search for: quasibinomi

After looking at 48 glm binomial models I decided to try the quasibinomial with the top model 25 (lowest AIC). To try to account for overdispersion (residual deviance 2679.7/68 d.f.) After doing so the dispersion factor is the same for the quasibinomial and less sectors of the beach were significant by p-value. While the p-values in the binomial were more significant f...

Full_Name: Henric Nilsson Version: 2.3.0 Patched (2006-05-09 r38014) OS: Windows 2000 SP4 Submission from: (NULL) (83.253.9.137) When supplying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit", ", ""probit" and "cloglog"...

...I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm(). 1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values for the Chi-squared tests. When not supplying the dispersion argument, i.e. when anova.glm() uses the default scaled Pearson statistic from family=quasibinomial, both tests returns the p-values I'd expect. What am I doing...

Dear all, I try to consider overdispersion in a lmer model. But using family=quasibinomial rather than family=binomial seems to change the fit but not the result of an anova test. In addition if we specify test="F" as it is recomanded for glm using quasibinomial, the test remains a Chisq test. Are all tests scaled for dispersion, or none? Why is there a difference between glm...

...design my.svy <- svydesign(ids=~1, weights=~wt, data=dta) # subset grp1 <- subset(my.svy, grp == "Group1") # set options and clear old warnings options(warn=0) assign("last.warning", NULL, envir = baseenv()) ## proportions and CIs p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) # save warnings wrn1 <- warnings(p) ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) ## sample counts n <- unwtd.count(~grp, grp1)[1] ## combine into table overall...

...glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual deviance and df does not change between these two models, however, he accepts the quasibinomial. Is there a different calculation that I missed that actually provides an overdispersion factor (as in SAS?) My code and output are below, g...

I am running a binomial glm with response variable the no of mites of two species y->cbind(mitea,miteb) against two continuous variables (temperature and predatory mites) - see below. My model shows overdispersion as the residual deviance is 48.81 on 5 degrees of freedom. If I use quasibinomial to account for overdispersion the dispersion parameter estimate is 2501139, which seems unrealistic. Any ideas as to why I am getting such a huge dispersion parameter? > y<-cbind(psmno,wsmno) > ldhours<-log(idhours+1) > lwpm<-log(wpm2wkb+1) > y psmno wsmno [1,] 1...

...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 complex model as the sum of squares Pearson resid...

Dear R-Users, I can't understand the behaviour of quasibinomial in lmer. It doesn't appear to be calculating a scaling parameter, and looks to be reducing the standard errors of fixed effects estimates when overdispersion is present (and when it is not present also)! A simple demo of what I'm seeing is given below. Comments appreciated? Thanks, Russ...

I am unable to calculate confidence intervals for the slope estimate in a quasibinomial glm using confint(). Below is the output and the package info for MASS. Thanks in advance! R 2.9.2 MASS 7.2-48 > confint(glm.palive.0.str) Waiting for profiling to be done... Error: dims [product 37] do not match the length of object [74] > glm.palive.0.str Call: glm(formula = cbind(ali...

...gt; grp1 <- subset(my.svy, grp == "Group1") > > > > # set options and clear old warnings > > options(warn=0) > > assign("last.warning", NULL, envir = baseenv()) > > > > ## proportions and CIs > > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) > > > > # save warnings > > wrn1 <- warnings(p) > > > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) > > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) > > > > ## sam...

...~wt, data=dta) > > # subset > grp1 <- subset(my.svy, grp == "Group1") > > # set options and clear old warnings > options(warn=0) > assign("last.warning", NULL, envir = baseenv()) > > ## proportions and CIs > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) > > # save warnings > wrn1 <- warnings(p) > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) > > ## sample counts > n <- unwtd.count(~grp...

