-Look at Chapter 6 or Section 6.3.4 in the book "Statistical Models in S" by Chambers and Hastie. Also you many want to check the package boot and function "glm.diag.plots" (Diagnostics plots for generalized linear models). Here are some code with gamma family and the plots from the help file.
data(leuk, package = "MASS")
leuk.mod <- glm(time ~ ag-1+log10(wbc), family = Gamma(log), data = leuk)
leuk.diag <- glm.diag(leuk.mod)
These plots are (upper left: residual vs linear predictor, upper right: normal scores plots of standardized deviance residuals, Lower left: approximate Cook statistics against leverage, Lower right: the plot of Cook statistic)
- See Introduction to Generalized Linear Models
- Have a look at the above reference, pages 42 and 44 to see the difference of deviance and $r^2$.
- The following code shows how to find SSE (but normally you don't need it!)
#Create a data set
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
#Fitting poisson GLM
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
# In the following, resid(glm.D93) extracts residuals of the fitted model glm.D93