[R] Fitting loglinear model with glm() and loglm()

[Christofer Bogaso]

Dear all, I have small difficulty in comprehending the loglinear model
with R. Assume, we have following data

dat <- array(c(911, 44, 538, 456, 3, 2, 43, 279), c(2, 2, 2))

Now I fit a loglinear model with this and get the fitted values:

library(MASS)
Model_1 <- loglm(~1 + 2 + 3, dat)
fitted(Model_1)

I could do this same task using glm() function as well because
loglinear model is just 1 kind of glm

### Create dummy variables manually
Dummy_Variable_Matrix <- rbind(c(1, 1, 1),
							   c(0, 1, 1),
							   c(1, 0, 1),
							   c(0, 0, 1),
							
							   c(1, 1, 0),
							   c(0, 1, 0),
							   c(1, 0, 0),
							   c(0, 0, 0))

### Fit glm

model_2 <- glm(as.vector(dat) ~
						   Dummy_Variable_Matrix[,1] +
						   Dummy_Variable_Matrix[,2] +
						   Dummy_Variable_Matrix[,3],
						   poisson(link = log));
fitted(model_2)

### However................

fitted(model_2) == as.vector(fitted(Model_1)) ### do not match


However it is true that the difference is very small, still I am
wondering whether should I just ingore that small difference? Or I
have done something fundamentally wrong?

Thanks for your help!