What AIC is necessary to select this model? I have read this page but am a little confused and I think a real example might help solidify the idea in my mind regarding how to use the AIC in model selection.
Equivalence of AIC and p-values in model selection
Say I have two nested models (that differ in 6 parameters) - these are from real data. The simpler model is mod1 and the more complex, mod2.
> AIC(mod1)
[1] 191.2335
> AIC(mod2)
[1] 190.5257

> BIC(mod1)
[1] 206.9418
> BIC(mod2)
[1] 225.084

> lmtest::lrtest(mod1,mod2)
Likelihood ratio test

Model 1: y ~ x1 + x2 + x3 + x4
Model 2: y ~ x1(NLR) + rcs(x2) + x3 + x4
  #Df  LogLik Df  Chisq Pr(>Chisq)  
1   5 -90.617                       
2  11 -84.263  6 12.708    0.04792 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

The p-value from the LR test would suggest that the model fit is compromised with the simpler model (and thus mod2 is better).
The AIC is lower for mod2 but not by a margin that I think suggests it's the optimal fit. What I want to know is how much lower (on 6 df) does the AIC need to be for mod2 (the more complex model in this case) to justify the extra parameters? Is it 6 x 2 = 12 units?
Clearly the BIC would suggest mod1.

AIC takes model complexity into account by construction. You do not need to adjust for that any further. If you like adjusting for model complexity manually, you can compare likelihoods and adjust them. But then you end up with... AIC (or BIC, or some other IC)! So there is really no point in doing that by hand if you have AIC available.
If the AIC of model 2 is lower than the AIC of model 1 (AIC2<AIC1), then the estimated expected likelihood of a new observation from the same data generating process is higher in model 2. If that is what you are looking for, select model 2. If you are looking for something else, perhaps AIC is not the criterion you want to use.
(You may also consider model averaging, especially if the differences in AICs between the models are not too large.)
