similar to: PPML in R

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

Dear list, I posted essentially this same question to the r-sig-geo mailing list last week with no response :( Unfortunately I am no closer to reaching a solution, so I now post it here (with some clarifications) in the hope that someone following this list might have an answer for me: Has anyone had much experience with spatial interaction (or gravity) models, specifically in the form of

Dear list members, Is there a package somewhere for jointly estimating two poisson processes? I think the closest I've come is using the "SUR" option in the Zelig package (see below), but when I try the "poisson" option instead of the "SUR" optioin I get an error (error given below, and indeed, reading the documentation of the Zelig package, I get the impression

Hello. I am running 9 poisson regressions with 5 predictors each, using glm with family=gaussian. Gaussian distribution fits better than linear regression on fit indices, and also for theoretical reasons (e.g. the dependent variables are counts, and the distribution is highly positively skewed). I want to determine pseudo R^2 now. However, using the pR2() of the pscl package offers drastically

On 08/07/12 21:40, R?mi Cardona wrote: > Le 08/07/2012 19:50, R?cker Thomas a ?crit : >> It sees far less testing and review than mainline. >> The other day we had someone for whom Icecast suddenly started crashing. >> Turned out it was a kh build. > I'll pipe in. > > The company I work for has 1000+ audio (and some video) streams, serving > tens of thousands

Hello R-helpers, I would like to fit a non-linear function to data (Discrete X axis, over-dispersed Poisson values on the Y axis). I found the functions gnlr in the gnlm package from Jim Lindsey: this can handle nonlinear regression equations for the parameters of Poisson and negative binomial distributions, among others. I also found the function nls2 in the software package

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

Hi Guys, it would be great if you could help me with a MLE problem in R. I am trying to evaluate the maximum likelihood estimates of theta = (a1, b1, a2, b2, P) which defines a mixture of a Poisson distribution and two gamma prior distributions (where the Poisson means have a gamma distribution, actually 2 gammas and P is the mixing factor). The likelihood function for theta is L(theta) = Pi,j{P

Hi, I'm having difficulties getting the news reader, Gravity, running reliably. Gravity installed okay, and I ran it for a single session. During that test, the following messages appeared many times: fixme:richedit:RichEditANSIWndProc EM_CHARFROMPOS: stub fixme:richedit:RichEditANSIWndProc EM_LINEFROMCHAR: stub fixme:richedit:RichEditANSIWndProc EM_LINEINDEX: stub

Hello all R-user I am relative new to the R-environment and also to GLMM, so please don't be irritated if some questions don't make sense. I am using R 2.0.0 on Windows 2000. I investigated the occurrence of insects (count) in different parts of different plants (plantid) and recorded as well some characteristics of the plant parts (e.g. thickness). It is an unbalanced design with 21

-Bugs and fixes reported to Diethelm Wuertz. -In the interim. To make the Ox functions part of the fSeries package work please follow the following steps. ------------------------------------------------- 1. Install R-project. 2. Install fSeries. 3. Download: http://www.core.ucl.ac.be/~laurent/G@RCH/site/xbdcons/garch42.zip (G@RCH package for Ox) 4. Download:

In case more of you come across my request from this morning, I've already gotten several great tips, which I summarize here since one or two of these did not come across R-help as well. A team of fellow political scientists is on this problem like "white-on-rice"! Brandt, Patrick, John T. Williams Benjamin O. Fordham, and Brian Pollins. 2000. "Dynamic Modeling for Persistent

Dear list, Following a recent enquiry, I've been playing with the idea of creating a colour gradient for a polygon, using the Grid package. The idea is to draw a number of stripes of different colours, using the grid.clip function. Below is my current attempt at this, library(grid) rotate.polygon <- function(g, angle=0){ # utility function, works fine matR <- matrix(c(cos(angle),

I am announcing the release of the exactci package. It calculates exact tests and confidence intervals for binomial and Poisson tests. Here is an example to motivate the package: Suppose you want to see if the observed rates of 2/17877 for group A are significantly different from the observed rates of 10/20000 for group B assuming Poisson counts. The poisson.test function in the stats package

I am announcing the release of the exactci package. It calculates exact tests and confidence intervals for binomial and Poisson tests. Here is an example to motivate the package: Suppose you want to see if the observed rates of 2/17877 for group A are significantly different from the observed rates of 10/20000 for group B assuming Poisson counts. The poisson.test function in the stats package

Hello: I am trying to understand why glm() does not replicate the results in Dobson, "Introduction to Generalized Linear Models," pp. 17-20. I set up the following model. The variable CONDT is assumed as Poisson and the objective is to estimate the expected value. The data (chronic medical conditions among women in Australia) is as follows: CONDT <- c(0, 1, 1, 0, 2, 3, 0, 1,

Wolfgang, It is common to handle relative risk problems using Poisson regression. In your example you have 8 events out of 508 tries, and 0/500 in the second data set. > tdata <- data.frame(y=c(8,0), n=c(508,500), group=1:0) > fit <- glm(y ~ group + offset(log(n)), data=tdata, family=poisson) Because of the zero, the standard beta/se(beta) confidence intervals don't work.

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Hiya, I've posted this 'enigma' on the newsgroup : http://groups-beta.google.com/group/comp.emulators.ms-windows.wine/browse_thread/thread/57dcb7531ebfb529/65018c77a5e58651#65018c77a5e58651 The answers were pretty rare so I post the problem here, too. " I'm getting connection problems with an application run under Wine which isn't acting like that under a native Windows

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 <-