On Tue, 20 Mar 2012, Christofer Bogaso wrote:
Dear all, I have small difficulty in comprehending the loglinear model with R. Assume, we have following datadat <- 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?
The fitted values are not the same (==) but equal up to some tolerance appropriate for floating point numbers (see all.equal).
The reason is that different numeric algorithms are employed for maximizing the log-likelihood. loglm() internally uses loglin() which uses iterative proportional fitting. glm() internally uses glm.fit() which performs iterative weighted least squares.
BTW: Setting up frequencies and factors for glm() modeling based on a table can be done more easily by coercing the "array" to a "table" and then to a "data.frame":
tab <- as.table(dat) m1 <- loglm(~ 1 + 2 + 3, data = tab) dframe <- as.data.frame(tab) m2 <- glm(Freq ~ Var1 + Var2 + Var3, data = dframe, family = poisson) all.equal(as.vector(fitted(m1)), as.vector(fitted(m2))) ## TRUE Also, the LR and Pearson statistics from print(m1) can be reproduced via sum(residuals(m2, type = "deviance")^2) sum(residuals(m2, type = "pearson")^2) Hope that helps, Z
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______________________________________________ R-help@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.
