search for: qaic

...richness) Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 2.628131 23.65318 0.0048847 So, I am running a simple glm with my distribution as quasipoisson > glm.richness1<-glm(beetle.richness~log.area, family = quasipoisson) Now I want to calculate a qAIC and qAICc. I was trying to modify the equation that I found in Bolker et al 2009 supplemental material: QAICc <- function(mod, scale, QAICc=TRUE){ LL <- logLik(mod) ll <- as.numeric(LL) df <- attr(LL, "df") n <- length(mod$y) #used $ to r...

...st time caller" who has been using R in research and graduate teaching for over 5 years. I hope that my question is simple but not too foolish. I've looked through the FAQ and searched the R site mail list with some close hits but no direct answers, so... I would like to estimate QAIC (and QAICc) for a glm fit using the quasibinomial family. I found a general reference suggesting a simple solution: "we calculated QAICc adjusting for overdispersion by dividing the residual deviance (i.e. -2 loglikelihood) with the overdispersion parameter calculated from the most c...

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

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: http://www.springer-ny.com/supplements/0387953647/Preface_Contents_and_Chapter_8.pdf Lebreton, J-D et al. (1992) Modeling Survival and Testing Biological Hypotheses Using Mark...

...d beta-coefficients and over-paramaterization during model selection. Robust standard errors are easily calculated by block-bootstrapping the data using animal as a cluster with the Design library, however I havent found a satisfactory solution for model selection. A couple options are: 1. Using QAIC where the deviance is divided by a variance inflation factor (Burnham & Anderson). However, this VIF can vary greatly depending on the data set and the set of covariates used in the global model. 2. Manual forward stepwise regression using both changes in deviance and robust p-values for the...

...ror. g<- dredge(mod, beta=F, evaluate=F, rank='AIC') Error in sprintf(gettext(fmt, domain = domain), ...) : invalid type of argument[1]: 'symbol' When I try with another rank the same thing happens: chat<- deviance(mod)/58 g<- dredge(mod, beta=F, evaluate=F, rank='QAIC', chat=chat) Error in sprintf(gettext(fmt, domain = domain), ...) : invalid type of argument[1]: 'symbol' Any suggestions would be greatly appreciated thanks Martin Turcotte, Ph. D. mart.turcotte@gmail.com [[alternative HTML version deleted]]

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 output for the...

...ear)*(1|Site), data=ex5m, family=poisson(link="log")) My questions are: 1) The model returns Z values, and I?m unable to find an argument in the function where this can be changed to return a t or F value (as Bolker et al. suggests I should use for my data). 2) I?m unsure what the AIC or QAIC value means, other than knowing that it should be as low as possible. Is there a rule of thumb of what is a good AIC value? Mine are in the region of 2230. 3) The default in glmer {lme4) for the argument nAGQ = 1, which uses the Laplace approximation. When nAGQ >1, it uses the GHQ method, but I...

R Users, I was just wondering if anyone has written a program (or if there is a package) out there that calculates the different derivations of AIC (e.g. AIC, AICc, QAIC, etc.) along with AIC differences (delta's), model likelihoods, Akaike weights and evidence ratio's (from Burnham and Anderson 2002). Just in a "for instance" if someone had the -2LL, sample sizes, parameter counts, and estimates of c_hat output from a program, is there a func...

...te), data=ex5m, family=poisson(link="log")) My questions are: 1) The model returns Z values, and I?m unable to find an argument in the function where this can be changed to return a t or F value (as Bolker et al. suggests I should use for my data). 2) I?m unsure what the AIC or QAIC value means, other than knowing that it should be as low as possible. Is there a rule of thumb of what is a good AIC value? Mine are in the region of 2230. 3) The default in glmer {lme4) for the argument nAGQ = 1, which uses the Laplace approximation. When nAGQ >1, it uses the GHQ method,...

CRAN (and crantastic) updates this week New packages ------------ Updated packages ---------------- New reviews ----------- This email provided as a service for the R community by http://crantastic.org. Like it? Hate it? Please let us know: cranatic at gmail.com.