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. Thanks, Pat [[alternative HTML version deleted]]
Not really an R question. Post on a statistics site like http://stats.stackexchange.com/ -- Bert On Mon, Apr 9, 2012 at 9:51 AM, Pat Wilkins <pwilkin2 at illinois.edu> wrote:> 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. > > Thanks, > > Pat > > ? ? ? ?[[alternative HTML version deleted]] > > ______________________________________________ > R-help at 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.-- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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)