#' --- #' title: "Diagnostic plots for berkeley data" #' author: "Michael Friendly" #' date: "`r format(Sys.Date())`" #' output: #' html_document: #' theme: readable #' code_download: true #' --- #+ echo=FALSE knitr::opts_chunk$set( warning = FALSE, # avoid warnings and messages in the output message = FALSE ) library(car) #' ## Prepare data frame for plotting berkeley <- as.data.frame(UCBAdmissions) cellID <- paste(berkeley$Dept, substr(berkeley$Gender,1,1), '-', substr(berkeley$Admit,1,3), sep="") rownames(berkeley) <- cellID #' ## using glm() berk.mod <- glm(Freq ~ Dept * (Gender+Admit), data=berkeley, family="poisson") summary(berk.mod) #' ## Influence plot influencePlot(berk.mod, id=list(n=3, labels=cellID)) op <- par(mfrow = c(2,2)) plot(berk.mod) par(op)