search for: quasy

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

Hi all, I have run a (glm) analysis where the dependent variable is the gender (family=binomial) and the predictors are percentages. I get a warning saying "fitted probabilities numerically 0 or 1 occurred" that is indicating that quasi-separation or separation is occurring. This makes sense given that one of these predictors have a very influential effect that is depending on a

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,

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

Hello, I've been looking in the on-line manuals and searching past posts but can't find an answer to this question. I'd like to define my own variance function in a GLM. The function glm(formula, family=quasi(var="var function")) lets me choose from a selection of built in variances, but I want to define my own function for the variance. Is there an S-plus

Howdy. I just posted an unofficial HOWTO on configuring icecast2 on a Linux or other UNIX machine. It's just a draft in DocBook format, but I was hoping it might help someone. I've worked enough with icecast over the past few years that I thought it time to share what I've learned. The URL is as follows: http://quasi.ksl.com/icecast/ I have older documentation in

This is not so much a bug as an infelicity in the code that can easily be fixed. The initialize expression in the quasi family function is, (uniformly for all links and all variance functions): initialize <- expression({ n <- rep.int(1, nobs) mustart <- y + 0.1 * (y == 0) }) This is inappropriate (and often fails) for variance function "mu(1-mu)".

Hi all, This is a very silly question or something escapes me: Let obj a simple gam poisson model. Let >obj<-gam(....,family=poisson) >obj1<-update(obj, family=quasi(link="log", var="mu")) >From summary.glm(obj1) the dispersion parameter is estimated 1.165; In fact it is: > (predict(obj1, se.fit=T)$se.fit[1:5]/predict(obj, se.fit=T)$se.fit[1:5])^2 4

Hello, Has anyone written up code to estimate for example a simple logit using quasi-likelihood? I know that glm() already does this, but I'd like to do some tinkering with the variance function beyond what glm() allows. I've scanned online sources and everyone seems to use glm(). Will take a crack at it if necessary but have zero experience w/q-likelihood and not that much with

Hi all, Cameron and Trivedi in their 1998 Regression Analysis of Count Data refer to NB1 and NB2 NB1 is the negative binomial model with variance = mu + (alpha * mu^1) yielding (1+alpha)*mu NB2 sets the power to 2; hence, variance = mu + (alpha*mu^2) I think that NB2 can be requested via negbin2<-glm(hhm~sex+age,family=quasi(var="mu^2",link="log")) Is

Hi I want to do simulation on quasi-binomial distribution with some covariates. Does anyone have an idea how to do that? [[alternative HTML version deleted]]

Hello! I am trying to model data on species abundance (count data) with a poisson error distribution. I have a fixed and a random variables and thus needs a mixed model. I strongly doubt that my model is overdispersed but I don't know how to get the overdispersion parameter in a mixed model. Maybe someone can help me on this point. Secondly, it seems that quasi models cannot be implemented

In a glm() call using a quasi() family, one may define a custom variance function in the form of a "list containing components varfun, validmu, dev.resids, initialize and name" (quoting the help page for family). In trying to do so, I run into the following issue that I have not seen discussed previously: x <- runif(1000, min=0, max=1) y <- x + rnorm(1000, mean=0, sd=1)*x^(3/4)

I am resubmitting this to r-bugs, since Thomas Lumley indicates that it might be an error: On Wed, 5 Jul 2000, Thomas Lumley wrote: > On Wed, 5 Jul 2000, halvorsen wrote: > > > Hola! > > > > I have the following simple function: > > > > > testcar > > function(pow){ > > ob <- glm(Pound~CG+Age+Vage,data=car,weights=No, > >

Dear list useRs, I have to generate a random set of coordinates (x,y) in [-1 ; 1]^2 for say, N points. At each of these points is drawn a circle (later on, an ellipse) of random size, as in: > N <- 100 > > positions <- matrix(rnorm(2 * N, mean = 0 , sd= 0.5), nrow=N) > sizes<-rnorm(N, mean = 0 , sd= 1) > plot(positions,type="p",cex=sizes) My problem is to

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Hi list, can anybody point me to the trick how glm is computing the dispersion parameter in quasi-poisson regression, eg. glm(...,family="quasipoisson")? Thanks &regards, Sven

Hello, I'm looking for a dataset for Quasipoisson regression. The result must be significantly different from the classic poisson regression. You can help me? Please It is for my last university exam Thanks a lot -- View this message in context: http://r.789695.n4.nabble.com/Dataset-Quasi-Poisson-tp3533060p3533060.html Sent from the R help mailing list archive at Nabble.com.

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf:

Hi, I'm tying to fit a glm using quasi with variance=mu(1-mu) (to try and get "well behaved" residuals). I get an error message: "cannot find valid starting values, please specify some". I have tried using start= and specifying coefficients from a previous fit (with different error structure) but get the same message. What would be valid starting values? I am using a