Pat Wilkins <pwilkin2 <at> illinois.edu> writes: > > Greetings, > > I am running glm models for species counts using a poisson link function. > Normal summary functions for this provide summary statistics in the form of > the deviance, AIC, and p-values for individual predictors. I would like to > obtain the p-value for the overall model. So far, I have been using an > analysis of deviance table to check a model against the null model with the > intercept as the only predictor. > > Any advice on other methods to obtain the proper p-value would be > appreciated. >
What you're doing seems reasonable, although you can also dig the necessary values out of the summary and compute the p-value yourself: counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) d.AD <- data.frame(treatment, outcome, counts) glm.D93 <- glm(counts ~ outcome + treatment, family=poisson(), data=d.AD) ## as you have been doing anova(update(glm.D93,.~1),glm.D93,test="Chisq") ## or sg1 <- summary(glm.D93) devdiff <- with(sg1,null.deviance-deviance) dfdiff <- with(sg1,df.null-df.residual) pchisq(abs(devdiff),df=dfdiff,lower.tail=FALSE) ______________________________________________ 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.