similar to: GLM - fixed responses?

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 for each section of the

Hi All, I am wondering if anyone can tell me what the warning message below the model means? J add1(DTA.glm,~ Aeventexhumed + Veg + Berm + HTL + Estuary + Rayos) Single term additions Model: cbind(MaxHatch, TotalEggs - MaxHatch) ~ Aeventexhumed + Veg + Berm + HTL Df Deviance AIC <none> 488.86 4232.9 Estuary 1 454.96 4201.0 Rayos 3 258.80 4008.9 Warning

I've originally made 48 GLM binomial models and compare the AIC values.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

Hi, I'm using a GLM with a quasi binomial error distribution and I would like to do a model selection method similar to step(AIC) to carry out a restricted search for the "best" model. I would like to know which of my 5 predictor variables would be included in the "best" model if I start with a 'full' model (fullbinom in this case). However, AIC can't be

Should the F statistic be the same when using add1() on models created by lm and glm(family=gaussian)? They are in the single-degree-of-freedom case but not in the multiple-degree-of-freedom case. MASS:addterm shows the same discrepancy. It looks like the deviance (==residual sum of squares) gets divided by the number of degrees of freedom for the term twice in add1.glm. Using anova() on the

Dear all, I am new to R and my question may be trivial to you... I am doing a GLM with binomial errors 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

> Hello, > > I have a question about a discrepancy between the > reported F statistics using anova() and add1() from > adding an additional term to form nested models. > > I found and old posting related to anova() and > drop1() regarding a glm with a dispersion parameter. > > The posting is very old (May 2000, R 1.1.0). > The old posting is located here. >

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(alive, red) ~ str,

In this real example (below), all four of the replicates in one treatment combination had zero failures, and this produced a very high standard error in the summary.lm. =20 Just adding one failure to one of the replicates produced a well-behaved standard error. =20 I don't know if this is a bug, but it is certainly hard for users to understand. =20 I would value your comments=20 =20 Thanks =20

Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively

I'm having a bit of difficulty using the stepwise model-building tools in a glm context. Here, for example is one problem I have had using add1, where the abbreviation "." does not work as I expected it to do. I someone could point me towards some examples involving the interactive building of glm models I would be grateful. The data set that I am using is the

Hi there - I am using R1.5.1 on WinNT and the latest MASS (Venables and Ripley) library. Running the following code: >minimod<-glm(miniSF~gtbt*f.batch+log(mxjd),data=gtbt,family="binomial") >summary(minimod,cor=F) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.91561 0.32655 2.804 0.005049 ** gtbtgt 0.47171

Hi R-listers, I have developed 47 GLM models with different combinations of interactions from 1 variable to 5 variables. I have manually made each model separately and put them into individual tables (organized by the number of variables) showing the AIC score. I want to compare all of these models. 1) What is the best way to compare various models with unique combinations and different number

Dear R-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the

Dear all, I am using glm with quasibinomial. What does the following error message mean: Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1 Does it mean that the predictor variable should only have zero and one or it is also possible to have continuous values between zero and one? Many thanks, Samuel [[alternative HTML version deleted]]

Hi, Did anyone sucessfully install SJava package to R and was able to call R function from java in redhat linux8.0? I tried several days it still give me error either in libR.so or libjvm.so. for example, I can compile JavaRCall.java(a example with the SJava package) without problem. When I run it, it can connect to R and accomplish part of results, but fail for other calls. The outputs are:

I am attempting to run a 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

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 and lmer

Hello R-list. I am a "long time listener - first 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

David, Thanks. The reason add1.lm (and drop1.lm) do not support offsets is that lm did not when they were written, and the person who added offsets to lm did not change them. (I do wish they had not added an offset arg and just used the formula as in S's glm.) That is easy to add. For the other point, some care is needed if 'x' is supplied and the upper scope reduces the number