similar to: glm + error

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Hi, I had a logit regression, but don't really know how to handle the "Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos)" problem. I had the same logit regression without weights and it worked out without the warning, but I figured it makes more sense to add the weights. The weights sum up to one. Could anyone give me some hint? Thanks a lot!

Full_Name: Alberto Gaidys Version: 2.9.0 OS: Mac OS X 10.5.7 Submission from: (NULL) (201.81.185.155) When calling LM or GLM from within a function, R gives a message error that it can't find the specified weights object "Erro em eval(expr, envir, enclos) : objeto 'W' n?o encontrado" (Error in eval(expr, envir, enclos) : object 'W' not found). The call from within

Dear all, I am dealing with the following (apparently simple problem): For some reasons I am interested in passing variables from a dataframe to a specific environment, and in fitting a standard glm: dati<-data.frame(y=rnorm(10),x1=runif(10),x2=runif(10)) KK<-new.env() for(i in 1:ncol(dati)) assign(names(dati[i]),dati[[i]],envir=KK) #Now the following two lines work correctly:

Hi there I'm trying to fit a logistic regression model to data that looks very similar to the data in the sample below. I don't understand why I'm getting this error; none of the data are proportional and the weights are numeric values. Should I be concerned about the warning about non-integer successes in my binomial glm? If I should be, how do I go about addressing it? I'm

I am trying to use R to do a weighted GAM with PA (presence/random) as the response variable (Y, which is a 0 or a 1) and ASPECT (values go from 0-3340), DEM (from 1500-3300), HLI (from 0-5566), PLAN (from -3 to 3), PROF (from -3 to 3), SLOPE (from 100-500) and TRI (from 0-51) as predictor variables (Xs). I need to weight each observation by its WO value (from 0.18 to 0.98). I have specified the

I've redirected this reply from r-help to the bugs list. On 11/3/2006 8:25 AM, vito muggeo wrote: > Dear all, > I am dealing with the following (apparently simple problem): > For some reasons I am interested in passing variables from a dataframe > to a specific environment, and in fitting a standard glm: > > dati<-data.frame(y=rnorm(10),x1=runif(10),x2=runif(10)) >

Hello dear r-gurus! I have a question about the logit-model. I think I have misunderstood something and I'm trying to find a bug from my code or even better from my head. Any help is appreciated. The question is shortly: why I'm not having same coefficients from the logit-regression when using a link-function and an explicite transformation of the dependent. Below some details. I'm

Hello All, I have a question about glm in R. I would like to fit a model with glm function, I have a vector y (size n) which is my response variable and I have matrix X which is by size (n*f) where f is the number of features or columns. I have about 80 features, and when I fit a model using the following formula,? glmfit = glm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13

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

Dear R-Help I 'm using GLM function to Modelling. But when I used Gamma Family in GLM, then I can't run. It was error > glm(DamageRatio~MinTEMP+MaxTEMP+DayRain+Group1+Group2+Group3+Year,family=Gamma()) Error in eval(expr, envir, enclos) : Non-positive values not allowed for the gamma family Can Gamma Distribution use data begin 0 ? and then when I used GLM in S-Plus Program then

Hi, I have a large matrix (SNPs) that I want to cycle over with logistic regression with interaction terms. I have made a loop but I am struggling to identify to the formula the name of the column in a way which is meaningful to the formula. It errors becasue it is not evaluated proporly. (below is a pilot with only 7 to 33 columns, my actual has 200,000 columns) My attempts: for (i in 7:33)

Folks: I'm having trouble doing a forward variable selection using step() First, I fit an initial model: fit0 <- glm ( est~1 , data=all, subset=c(n>=25) ) then I invoke step(): fit1 <- step( fit0 , scope=list(upper=est~ pcped + pchosp + pfarm ,lower=est~1)) I get the error message: Error in eval(expr, envir, enclos) : invalid 'envir' argument I looked at the

Hi R-people: When I use the command to fit a model with an intercept, only: glm ( formula=haspdata ~ 1, data=dat, family=binomial, weights= dat$hy.wgt.s, subset=(dat$haspdat0!=3) ) I get the message: Warning message: non-integer #successes in a binomial glm! in: eval(expr, envir, enclos) Does anyone know what this means?? The data for this command is listed below. Thanks, Phil Smith CDC

So, consider the following: > example(glm) > g = function(model) { w = runif(9);glm(model,weights=w); } > g(counts ~ outcome + treatment) Error in eval(expr, envir, enclos) : object "w" not found Huh?! I suspect that somebody is lazily evaluating arguments in the wrong environment (probably GlobalEnv in this case). I'm willing to accept the fact that there's

Dear all, I am using glm with quasibinomial. What does the following error message mean: Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1 Does it mean that the predictor variable should only have zero and one or it is also possible to have continuous values between zero and one? Many thanks, Samuel [[alternative HTML version deleted]]

I'm trying to plot an extrapolated logistic sigmoid curve using glm(..., family=binomial) as follows, but neither the fitted() points or the predict()ed curve are plotting correctly: > year <- c(2003+(6/12), 2004+(2/12), 2004+(10/12), 2005+(4/12)) > percent <- c(0.31, 0.43, 0.47, 0.50) > plot(year, percent, xlim=c(2003, 2007), ylim=c(0, 1)) > lm <- lm(percent ~ year)

Hello all: I've been given the following data and have been asked to run a logit model using glm(). The variable, Y, is a proportion ranging from 0 to 1, X is a covariate. Without a base number of observations from which Y is computed as a proportion, I believe there is not sufficient information. If I try the model below, R seems to grumble with a complaint. glm(cbind(Y,1-Y) ~ X,

Dear all Could someone explain to why weights does not works in lm and glm in the example below? Thanks in advance, Roseli. function(model){ www<-fitted(model) lm(formula(model),weights=www) } The message error is: Error in eval(expr,envir,enclos): object "www" not found. [[alternate HTML version deleted]]

Good morning, I am using R to try to model the proportion of burned area in Portugal. The dependent variable is the proportion. The family used is binomial and the epsilon would be binary. I am not able to find the package to be used when the proportion (%) has to be used in glm. Could someone help me? I am using normal commands of glm.. for example: glm_5<- glm(formula=p~Precipitation,