Hi, just a minor comment below. Bob O'Hara píše v So 14. 06. 2014 v 09:45 +0200: > On 06/14/2014 03:05 AM, Luis Fernando Garca wrote: > > Dear all, > > > > I am making an analysis using a GLM using three explanatory variables and a > > response variable. I need to obtain a table similar to this one, > > http://postimg.org/image/5sau79wlt/r > > > > nevertheless, I have not been able to do it. I am having a hard time > > specially getting the chi square values. I would like to know how to obatin > > them. > Use anova(). The deviance follows a chi-squared distribution (usually - > if you have overdispersion it gets a bit more complicated). > > I also would like to know what function could help me to make ad hoc > > comparisons for single variables and interactions. > These comparisons are called contrasts. There is a contrasts() function > in R, and also a contrast package (which, I'm guessing will be of more > use). Googling "R contrast" might help too - there seems to be plenty of > material, so hopefully one or two results will be exactly what you want. > Contrasts can get esoteric, so if you can find some a page with code > that gives you the comparisons you want, that should help a lot. > > Good luck! > > Bob > > > If any of you knows how to do both estimations, I would really appreciate > > it. > > > > All the best!!! > > > > This is my script > > a=read.table("ricis3.txt",header=T) > > attach(a) > > model7=glm(Count~Sex+Time+Behaviour+Sex*Time+Sex*Behaviour+Time+Behaviour*Sex,family=poisson) > > summary(model7) It seem so me that your model is misspecified: if expanded and reordered, your model would look like: Count~Sex+Sex+Sex+Sex+Time+Time+Time+Behaviour+Behaviour+Behaviour +Sex:Time+Sex:Behaviour+Sex:Behaviour
So: note the ":" and "*" difference, see help(formula) Some less related tip: step-by-step work with deviance (anova) tables and contrasts is done in Crawley: The R book. Even more distant things, ignore if familiar with glms: a,if residual deviance of your model >> degrees of freedom, you have overdispersion (see summary(your.model)). Try family=quasipoisson and testing with F distribution instead of Chi (test="F" in the anova command) then (or glm.nb from the MASS package). b, if numerical problems occur, try to standardise your predictors: subtract each predictor's mean from the actual values (so mean(new.predictor)==0 (not exactly, machine precision)), divide the rest by the standard deviation of the original predictor (so most of the values fall within -3,+3), see help(scale). Beware, you loose your original scale with this. HTH. Best, Martin > > _______________________________________________ > > R-sig-ecology mailing list > > R-sig-ecology@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- ------------------------------ Pokud je tento e-mail součástí obchodního jednání, Přírodovědecká fakulta Univerzity Karlovy v Praze: a) si vyhrazuje právo jednání kdykoliv ukončit a to i bez uvedení důvodu, b) stanovuje, že smlouva musí mít písemnou formu, c) vylučuje přijetí nabídky s dodatkem či odchylkou, d) stanovuje, že smlouva je uzavřena teprve výslovným dosažením shody na všech náležitostech smlouvy. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology