search for: quasilikelihood

Hello, I have a question about the add1() function and quasilikelihoods for GLMs. I am fitting quasi-Poisson models using glm(, family = quasipoisson). Technically, with the quasilikelihood approach the deviance does not have the interpretation as a likelihood-based measure of sample information. Functions such as stepAIC() cannot be used. The function add1() return...

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

...me to two questions. 1. Does anyone have any thoughts on what might cause such a discrepancy? 2. Given two discrepant fits, I would like a way to choose the better one. If my reading of Breslow and Clayton's 1993 paper (specifically, their equation 12) is correct, at convergence, the profile quasilikelihood should be approximately equal to the log likelihood from the last linear mixed model fit by the algorithm. If so, the $logLik component of the lme object produced by glmmPQL should approximate the quasilikelihood I am trying to maximize. In short: according to this argument, the glmmPQL fit with...

Hello, I have the following r-codes for solving a quasilikelihood estimating equation: >library(geepack) >fit<-geese(y~x1+x2+x3,jack=TRUE,id=id,scale.fix=TRUE,data=dat,mean.link = "logit", corstr="independence") Now my question is how can I calculate the confidence interval of the parameters of the above model "fit"? [[...

...of using stepAIC() for the automated selection of a coxph model for VA lung cancer data. A statistics question: Can partial likelihoods be interpreted in the same manner as likelihoods with respect to information based criterion and likelihood ratio tests? It seems that they should be treated as quasilikelihoods which would make stepAIC() invalid and would require the use of add1() with a F-test for the reduction in deviance. An answer and a reference would be greatly appreciated. Thanks, Chad R. Bhatti !DSPAM:445d377a41433079914684!

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

I have been asked to look at options for doing relative risk regression on some survey data. I have a binary DV and several predictor / adjustment variables. In R, would this be as "simple" as using the survey package to set up an appropriate design object and then running svyglm with family=binomial(log) ? Any other suggestions for covariate adjustment of relative risk

Hello, I have the following r-codes for solving a quasilikelihood estimating equation: >library(geepack) >fit<-geese(y~x1+x2+x3,jack=TRUE,scale.fix=TRUE,data=dat,mean.link = "logit", corstr="independence") Now my question is how can I calculate the confidence interval of the parameters of the above model "fit"? [[altern...

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