similar to: Poisson GLM with a logged dependent variable...just asking for trouble?

Hello, I have a question about modelling via glm. I have a dataset (see dput) that looks like as if it where poisson distributed (actually I would appreciate that) but it isnt because mean unequals var. > mean (x) [1] 901.7827 > var (x) [1] 132439.3 Anyway, I tried to model it via poisson and quasipoisson. Actually, just to get an impression how glm works. But I dont know how to

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Hello R-help, According to a research article that covers the topic I'm analyzing, in Stata, a Poisson pseudo-maximum-likelihood (PPML) estimation can be obtained with the command poisson depvar_ij ln(indepvar1_ij) ln(indepvar2_ij) ... ln(indepvarN_ij), robust I looked up Stata help for the command, to understand syntax and such: www.stata.com/help.cgi?poisson Which simply says

Hello all, I found a weird result of the GLM function that seems to be a bug. The code: a=c(rep(1,8),rep(2,8)) b=c(rep(0,8),rep(3,8)) cbind(a,b) model=glm(b~a, family=poisson) summary(model) generates a dataset with two groups. One group consists entirely of zeros, the other of 3's (as happened in a dataset I’m analyzing right now). Since they are count data, one should apply a

Hello! I have a data frame A (below) with a grouping factor (group). I take my DV and create the new, lagged DV by applying the function lag.it (below). It works fine. A <- data.frame(year=rep(c(1980:1984),3), group= factor(sort(rep(1:3,5))), DV=c(rnorm(15))) lag.it <- function(x) { DV <- ts(x$DV, start = x$year[1]) idx <- seq(length = length(DV)) DVs <- cbind(DV, lag(DV,

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

Hi list, can anybody point me to the trick how glm is computing the dispersion parameter in quasi-poisson regression, eg. glm(...,family="quasipoisson")? Thanks &regards, Sven

Sorry about re-posting this, it never went out to the mailing list when I posted this to r-help forum on Nabble and was pending for a few days, now that I am subscribe to the mailing list I hope that this goes out: I've been a viewer of this forum for a while and it has helped out a lot, but this is my first time posting something. I am running glm models for richness and abundances. For

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. The presence of negatives is not

Hi I'm a postgrad in ecology, and have recently started to use R. I'm planning to model various sets of animal abundance (i.e. count) data in relation to habitat data using glm's and/or glmmPQL's. However, some of my potential response variables have many zeros. From what I gather the "family = ..." option in the command line does not allow for the direct

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

This is a simple problem that has stumped me: I'm trying to loop through a few dozen variable names in graphs. I've tried various approaches like this: attach(mydata) ivs <- c("oneiv", "anotheriv", "yetanotheriv") dvs <- c("onedv", "anotherdv", "yetanotherdv") for (iv in ivs) { for (dv in dvs) { graphname <- paste(iv,

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

Hello, I’m trying to fit the following model: Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during a given dive) Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird), STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the interaction SUCCESSMN*STUDYDAY Random effect: BIRD (the bird id, as each bird is performing several dives)

Hello, I'm looking for a dataset for Quasipoisson regression. The result must be significantly different from the classic poisson regression. You can help me? Please It is for my last university exam Thanks a lot -- View this message in context: http://r.789695.n4.nabble.com/Dataset-Quasi-Poisson-tp3533060p3533060.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have count data that have been meddled with enough to make them non integers. Using glm(poisson) returns a "non integer" error but glm(quasipoisson) does not. Just wondering if anyone knows if I am violating the assumptions of a quasipoisson error structure by using these non-integer response data? Thanks! I'd welcome your thoughts and/or references... Mark

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Hello, I'm having an error when trying to fit the next GLM: >>model<-glm(response ~ CLONE_M + CLONE_F + HATCHING +(CLONE_M*CLONE_F) + (CLONE_M*HATCHING) + (CLONE_F*HATCHING) + (CLONE_M*CLONE_F*HATCHING), family=quasipoisson) >> anova(model, test="Chi") >Error in if (dispersion == 1) Inf else object$df.residual : missing value where TRUE/FALSE needed If I fit