search for: underdispersed

Hello, Anyone who knows a fast and accurate algorithm for generating draws from an underdispersed Poisson distribution. Or even better, if there is a package containing such an implementation. Thanks Rene

...a using a poisson distribution. The results show evidence of underdispersion. I have only ever encountered overdispersion. Am I still able to use family=quasipoisson to correct for underdispersion? Thank you, Karla -- View this message in context: http://r.789695.n4.nabble.com/what-to-do-with-underdispersed-count-data-tp4279302p4279302.html Sent from the R help mailing list archive at Nabble.com.

Using anova of a glm with test = "Chisq", I get this: Analysis of Deviance Table Model: poisson, link: log Response: Days Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev P(>|Chi|) NULL 373 370.56 Block 3 71.05 370 299.51 2.543e-15 Variety 1 94.04 369

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...

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

Hello all: I frequently have glm models in which the residual variance is much lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF = 82). Is it appropriate for me to use a quasipoisson error distribution and test it with an F distribution? It seems to me that I could stand to gain a much-reduced standard error if I let the procedure estimate my dispersion factor (which

Dear R users: I am using lmer fo fit binomial data with a probit link function: > fer_lmer_PQL<-lmer(fer ~ gae + ctipo + (1|perm) -1, + family = binomial(link="probit"), + method = 'PQL', + data = FERTILIDAD, + msVerbose= True) The output look like this: > fer_lmer_PQL Generalized linear mixed model fit

...arameter is usually very close to 1, while when I include the Subject effect the scale parameter drops, usually to around 0.85? Can I at least conclude something interesting from this? Is it the same as saying that the subject effect itself (meaning the 'observed' subject BLUPs) is underdispersed with respect to its theoretical normal distribution? To summarize: a <- lmer(Response~Fixed Effects+(1|Subject)+(1|Item),data,binomial) b <- lmer(Response~Fixed Effects+(1|Item),data,binomial) a has a much better fit by any measure, and estimated scale around 0.85. b has a worse fit, but...

...Warning message: In rnbinom(10, mu = 100, size = 0) : NAs produced For dispersion set to 0, it should work like drawing from a Poisson distribution. It also produces incorrect draws for non-zero dispersions : > rnbinom(10, mu = 100, size = 0.000001) [1] 0 0 0 0 0 0 0 0 0 0 It also fails for underdispersed count models : > rnbinom(10, mu = 100, size = -0.1) [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Warning message: In rnbinom(10, mu = 100, size = -0.1) : NAs produced > sessionInfo() R version 3.0.2 (2013-09-25) Platform: x86_64-pc-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_AU.UTF-8...

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

Greetings all, I am using lmer (lme4 package) to analyze data where the response is a proportion (0 to 1). It appears to work, but I am wondering if the analysis is treating the response appropriately - i.e. can lmer do this? I have used both family=binomial and quasibinomial - is one more appropriate when the response is a proportion? The coefficient estimates are identical, but the standard

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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 All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My