library(faraway) data(gala) gala <- gala[,-2] modl <- lm(Species ~ . , gala) plot(predict(modl),residuals(modl),xlab="Fitted",ylab="Residuals") modt <- lm(sqrt(Species) ~ . , gala) plot(predict(modt),residuals(modt),xlab="Fitted",ylab="Residuals") summary(modt) modp <- glm(Species ~ .,family=poisson,gala) summary(modp) halfnorm(residuals(modp)) plot(log(fitted(modp)),log((gala$Species-fitted(modp))^2),xlab=expression(hat(mu)),ylab=expression((y-hat(mu))^2)) abline(0,1) (dp <- sum(residuals(modp,type="pearson")^2)/modp$df.res) summary(modp,dispersion=dp) drop1(modp,test="F") data(dicentric) round(xtabs(ca/cells ~ doseamt+doserate, dicentric),2) with(dicentric,interaction.plot(doseamt,doserate,ca/cells)) lmod <- lm(ca/cells ~ log(doserate)*factor(doseamt), dicentric) summary(lmod)$adj plot(residuals(lmod) ~ fitted(lmod),xlab="Fitted",ylab="Residuals") abline(h=0) dicentric$dosef <- factor(dicentric$doseamt) pmod <- glm(ca ~ log(cells)+log(doserate)*dosef, family=poisson,dicentric) summary(pmod) rmod <- glm(ca ~ offset(log(cells))+log(doserate)*dosef, family=poisson,dicentric) summary(rmod) data(solder) modp <- glm(skips ~ . , family=poisson, data=solder) deviance(modp) df.residual(modp) modp2 <- glm(skips ~ (Opening +Solder + Mask + PadType + Panel)^2 , family=poisson, data=solder) deviance(modp2) pchisq(deviance(modp2),df.residual(modp2),lower=FALSE) library(MASS) modn <- glm(skips ~ .,negative.binomial(1),solder) modn modn <- glm.nb(skips ~ .,solder) summary(modn)