similar to: gam bug with poisson family? (PR#2234)

Hello, At present, can generalized linear mixed models with negative binomial distribution and estimating the shape parameter be fit using R? I am aware of glm.nb but am wondering about incorporation of mixed effects. Thanks in advance, Brian Aukema

I was surprised to see that this hadn't made it to the samba list yet. Note I have not spent any time trying to confirm validity. ---------- Forwarded message ---------- Date: Fri, 26 Sep 1997 00:21:55 +0200 From: root <root@ADM.KIX-AZZ.ORG> To: BUGTRAQ@NETSPACE.ORG /* ___ ______ _ _ / \ | _ \ | \ / |

Black Hole alert. I've tried to get this sent for 2 weeks and it rejects please forward it to list for me! HELP. I've had this message rejected several times. OK. Here is the problem: I have several W2k Workstations, with a SAMBA 2.2.3 PDC. Problem is that I must have removed all of the important parts of server validation. Here's the story: OUTLAND is domain, W2k/ clients are

Hello! Anyone can help me to know how to do a bootstrap evaluation analysis to a GLM with family=Poisson? I have some R codes but they were coded only for family=binomial... thanks a lot Joana Vicente .?. CIBIO - Centro de Investiga??o em Biodiversidade e Recursos Gen?ticos. Faculdade de Ci?ncias do Porto Departamento de Bot?nica -Edif?cio FC4 Rua Do Campo Alegre, S/N 4169-007 Porto - Portugal

This message is in MIME format. The first part should be readable text, while the remaining parts are likely unreadable without MIME-aware tools. --27464147-1557463723-1147085467=:8118 Content-Type: TEXT/PLAIN; charset=iso-8859-1; format=flowed Content-Transfer-Encoding: 8BIT On Mon, 8 May 2006, x.sole at iconcologia.net wrote: > Full_Name: Xavier Sol? > Version: 2.3.0 > OS: Windows

Hi dear R users, 1) I would like to know if there is a simple way to define a vglm family which would be a mix of poisson variables and bernoulli variables (0/1 response) for idea this would be invoked like this: vglm(...,family=mixpoissonmultinom(npoisson,n01response)) where the n's give the number of each type of response. 2) and a simpler question : How to use constraints in rrvglm?

Hi all I'm trying to use the function glm from the MASS package to do the following fit. fit <- glm(FP ~ rand, data = tab, family = poisson(link = "identity"), subset = rand >= 1) (FP is >= 0) but I get the following error Error: no valid set of coefficients has been found:please supply starting values In addition: Warning message: NaNs produced in: log(x) in contrast

Hello, I'm trying to run an lmer model with family poisson but receive the following warning message: model<-lmer(count~tem+dat+alt+year+tem:dat+tem:alt+tem:year+dat:alt+dat:year+alt:year+(1|id),family=poisson) Error in objective(.par, ...) : Leading minor of order 2 in downdated X'X is not positive definite I'm using the newest version of R and recently installed the

Dear R/statistics wizards, This may be more of a statistics question than an R one, but I’m hoping that someone has the time to help. I’ve already consulted a few local statisticians and I’ve not yet received a clear answer. Before I start an experiment I want to conduct an a priori power analysis test for a Generalized Linear Model, family = poisson. The response variables will be counts.

Hello, I’m trying to fit the following model: Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during a given dive) Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird), STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the interaction SUCCESSMN*STUDYDAY Random effect: BIRD (the bird id, as each bird is performing several dives)

Hi! The nobs() method for glm objects always returns the number of cases with non-null weights in the data, which does not correspond to the number of observations for Poisson regression/log-linear models, i.e. when family="poisson" or family="quasipoisson". This sounds dangerous since nobs() is, as the documentation states, primarily aimed at computing the Bayesian

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

Dear R-helpers, I would like to compare the fit of two models, one of which I fit using lm() and the other using glm(family=poisson). The latter doesn't provide r-squared, so I wonder how to go about comparing these models (they have the same formula). Thanks very much, Mark Na [[alternative HTML version deleted]]

Dear R-users When running a glm polynomial model with one explanatory variable (example Y~X+X^2), with a poisson or binomial error distribution, the predicted values obtained from using the predict() function and those obtained from using the coefficients from the summary table "as is" in an equation of the form Y=INTERCEPT+ XCoef x X + XCoef x X^2, differ considerably. The former are

Shawn sent me a copy of the non-Direct Sound version of this output plugin, and I'm hosting it on my web site's rare software page: http://members.lycos.co.uk/bhafool1/rarities/ I've found it adds far more smoothness and clarity to various low sample-rate audio files, including an 80-minute speech I mentioned previously, due to my soundcard's approach to D-to-A conversion

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui

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

Hi, (this is my first post here) A previous thread, starting Date: Tue 19 Nov 2002 - 06:09:56 EST "[vorbis] need speech and music in one" http://www.xiph.org/archives/vorbis/200211/0142.html expressed needs similar to mine, to encode a lengthy speech at low bitrate. I did some tests initially in September then concluded in December, and I was surprised to find Vorbis to be the best

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

https://bugzilla.mindrot.org/show_bug.cgi?id=2234 Bug ID: 2234 Summary: ssh-add -l output aborts on unrecognized key, skips flush when stdout not tty Product: Portable OpenSSH Version: 6.6p1 Hardware: All OS: All Status: NEW Severity: normal Priority: P5 Component: