similar to: non homogeneous poisson process

Hi R Users, I'm going to estimate via. ML the parameters in Poisson Lognormal model. The model is: x | lambda ~ Poisson(lambda) lambda ~ Lognormal(a,b) Unfortunately, I haven't found a useful package allowing for such estimation. I tried to use "poilog" package, but there is no equations and it's hard to understand what exactly this package really does. Using it I get the

Dear everyone I'm dealing with a problem related to Poisson Ridge Regression. If anyone can help me in this regard by telling if any changes in the source code of "glm.fit" may help -- Regards Umesh Khatri

Hi all, So far I only know one way to get the confidence limit for the Poisson distribution is to use the look-up table given by the 2 parameter (the number of observation x and the confidence level, e.g. 95%) and the table is limit by the maximum number of observations (x <= 50). I know the formula to compute the CI, however, mathematically it is not easy to do it. So, anyone know an R

Dear R users I would like to simulate underdispersed Poisson and binomial distributions somehow. I know you can do this for overdispersed counterparts - using rnbinom() for Poisson and rbetabinom() for binomial. Could anyone share functions to do this? Or please share some tips for modifying existing functions to achieve this. Thank you very much for your help and time Shinichi

I am interested in R functions for the following integrals / sums (expressed best I can in text) - Normal: G_u(k) = Integration_{Lower limit=k}^{Upper limit=infinity} [(u -k) f(u) d(u)], where where u is N(0,1), and f(u) is the density function. Poisson: G(lambda,k) = Sum_{Lower limit=k}^{Upper limit=infinity} [(x-k) p(x, lambda)] where P(x,lambda) is the Poisson prob function with parameter

Dear all, I have a very basic question: how does the logLik function work for poisson models? Example: I simulate 20 observations from a Poisson distribution with mean 800. y <- rpois(20,800) model <- glm(y ~ 1, family=poisson()) logLik(model) I would like to know what's the exact formula the function logLik uses. I looked at ?extractAIC but I cannot sort it out. Can you please

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

Hello dear R-help mailing list, A friend of mine teaches a regression and experimental design course and asked me the following question. She is trying to find a way to display the "homogeneous groups" (after performing tukey test on an aov object). here's an example for what she means by "homogeneous groups": She did one way anova and got these results for tukey test:

Hello, I'd like to upstream our work over the time which the community would benefit from. This is a part of effort toward minimizing code size presented in here <https://llvm.org/devmtg/2020-02-23/slides/Kyungwoo-GlobalMachineOutlinerForThinLTO.pdf>. In particular, this RFC is about optimizing prolog and epilog for size. *Homogeneous Prolog and Epilog for Size Optimization, D76570

Hi Vedant, Thanks for your interest and comment. Size-optimization improves page-faults and a start-up time for a large application, which this enabling also followed. Even though I didn't see a large regression/complaint on a CPU-bound case, which is not a typical case for mobile workload, I wanted to be precautious of enabling it by default. However, as with default outlining case, I

Dear R users I have been looking for functions that can deal with overdispersed poisson models. 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. However, we see them frequently in this type of data, and we would like to

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

Dear RHelpers, Does anyone know of any R code to perform bivariate Poisson regression (including random effects)? Best wishes Simon Simon D.W. Frost, M.A., D.Phil. Department of Pathology University of California, San Diego Antiviral Research Center (Formerly: UCSD Treatment Center) 150 W. Washington St., Suite 100 San Diego, CA 92103 USA Tel: +1 619 543 8080 x275 Fax: +1 619 298 0177 Email:

Dear R users, I have a large individual-level dataset (~700,000 records) which I am performing a conditional logistic regression on. Key variables include the dichotomous outcome, dichotomous exposure, and the stratum to which each person belongs. Using this individual-level dataset I can successfully use clogit to create the model I want. However reading this large .csv file into R and running

Hello all, I'm running very simple regression but face a problem of non-homogeneity of variance, but with a decreasing variance with increasing mean...I do not know how to deal with that. this relationship doesn't seem to be strong, but it's my first time to see something like that, and would like to know what to do if one day it becomes stronger. I tested just for fun some

Hi, I was wondering if someone could help me answer a question that is bound to come up in my Master's defense. I'm using AIC to select models and my question is how do I know that the models I developed a priori contain the 'best' models in the system. How do I not know that some models which I didn't include aren't actually the 'best' model?? Thanks so much

Dear all I want to simulate a stochastic jump variance process where N is Bernoulli with intensity lambda0 + lambda1*Vt. lambda0 is constant and lambda1 can be interpreted as a regression coefficient on the current variance level Vt. J is a scaling factor How can I rewrite this avoiding the loop structure which is very time-consuming for long simulations? for (i in 1:N){ ... N <- rbinom(n=1,

I am fairly new to log-linear modelling, so as opposed to trying to fit modells, I am still trying to figure out how it actually works - hence I am looking at the interpretation of parameters. Now it seems most people skip this part and go directly to measuring model fit, so I am finding very few references to actual parameters, and am of course clear on the fact that their choice is irelevant for

Hi all, I've been running loglinear models for three-way tables: one of the variables having three levels, and the other two having two levels each. An example looks like below: > yes.no <- c("Yes","No") > switch <- c("On","Off") > att <- c("BB","AA","CC") > L <- gl(2,1,12,yes.no) > T <-

Dear R users, I am trying to find a package that can estimate an autoregressive model for discrete data. I am imagining a Poisson or Gamma process in which the mean (say mu) follows a process such as mu_t = a + b*x + c*mu_{t-1} Suppose I have data on the time-series Poisson outcomes and x and would like to obtain ML estimates for b and c. Does anyone know of a package that can do this