similar to: Error distribution question

Dear all, I am interested in correctly testing effects of continuous environmental variables and ordered factors on bacterial abundance. Bacterial abundance is derived from counts and expressed as percentage. My problem is that the abundance data contain many zero values: Bacteria <-

Hi there, I'm using the tweedie distribution package and I cant figure out how to run a model using an identity link. I know I can use a log link by having link.power=0 and I think identity would be link.power=1, but I'm not sure. Furthermore when I try running it with link.power=1 it requires starting values which I cant manage to give appropriately so I'm not sure if its

Dear R-users, I'm trying to model some data using a tweedie GLM approach. My response variable is the number of pupae that are the offspring of a subordinate wasp on a wasp's nest. However, they're not count data- for each nest, I only know the mean number of pupae per subordinate, which is continous. The data also contain a high proportion of zeros. I'm not very experienced at

Hi all, I was wondering if somebody could give me advice regarding the dispersion parameter in GLMM's. I'm a beginner in R and basically in GLMM's. I've ran a GLMM with a poisson family and got really nice results that conform with theory, as well with results that I've obtained previously with other analysis and that others have obtained in similar studies. But the

Hi Dr. Snow, I am a graduate student working on analyzing data for my thesis and came across your post on an R forum: The default link function for the glm poisson family is a log link, which means that it is fitting the model: log(mu) ~ b0 + b1 * x But the data that you generate is based on a linear link. Therefore your glm analysis does not match with how the data was generated

Hi -I'm running a GAM with 7 explanatory variables with a Poisson error structure. All of the variables are continuous so I'm getting error messages in R. cod.fall.full.gam.model<-gam(Kept.CPUE~s(HOUR)+s(LAT_dec)+s(LONG_dec)+s(meantemp_C)+s(meandepth_fa)+s(change_depth)+s(seds), data=cod.fall.version2,family=poisson) In dpois(y, mu, log = TRUE) ... : non-integer x = 5.325517

Hi all I've been getting some emails from the R package maintainers that I need to update some code in a CRAN packge that uses FORTRAN, to comply with (not so recent) changes. I've been a little busy... I'm having trouble adjusting my code. I hope someone can help. The package was working fine, and a few R functions in my package had lines like this: tmp <- .Fortran(

Hello all, I have count data with about 36% of observations being zeros. I found in some of the examples of the r-help mail archives that a tweedie family of distributions could be used to fit a model with random effects. Upon installing the tweedie package and attempting to fit the following model: lmer(SUS ~ 1 + (1|

Hi there, I've been wanting to fit a GLMM and I'm not completely sure I'm doing things right. As I said in a previous message my response variable is continuous with many zeros, so I was having a hard time finding an appropriate error distribution. I read some previous help mails given to other people advising them to use the Tweedie distribution. I'm still not sure if this

Dear Catarina, I prefer to leave the AIC value as NA for the tweedie GLM family because it takes extra time to compute and is only occasionally wanted. It's easy to compute the AIC yourself using the dtweedie() function of the tweedie package. Best wishes Gordon At 03:05 AM 14/08/2007, Catarina Miranda wrote: >Dear Gordon; > >I have also sent this email to R help mailing list,

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

Hi there Thank you for everyone's help in all my previous questions. By way of intro, I am a masters student in actuarial science at the University of Cape Town, and I am doing a project in R on some healthcare cost data. Just for clarity before I embark on further research may I please ask the following. I want to take the direction of modelling healh insurance claims data with Tweedie

I'm trying to make a glmm to identify the relationship between insect species richness with fragment size, isolation and time (different years). I already tried to analyse it using poisson distribution error, but I always face with the following warning: *glm.fit: fitted probabilities numerically 0 or 1 occurred * This is probably hapenning because my dataset has a lot of zeros. So, what

Hi All, I have a sequence of positive integers, which is right skewed. The mean value is 6 and variance is 11. I suspect it can be fitted by poisson distribution. But I'm not familiar with the function to fit distribution. Could you please help me with it? Also, once I fit the poisson distribution, how can I check the good-ness of this fitting? Thank you! [[alternative HTML

I am using a binomial mixture model to estimate abundance (N) and detection probability (p) using simulated count data: -Each site has a simulated abundance that follow a Poisson distribution with lambda = 5 -There are 200 simulated sampled sites -3 repeated counts at each site - only 50 percent of the animals are counted during each count (i.e, detection probability p =0.5, see codes) We removed

Dear All I would like to know whether it is possible with R to generate random numbers from zero-truncated Poisson distribution. Thanks in advance, Galina

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

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, I am trying to find a function to match two data frames of different lengths for one field only. So, for example, df1 is: Name Position location francesca A 75 cristina B 36 And df2 is: location Country 75 UK 56 Austria And I would like to match on "Location" and the output to be something like: Name Position Location Match francesca A 75 1 cristina B 36 0 I have tried with

Hi, I have not been able to calculate residuals in the lme4 package. I've been trying the resid() function after I ran a GLMM with the lmer() function, but I get an error message that says "residuals are not inserted yet". I looked it up in the "help" history and I realized that several people have had this problem in the past, related to some bug in this function and