similar to: NB and poisson glm models: three issues

Hi, I am trying to fi a glm-poisson model to 400.000 records. I have tried biglm and glmulti but i have problems... can it really be the case that 400.000 are too many records??? I am thinking of using random samples of my dataset..... Many thanks, -- View this message in context: http://r.789695.n4.nabble.com/glm-poisson-fitting-400-000-records-tp3925100p3925100.html Sent from the R help

I''m working on a questionnaire which is a "one to many"-realtion between a question and some answers. I can extract the data form the database whitout problems, but I''m having problems creating a new question - or actually, creating new answers to a question. What I would like to have is: A form where you can write a question, and then 8 (or whatever) text_fields

hey! I have the following table structure in my DB | users | ------------ | (pk)id | | username | | pass | ------------ (1:n) | users_objects | | objectattributes | ------------------ ---------------------------------- | (fk,pk)user_id | (1:n) | (pk)attribute | |(fk,pk)object_id| | (pk,fk)

Dear list, This is a question about overdispersion and the ML estimates of the parameters returned by the glm.poisson.disp (L. Scrucca) and glm.nb (Venables and Ripley) functions. Both appear to assume a negative binomial distribution for the response variable. Paul and Banerjee (1998) developed C(alpha) tests for "interaction and main effects, in an unbalanced two-way layout of counts

Dear userRs, when applied the summary function to a glm fit (e.g Poisson) the parameter table provides the categorical variables assuming that the first level estimate (in alphabetical order) is 0. What is the standard error for that variable then? Are the standard errors calculated assuming a normal distribution? Many thanks, -- View this message in context:

Hi userRs! I am trying to fit some GLM-poisson and neg.binomial. The neg. Binomial model is to account for over-dispersion. When I fit the poisson model i get: (Dispersion parameter for poisson family taken to be 1) However, if I estimate the dispersion coefficient by means of: sum(residuals(fit,type="pearson")^2)/fit$df.res I obtained 2.4. This is theory means over-dispersion since

Some might have noticed that REvolution Computing released the doSMP package to the general public about a month and a half ago, which allows multiple cores to be accessed for parallel computation in R. Some of our physical habitat calculations were taking an extraordinary amount of time to complete and required over-weekend runs, which prompted our interest in this package. What follows

Hi everyone, My background is not math and I am trying to figure out exactly what equation to use to map a response variable in GIS based on the coefficients obtained from the GLM and the values of the independent variables in each grid cell of my study area. Most specifically, I want to know how to incorporate the Poisson family and log link function in the equation. I would really appreciate if

You can save as png like this too: library(ggplot2) data=data.frame( ? X=sample(10,1000,replace=T) ? , Y=letters[1:10]) png("mypng.png") qplot(X, data=data, geom='histogram') + facet_wrap( ~ Y) dev.off() ? Felipe D. Carrillo Supervisory Fishery Biologist Department of the Interior US Fish & Wildlife Service California, USA ----- Original Message ---- > From: Peng Yu

I'm getting some odd queries from a WinXPSP3 client to an older samba (3.2.0-24) Domain & File server running on linux kernel (2.6.27.19). The client seems to be querying for a netbios name <literal-question mark> followed by binary 0 (<00>) to the DomainServer, or Broadcasting the query with the '?' followed by a space <20>. This is from conversation over wire

hello list! I'm sorry, I just stumbled over this strange behaviour (at least I am not able to explain the behaviour, therefore I assume it to be a strange behaviour): attach(water) # I know, this is not recommended names(water[3:10]) [1] "temp" "pH" "DO" "BOD" "COD" "no3" "no2" "po4" for (i in

Hi there, I need to do the same thing as cumsum but with the variance and skewness. I have tried to do a loop for like this: var.value <- vector(mode = "numeric", length = length(daily)) for (i in (1:length(daily))) { var.value[i] <- var(daily[1:i]) } But because my dataset is so huge, I run out of memory..... Any ideas?!?! Much appreciate

I am trying to fit the generalised linear model for the negative binomial, but the results which come out are attached below. When we fit this model using few covariates, the model converge. Does it mean that this family is fitted differently from other glm? or the number of zeros in my response variable has a limiting factor? Thanks Bruno fit <- glm.nb(pfde~SEX+...., data=data1) Warning

Hi, I make some simulations with rnbinom and try to test with glm.nb. But in some data set the glm.nb fail. Look: pop <- rnbinom(n=1000,size=1,mu=0.05) > glm.nb(pop~1,maxit=1000) Error in while ((it <- it + 1) < limit && abs(del) > eps) { : missing value where TRUE/FALSE needed look some pop charactetistics: > summary(pop) Min. 1st Qu. Median Mean 3rd Qu.

Hello to everyone. I'm fitting a glm.nb model to a count data. I'm using about 8 predictive variables. Once a run the script I do get a result but it tells me that the iteration limit has been reached. So, can i trust the results given by the model? Could it be a multicollinearity problem? Thank you for taking the time to help me. Greetings Lucas. -- View this message in context:

I'm having problems (or to be precise a student is having problems, which I'm having problems helping her with) trying to use glm.nb() from the MASS package to do some negative binomial fits on a data set that is, admittedly, wildly overdispersed (some zeros and some numbers in the hundreds). glm.nb is failing to converge, and furthermore is (to my surprise) producing values of theta

dear listers, i tried to use glm.nb to estimate a nega. binomial but have no luck to get the result. here is the code: model <- glm.nb(Y ~ ., data = mydata) I am not sure if I have missed anything. thanks for your insight! wensui

Hi, I am trying to fit a negative binomial model with a number of parasite tapeworms as response variable to geographical coordinates (actually preparing a trend surface before kriging). When I try an anova, I get warnings: > glm4.nb<-glm.nb(wb~X4+Y4+I(X4^2)+I(Y4^2)) > anova(glm4.nb) Analysis of Deviance Table Model: Negative Binomial(0.0463), link: log Response: wb Terms added

If you rebuild the Makefiles using automake >= 1.13 then you'll likely hit this error: Makefile:1904: *** unterminated variable reference. Stop. This is caused by automake miscompiling GNU make macros that appear in the 'TESTS' variable decl in 'ocaml/Makefile.am'. I posted a bug about this upstream. It hasn't appeared yet, but it should turn up here in the near

Hi, I noticed that speex.org has a banner that mentions that Opus is better than Speex in all aspects. The supported bitrate range for Speex seems to be as low as 2kbps though but Opus can only go as low as 6kbps. Is this one aspect where Speex is still preferred? (I understand that it's not a very common scenario though). Thanks, Manpreet. -------------- next part -------------- An HTML