similar to: Modelling an "incomplete Poisson" distribution ?

Dear all, My question might be more of a statistics question than a question on R, although it's on how to apply the 'quantreg' package. Please accept my apologies if you believe I am strongly misusing this list. To be very brief, the problem is that I have data on only a random draw, not all of doctors' patients. I am interested in the, say, median number of patients of

Hi, I'd like to simply add column-wise using Matrix objects (Csparse). It looks like one can apply mosty any base function to these objects (i.e., apply, colSums), but there is a nasty conversion to traditional matrix objects if one does that. Is there any workaround? I can see colSum listed in the help for Class 'CsparseMatrix' , but I wonder whether I'm using the default

Hello, I am mosty done with the upgrade, but there is a problem that keeps me awake at night: file ownership and permissions. It would be most helpful to have the following from Aki's live system: ls -halFR /var/run/dovecot -------------- next part -------------- An HTML attachment was scrubbed... URL:

I am attempting to model data with the following variables: timepoint - n=48, monthly over 4 years hospital - n=3 opsn1 - no of outcomes total.patients skillmixpc - skill mix percentage nurse.hours.per.day Aims To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients). To determine if nurse.hours.per.day affects rate. To determine if rates vary between

I'm dealing with count data that's nested and has spatial dependence. I ran a glmm in lmer with a random factor for nestedness. Spatial dependence seems to have been accommodated by model. However I can't add a variance strcuture to this model (to accommodate heterogeneity). Is there a model that can have a poisson distribution *AND* a variance structure *AND* have AIC in output (for

Dear list, This is a copy of a mail sent to Max Kuhn, original author and maintainer of the odfWeave package, which seems not to have received it. It reports a problem that seems to be very implementation specific (reproductible on three Debian testing amd64 machine, does *not* happen on two i686 Debian testing systems, does *not* happen on an Ubuntu 11.06 amd64 machine) and therefore not

Dear all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

Dear List, I am (again) looking at meta-regression as a way to refine meta-analytic results. What I want to do is to assess the impact of some fixed factors on the results of a meta-analysis. Some of them may be crossed with the main factor of the meta-analysis (e. g. clinical presentation of a disease, defining subgroups in each of the studies under analysis), some of them may be a grouping

Thanks Luke, On 10 September 2015 at 14:47, <luke-tierney at uiowa.edu> wrote: > Conditions have classes and the condition system is designed around > the idea that classes would be used for this sort of thing. That is > already how tryCatch and withCallingHandlers discriminate the > conditions to handle. That makes sense. Though with my sqrt example, it's just a plain

i cannot cajole outer in doing what i want it to do. i hav a function only defined for scalars fun(n,k) and i want to do outer(1:5,1:5,function(x,y) fun(x,y)) (i know this is written somewhat clumsy) to get an array with the values of fun to use it as an input for image this does not work since outer assumes then fun works on arrays elementwise. what is the easiest way of getting what i

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

Dear Listers, I have a general statistical question. Are hurdle logit-poisson model and posson model nested? Thank you so much?

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

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

Hello, from a search of the archives and functions, I am looking for information on creating random correlated counts from a multivariate Poisson distribution.  I can not seem to find a function that does this. Perhaps, it has not yet  been created. Has anyone created an R package that does this.   thanks,   Jourdan Gold     [[alternative HTML version deleted]]

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

Hi I am trying to carry out some maximum likelihood estimation and I'm not making much headway, and I'm hoping that someone will be able to point me in the right direction. I am modelling mortality statistics. One way to do this is to model the mortality rate (or, more accurately, log of the mortality rate, log_m) as (say) a constant plus a proportion of age, plus time, so: r_1 <-

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

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

Dear sir, I want to know about R command for parameter estimation for generalized Poisson regression. yours faithfully Arafat Ud Zaman 4th year(Hons) Applied statistics University of Dhaka Mob- +8801715173176 E-mail: arafat.zaman@yahoo.com azaman1@isrt.ac.bd azaman1@quantummethod.org.bd [[alternative HTML version deleted]]