similar to: Estimation of Dispersion parameter in GLM for Gamma Dist.

Hi All, could someone please shed some light on the proper goodness-of-fit analysis for the GLM output based on Gamma distributions with the log-link? My objective is to test the goodness-of-fit for the final model (and not the comparison of nested models). In particular, should the 'Residual Deviance' be compared with the Chi-Square distribution, or should the 'Scaled Residual

I'm confused as to the trustworthiness of the dispersion parameters reported by glm. Any help or advice would be greatly appreciated. Context: I'm interested in using a fitted GLM to make some predictions. Along with the predicted values, I'd also like to have estimates of variance for each of those predictions. For a Gamma-family model, I believe this can be done as Var[y] =

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

Hi all, How is dispersion calculated within the glm function in R ? Cheers _________________________________________________________________ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body",

I have been implementing a proposal of Jim Lindsey for glm(.) to return AIC values, and print.glm(.) and print.summary.glm(.) printing them.... however: >>>>> "Jim" == Jim Lindsey <jlindsey@luc.ac.be> writes: Jim> The problem still remains of getting the correct AIC when the user Jim> wants the scale parameter to be fixed. (The calculation should

Dear R users, Which test is most appropriate in glm when the family is Gamma? In the help page of anova.glm, I found the following ?For models with known dispersion (e.g., binomial and Poisson fits) the chi-squared test is most appropriate, and for those with dispersion estimated by moments (e.g., gaussian, quasibinomial and quasipoisson fits) the F test is most appropriate.? My questions :

Dear R users, I would like to fit a glm with Poisson distribution and log link with a known dispersion parameter. I do not want to estimate the dispersion parameter. I know what it is, so I simply want to fix it at a constant for this and other models to follow. My simple, no covariate model is: Tall.glm<-glm(Seedling~1, family=poisson, offset(log(area)), data=tallPSME.df) I want to

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

Hi All, Is the definition and/or algorithm for the 'Hinges' (upper and lower) in Boxplot available somewhere online? I would like to know a little more than what is described in boxplot.stats. In particular I am trying to understand exactly how it differs from the default (Type 7) of the quantile function. Many thanks, Fredrik

Hello all, I was wondering if someone can enlighten me as to the difference between the logLik in R vis-a-vis Stata for a GLM model with the gamma family. Stata calculates the loglikelihood of the model as (in R notation) some equivalent function of -1/scale * sum(Y/mu+log(mu)+(scale-1)*log(Y)+log(scale)+scale*lgamma(1/scale)) where scale (or dispersion) = 1, Y = the response variable, and mu

Hi, Can someone tell me if it is possible to set the dispersion parameter constant when fitting a negative binomial glm in R? I've looked at the documentation and can't find the appropriate argument to pass. In STATA I can type: nbreg depvar [indepvar...], offset(offset) dispersion(constant). Thank you [[alternative HTML version deleted]]

Edward, you also can use the package aod on CRAN, see the help page of the function negbin. Best Matthieu An example: > library(aod) > data(dja) > negbin(y ~ group + offset(log(trisk)), ~group, dja, fixpar = list(4, 0)) Negative-binomial model ----------------------- negbin(formula = y ~ group + offset(log(trisk)), random = ~group, data = dja, fixpar = list(4, 0))

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

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

Hello, I have a question regarding estimation of the dispersion parameter (theta) for generalized linear models with the negative binomial error structure. As I understand, there are two main methods to fit glm's using the nb error structure in R: glm.nb() or glm() with the negative.binomial(theta) family. Both functions are implemented through the MASS library. Fitting the model using these

Dear R experts, How should lambda and gamma (with std.errors) be calculated for a weibull model with age as an independent predictor? I have assumed that this can be done with survreg with e. g. (summary(survreg(Surv(time, status) ~ age, dist = 'weibull')) ) and predict.survreg with e.g. (predict(model, se.fit = T, newdata = data.frame(age = seq(50, 80, 5)) but unfortunately I'm

Greetings, I'm posting this OT query here because of out very international membership! In the French sentence "Les taux de tirage sont calcul??s de mani??re ?? ce que la dispersion soit inf??rieure ?? 5 % dans chaque strate." it would seem intended that the "dispersion" is to be calculated in a specific way (unstated) -- otherwise, how to ensure that it shall be

In glm, is there a way to specify the family to be exponential, or Gamma but with alpha=1? This is required by Dobson Exercise 4.3d, page 48. But without specifying alpha=1, the asnwer is the same as in the book. If reply to the list, please cc me a copy. Thanks. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

--Multipart_Wed_Feb__4_12:25:40_1998-1 Content-Type: text/plain; charset=US-ASCII Jim, I am relating your message to R-devel. This should be discussed in a broader audience; I am not an expert on GLM's, I know you are and others on this group also... R-develers, please CC to Jim Lindsey (on this topic), since he hasn't been part of the R-devel list for a while.. BTW: I will be gone

Hi, I would like to obtain the dispersion parameter for a quasipoisson model for later use in calculating QAIC values for model comparison.Can anyone suggest a method of how to go about doing this? The idea I have now is that I could use the residual deviance divided by the residual degrees of freedom to obtain the dispersion parameter. The residual deviance is available in the summary