...~wt, data=dta) > > # subset > grp1 <- subset(my.svy, grp == "Group1") > > # set options and clear old warnings > options(warn=0) > assign("last.warning", NULL, envir = baseenv()) > > ## proportions and CIs > p <- ((svyciprop(~grp, grp1, family=quasibinomial))[1]) > > # save warnings > wrn1 <- warnings(p) > > ci_l <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[1]) > ci_u <- (confint(svyciprop(~grp, grp1, family=quasibinomial), 'ci')[2]) > > ## sample counts > n <- unwtd.count(~grp...

I've originally made 48 GLM binomial models and compare the AIC values. But dispersion was very large: Example: Residual deviance: 8811.6 on 118 degrees of freedom I was suggested to do a quasibinomial afterwards but found that it did not help the dispersion factor of models and received a warning: Residual deviance: 3005.7 on 67 degrees of freedom AIC: NA Number of Fisher Scoring iterations: 13 Warning message: In summary.glm(qModel48.glm) : observations with zero weight not used for calc...

...,1) > b1<-glm(y~x,binomial) > b2<-glm(y~1,binomial) > drop1(b1,test="F") Single term deletions Model: y ~ x Df Deviance AIC F value Pr(F) <none> 6.3024 10.3024 x 1 6.7301 8.7301 0.2036 0.6824 Warning message: F test assumes quasibinomial family in: drop1.glm(b1, test = "F") > anova(b2,b1,test="F") Analysis of Deviance Table Model 1: y ~ 1 Model 2: y ~ x Resid. Df Resid. Dev Df Deviance F Pr(>F) 1 4 6.7301 2 3 6.3024 1 0.4277 0.4277 0.5131 >

Hello list, i have a glm with family=binomial, link=logit but there is over-dispersion. So, in order to take into account for this problem i choose to do a glm with family=quasibinomial(). I'm not an expert on this subject and i ask if someone could validate my approach (i'm not sure for the tests) : quasi_glm(myformula,quasibinomial(),start=mystart) summary(quasi) # test t for coefficients anova(quasi,test="F") # F test (quasi glm) instead of Chisq test (cl...

...ry1Ab 3 13 4 11 15 24 32 ana Cry1Ab 4 14 3 10 13 24 33 ana Vip3A 1 21 12 3 15 24 34 ana Vip3A 2 12 6 12 18 24 35 ana Vip3A 3 9 5 15 20 24 36 ana Vip3A 4 9 1 15 16 24 =20 y<-cbind(mi,n-mi) model<-glm(y~antibio*toxin,quasibinomial) summary(model) =20 Call: glm(formula =3D y ~ antibio * toxin, family =3D quasibinomial) =20 Deviance Residuals:=20 Min 1Q Median 3Q Max=20=20 -2.0437 -0.5645 -0.1022 0.6921 1.9996=20=20 =20 Coefficients: Estimate Std. Error t value Pr(&gt...

...to compare proportions of species in different categories of seed sizes (4 categories) between 2 sites. In the model summary the residual deviance is much higher than the degree of freedom (Residual deviance: 153.74 on 4 degrees of freedom) and even after correcting for overdispersion by using a quasibinomial error structure instead of binomial the residual deviance does not change. Is this a data problem and I cannot use this statistic or is it because I do something wrong with R (see models attached)? Thanks a lot for your help! Anna first model with binomial error structure: > success<-c(...

Hi, a while ago I posted a question regarding the use of alternative models, including a quasibinomial mixed-effects model (see Results 1). I rerun the exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results 2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3). While the coefficient estimates are basically the same in all three regressions, the estimated standard er...

...(los datos están agrupados porque hay pseudoreplicación). Hemos usado el paquete "lme4" y la función "lmer". Encontramos "overdispersion" en el resultado (devianza residual mayor del doble que los grados de libertad residuales), por lo que hemos usado la familia "quasibinomial" para ajustar el modelo. El problema es que no sabemos cuál es el valor de significación del modelo porque el summary del mismo no lo da. Queríamos saber si hay alguna manera de conocer ese valor para saber si la regresión logística (el modelo mixto) es significativo o no. ¿Alguien puede apo...