# NOT RUN {
f <- mmkin(c("SFO", "FOMC", "DFOP"),
list("FOCUS A" = FOCUS_2006_A,
"FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)
AIC(f[1, "FOCUS A"]) # We get a single number for a single fit
# For FOCUS A, the models fit almost equally well, so the higher the number
# of parameters, the higher (worse) the AIC
AIC(f[, "FOCUS A"])
AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
# For FOCUS C, the more complex models fit better
AIC(f[, "FOCUS C"])
# }
Run the code above in your browser using DataLab