Ivan Allaman <ivanalaman <at> yahoo.com.br> writes: > > > I'm trying to use the inflated binomial distribution of zeros (since 75% of > the values are zeros) in a randomized block experiment with four > quantitative treatments (0, 0.5, 1, 1.5), but I'm finding it difficult, > since the examples available in VGAM packages like for example, leave us > unsure of how it should be the data.frame for such analysis. Unfortunately > the function glm does not have an option to place a family of this kind I'm > about, because if I had, it would be easy, made that my goal is simple, just > wanting to compare the treatments. For that you have an idea, here is an > example of my database. > > BLOCK NIV NT MUMI > Inicial 0 18 0
[snip] > > where: NIV are the treatments; NT is the total number of piglets born; Mumi > is the number of mummified piglets NT. Mumi The variable is of interest. If > someone can tell me some stuff on how I can do these tests in R, similar to > what I would do using the function glm, I'd be grateful. > I thank everyone's attention. something like comparing the likelihoods of m1 <- vglm(cbind(MUMI,NT-MUMI)~NIV*BLOCK,zibinomial,data=mydata) m2 <- vglm(cbind(MUMI,NT-MUMI)~NIV+BLOCK,zibinomial,data=mydata) m3 <- vglm(cbind(MUMI,NT-MUMI)~BLOCK,zibinomial,data=mydata) I don't know whether the anova() method works for VGLM objects or not. By the way, 75% zeroes doesn't necessarily imply zero-inflation -- perhaps it just means a low incidence? ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
