similar to: regression with proportion data

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,

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

Greetings all, I am using lmer (lme4 package) to analyze data where the response is a proportion (0 to 1). It appears to work, but I am wondering if the analysis is treating the response appropriately - i.e. can lmer do this? I have used both family=binomial and quasibinomial - is one more appropriate when the response is a proportion? The coefficient estimates are identical, but the standard

Hello, dear experts. I don't have much experience in building regression models, so sorry if this is too simple and not very interesting question. Currently I'm working on the model that have to predict proportion of the debt returned by the debtor in some period of time. So the dependent variable can be any number between 0 and 1 with very high probability of 0 (if there are no payment)

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! When I am analyzing proportion data, I usually apply logistic regression using a glm model with binomial family. For example: m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial") However, sometimes I don't have the number of cases (realized, not realized), but only the proportion and thus cannot compute the binomial model. I just

Hello, Anyone know why the command: > plot(x,y) where y is a 0,1 result sometimes plots the y values as 1,2 rather than 0,1? And how to prevent this? Thank you, Georgiana May [[alternative HTML version deleted]]

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,

Hi, Does anyone know of a function in R that can handle a fractional variable as the dependent variable? The catch is that the function has to be inclusive of 0 and 1, which betareg() does not. It seems like GLM might be able to handle the fractional logit model, but I can't figure it out. How do you format GLM to do so? Best, Rachael [[alternative HTML version deleted]]

Greetings, I am interested in using a generalized linear mixed model with data that best fits a beta distribution (i.e., the data is bounded between 0 and 1 but is not binomial). I noticed that the beta distribution is not listed as an option in the "family objects" for glmmPQL or lmer. I found a thread on this listserve from 2006 ("[R] lmer and a response that is a

Hello everybody I want to compare the proportions of germinated seeds (seed batches of size 10) of three plant types (1,2,3) with a glm with binomial data (following the method in Crawley: Statistics,an introduction using R, p.247). The problem seems to be that in two plant types (2,3) all plants have proportions = 0. I give you my data and the model I'm running: success failure

Hi all, I am running into a problem installing a package. I am using a Mac with OSX 10.5.8 I have been using mgcv for gam models, it has been working fine. The mgcv listed in my directory of R packages (Package Manager) does not load up and I receive the following: Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared object

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

Hi, I have a dataset that has 2 groups of samples. For each sample, then response measured is the number of success (no.success) obatined with the number of trials (no.trials). So a porportion of success (prpop.success) can be computed as no.success/no.trials. Now the objective is to test if there is a statistical significant difference in the proportion of success between the 2 groups of

Hello, I am looking for a sample size function for samples sizes, to test proportions that are not binomial proportions. The proportions represent a ratio of (final measure) / (baseline measure) on the same experimental unit. Searches using RSeek and such bring multiple hits for binomial proportions, but that doesn't seem to fit my situation. Perhaps there's some standard terminology

Hi all, In my continuous transition of GLIM to R I try to make a glm with binomial errors. The data file have 3 vectors: h -> the factor that is ajusted (have 3 levels) d -> number of animais alive (the response) n -> total number of animals To test proportion of alive, make d/n. In GLIM: $yvar d$ $error binomial n$ $fit +h$ scale deviance = 25.730 (change = -9.138) at cycle 4

Hello R folks, I have three questions. I am trying to run a logistic regression (binomial family) where the response variable is a proportion. According to R Documentation in "a binomial GLM prior weights are used to give the number of trials when the response is the proportion of successes." However when I run my code I get the following error message: Error in

This question is slightly off topic, but I'll use R to try and make it as relevant as possible. I'm working on a problem where I want to compare estimates from a posterior distribution with a uniform prior with those obtained from a frequentist approach. Under these conditions the estimates should agree. Specifically, I am asking the question, "What is the probability that the true

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

> sessionInfo() R version 2.15.0 (2012-03-30) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] grid datasets splines utils stats [6] graphics grDevices methods base other attached packages: [1] effects_2.1-0 colorspace_1.1-1 [3] nnet_7.3-1