix(rnorm(400),ncol=8) #sample size is 50 data<-data.frame(data) names(data)<-c(paste("x",1:8,sep="")) #8 independent variables,x1-x8; #logistic regression model is

Dear R users, I am having some problems with glm. The first is an error message "subscript out of bounds". The second is the fact that reasonable starting values are not accepted by the function. To be more specific, here is an example: > success <- c(13,12,11,14,14,11,13,11,12) > failure <- c(0,0,0,0,0,0,0,2,2) > predictor <- c(0,80*5^(0:7)) >

DeaR R-useRs, I'm trying to fit a logist model with these data: > dati y x 1 1 37 2 1 35 3 1 33 4 1 40 5 1 45 6 1 41 7 1 42 8 0 20 9 0 21 10 0 25 11 0 27 12 0 29 13 0 18 I use glm(), having this output: > g<-glm(y~x,family=binomial,data=dati) Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart =

I got the following warning messages when I did a binomial logit regression using glm(): Warning messages: 1: Algorithm did not converge in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, 2: fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, Can some one share your thoughts on how to

Dear R users I have built the following model m1<-lmer(y~harn+foodn+(1|ass%in%pop%in%fam),family = "quasibinomial") where y<-cbind(alive,dead) where harn and foodn are categorical factors and the random effect is a nested term to represent experimental structure e.g. Day/Block/Replicate ass= 5 level factor, pop= 2 populations per treatment factor in each assay, 7 reps

Estimad en s errer en s He hecho este modelo glm m1.pile<-glm(ger~tem+pot+time+I(tem^2)+I(tem^2):pot ,family="binomial" ,data=long.PILE ) Que nos da la probabilidad de germinación de una semilla en función de tem (Temperatura), pot (Humedad del suelo) y time (Tiempo que la semilla pasa en esas condiciones). Ahora quiero, para diferentes tem, pot

Hello, Suppose one is interested in fitting a GLM with a log link to binomial data. How does R choose starting values for the estimation procedure? Assuming I don't supply them. Thanks, Tyler

Hello R Users, I am having difficulty deleting the last warning message in a loop so that the only warning that is produced is that from the most recent line of code. I have tried options(warn=1), rm(last.warning), and resetting the last.warning using something like: > warning("Resetting warning message") This problem has been addressed in a previous listserve string,

Hello, I was trying a glm fitting (as shown below) and I got a warning and a fitted residual deviance larger than the null deviance. Is this the expected behavor of glm? I would expect that even though the warning might be warranted I should not get worse fitting with an additional covariate in the model. Could anyone tell me what I'm missing? I get the same results in both R2.5.1 on windows

Hi All, I have a problem with weighted logistic regression. I have a number of SNPs and a case/control scenario, but not all genotypes are as "guaranteed" as others, so I am using weights to downsample the importance of individuals whose genotype has been heavily "inferred". My data is quite big, but with a dummy example: > status <- c(1,1,1,0,0) > SNPs <-

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between

I encountered a segfault running glm() and wonder if it could have something to do with the way memory is handled in a dual core system (which I just set up). I'm running R-base-2.4.0-1, installed from the SuSE 10.1 x86_64 rpm (obtained from CRAN). (My processor is an AMD Athlon 64 x2 4800+). The error and traceback are *** caught segfault *** address 0x8001326f2b, cause 'memory not