Unlabeled Log Linear Analysis

lla-unlabeled.r

# Get the data and factor it as needed
data = data.frame()

# Create contingency tables
data.table = xtabs(~A + B + C, data=data)

# Create models
library(MASS)
model.0 = loglm(~A + B + C + A:B + A:C + B:C + A:B:C, data = table.phishingABC, fit=TRUE)
model.1 = loglm(~A + B + C + A:B + A:C + B:C, data = table.phishingABC, fit=TRUE)
model.2 = loglm(~A + B + C + A:B + A:C, data = table.phishingABC, fit=TRUE)
model.3 = loglm(~A + B + C + A:B + B:C, data = table.phishingABC, fit=TRUE)
model.4 = loglm(~A + B + C + A:C + B:C, data = table.phishingABC, fit=TRUE)
model.5 = loglm(~A + B + A:B, data = table.phishingABC, fit=TRUE)
model.6 = loglm(~A + C + A:C, data = table.phishingABC, fit=TRUE)
model.7 = loglm(~B + C + B:C, data = table.phishingABC, fit=TRUE)
model.8 = loglm(~A + B + C, data = table.phishingABC, fit=TRUE)

#Determine which model you like the best