similar to: anova.glm needs y = TRUE in glm() (PR#13098)

Hi, I am trying to fit a negative binomial model with a number of parasite tapeworms as response variable to geographical coordinates (actually preparing a trend surface before kriging). When I try an anova, I get warnings: > glm4.nb<-glm.nb(wb~X4+Y4+I(X4^2)+I(Y4^2)) > anova(glm4.nb) Analysis of Deviance Table Model: Negative Binomial(0.0463), link: log Response: wb Terms added

I have *not* tracked this bug very far because I hope someone who is more familiar with the glm/anova code will jump in. If one fits a glm model with an offset, the NULL deviance is incorrectly calculated (it is calculated without the offset). The means one can get examples where the model deviance is greater than the NULL deviance. Here is some example output, (without the data). >

I have *not* tracked this bug very far because I hope someone who is more familiar with the glm/anova code will jump in. If one fits a glm model with an offset, the NULL deviance is incorrectly calculated (it is calculated without the offset). The means one can get examples where the model deviance is greater than the NULL deviance. Here is some example output, (without the data). >

I am fitting a logistic model to binary data. The response variable is a factor (0 or 1) and all predictors are continuous variables. The main predictor is LT (I expect a logistic relation between LT and the probability of being mature) and the other are variables I expect to modify this relation. I want to test if all predictors contribute significantly for the fit or not I fit the full

Full_Name: Sebastian Meyer Version: 2.10.0 OS: x86_64-pc-linux-gnu Submission from: (NULL) (77.135.124.96) When using model.frame (model.frame.default) on an intercept-only model and with na.action=na.fail SPECIFIED, i.e. data(iris) model.frame( ~ 1, data = iris, na.action = na.fail) one gets the following error: Error in complete.cases(object) : negative length vectors are not

> 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 guess I understand it now (although it is surprising to me). The following is a valid model formula fred ~ wilma + offset(barney) that sets the model offset to barney. Given that this works, it would seem that one could remove the offset = argument from the glm call (and document the offset feature somewhere). Too bad that one can't set weights the same way. The anova bug when offsets

Dear All, This is my first post, and probably (and hence apologies that) my question is very silly! I'm having issues with a the mvabund package (http://cran.r-project.org/web/packages/mvabund/index.html), and would be great to get some help! Here is the code (and files are attached): library(mvabund) ##visualizing data florabund <- read.csv("CPL_floristics_abund_v1d.csv",

Dear R users, I am trying to analyze multivariate abundance data using the mvabund package, but errors occur for several functions. I created a mvabund object named faunadat consisting of 8 dependent variables each containing 64 positive integer values (bact, fung, plant, omn, pred, orib, meso, spring). There are 4 factors: harv, rep, depth and lit. I then created a formula: >

Hello everybody, In order to predict income for different time points, I fitted a linear model with polynomial effects using BMA (bicreg(...)). It works fine, the results are consistent with what we are looking for. Now, we would like to predict income for a future time point t_next and of course draw the prediction interval around the estimated value for this point t_next. I've found the

Hi, this is more related to understanding some statistics while using R; I've see such output in a paper: out <- glm(response~Var1+Var2+Var3..,family=binomial,data=mydata) summary(out) stepAIC(out) anova(out, test='Chisq') I understand that stepAIC is used to select the model with the lowest AIC (the best model) but can someone explain what is the purpose of doing the anova:

When using test="F" in stat.anova() / anova.glm(), R uses the assumed dispersion parameter for the specified family (e.g. scale=1 for binomial), while S-PLUS automatically uses the estimated dispersion parameter (residual deviance/residual df). I think there are good reasons for the behavior in R -- it fits with the "you get what you actually asked for" philosophy -- and

It appears that file.copy() does not protect against the case when one copies one directory to a subdirectory of itself resulting in it trying to create and endless recursive copy of itself. REPRODUCIBLE EXAMPLE: src <- "foo" dir.create(src); file.copy(src, src, recursive=FALSE) # ok file.copy(src, src, recursive=TRUE) # PROBLEM

Hallo, zur Analyse von Daten zum Artenreichtum von Pflanzen, habe ich ein Glm (glm) und anschlie?end eine Anova (anova) durchgef??hrt. Nun m??chte ich f??r die signifikanten Einflussfaktoren einen Post Hoc Tukey Test durchf??hren, um zu ermitteln in wie weit die einzelnen Faktorstufen sich signifikant voneinander unterscheiden. Mit dem Befehl (TukeyHSD) komme ich nicht

Dear R team, I don't get what I think I should get when using anova.glm() with family="gaussian" -- please ignore this and forgive me if this turns out to be another example of a fundamental misunderstanding on my part (a highly likely event!) For example: S <- as.factor(rep(c(rep("m",2),rep("f",2)),2)) A <-

Hi All, I am using the glm.nb function from the MASS package (current version) to fit and compare GLMs with negative binomial error distributions. My question is: what is the appropriate method to use in the anova function to compare models? If only one fitted object like m<-glm.nb(number<-p+sal+temp,data=data) is specified in the anova function (anova(m)), a fixed theta is used to

Dear R users I want to evaluate my significant interactions among 3 predictors in glm (one is a factor and 2 are numeric indices). I had a 3rd order interaction in my anova.glm and I'm wondering how to investigate them. I read multcomp package but I found it difficult for me. #Say e.g. (I'm not using real values and in this e.g. does not have the interactions I'm saying - but my data

There may be a bug in the anova.glm function. deathstar[32] R R : Copyright 2004, The R Foundation for Statistical Computing Version 2.0.1 (2004-11-15), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project

Full_Name: Robert King Version: 1.5.0 OS: windows Submission from: (NULL) (134.148.4.19) Also occurs in 1.6.0 on linux anova.glm(fitted.object,test="Chisq") is giving strange answers in this situation > resptime sex task time 1 m s 210 2 m s 300 3 m s 420 4 f s 250 5 f s 310 6 f s 390 7 m c 310 8 m c 400 9 m c 600

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat :