similar to: glm problem

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)) >

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

I have a very simple problem. When using glm to fit binary logistic regression model, sometimes I receive the following warning: Warning messages: 1: fitted probabilities numerically 0 or 1 occurred 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,

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 =

Hello R users I ran more than 100 logistic regression analyses. Some of the analyses gave me this kind warning below. ########################################################### 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,

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 feeling my way in the use of GLM's and have come across a warning whilst manually simplifying a model with interaction terms, removing terms one at a time from the maximum model (R1.9.0). > model<-glm(midpnts~(AET+tempave+tempvar+MDE+sqrtarea)^2+Lat,family=poisson,weights=weightS) > model2<-update(model,~.-tempave:tempvar) Warning message: fitted rates numerically 0

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

Hello everybody, I'm new to MCMClogit. I'm trying to use MCMClogit to fit a logistic regression model but I got some warnings I can't understand. My input data X is 32(tissue sample)*20(genes) matrix, each element in this matrix corresponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer)

G'day all, I had a look at the GLM code of R (1.4.1) and I believe that there are problems with the function "glm.fit" that may bite in rare circumstances. Note, I have no data set with which I ran into trouble. This report is solely based on having a look at the code. Below I append a listing of the glm.fit function as produced by my system. I have added line numbers so that I

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

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,

Bug summary: glm() causes a segfault if the argument 'data' is a data frame with more than 16384 rows. Bug demonstration: -------input --------------- N <- 16400 df <- data.frame(x=runif(N, min=1,max=2),y=rpois(N, 2)) glm(y ~ x, family=poisson, data=df) ------ output --------------- *** caught segfault *** address (nil),

Dear all, I am encountering some issues with my data and need some help. I am trying to run glm analysis with a presence/absence variable as response variable and several explanatory variable (time, location, presence/absence data, abundance data). First I tried to use the glm() function, however I was having 2 warnings concerning glm.fit () : # 1: glm.fit: algorithm did not converge # 2:

R 2.8.1 windows XP I am getting an error message that I don't understand when I try to run GLM. The error only occurs when I have all independent variables in the model. When I drop one independent variable, the model runs fine. Can anyone help me understand what the error means and how I can correct it? Thank you, John > fit11<-glm(AAMTCARE~BMI+BMIsq+SEX+jPHI+jMEDICAID+factor(AgeCat)+

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

Hello to everyone. I need your help. I´m trying to fit the same *glm.nb* to a different data set and i am getting these errors in some of the data. Sometimes, one data set has two of these errors when fitting the model. 1.- Error en while ((it <- it + 1) < limit && abs(del) > eps) { : valor ausente donde TRUE/FALSE es necesario 2.- Mensajes de aviso perdidos 1: In sqrt(1/i)

glm(S ~ -1 + Mdif, family=quasipoisson(link=identity), start=strt, sdat) gives error: > Error in glm.fit(x = X, y = Y, weights = weights, start = start, etastart > = > etastart, : > cannot find valid starting values: please specify some strt is set to be the coefficient for a similar fit glm(S ~ -1 + I(Mdif + 1),... i.e. (Mdif + 1) is a vector similar to Mdif. The error

Hi everyone, BEGINNER question: I get the error below when running hier.part. Probably i´m doing something wrong. Error in glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : object 'fit' not found In addition: Warning messages: 1: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : no observations informative at iteration 1

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,