AIC between glm and gam What are the consequences of comparing the AIC from a logistic regression model (glm) to the AIC from a generalized additive model (gam with binomial link and REML)? Here's an example of two models, are the AIC values comparable?
library(mgcv)

nobs <- 100

set.seed(1)

x1 <- rnorm(nobs, 3, 3)
x2 <- rnorm(nobs)

z <- -2 + 2*x1 - 0.5*x1^2 + rnorm(nobs)

y <- rbinom(n=nobs, size=1, 
            prob={exp(z) / (1 + exp(z))})

df <- data.frame(y=y, x1=x1, x2=x2)

rm(x1, x2, z, y)

logit_mod <- glm(y ~ poly(x1, 2, raw=TRUE) + x2, 
                 family=binomial, data=df)

summary(logit_mod)

mean_x1 <- mean(df$x1)
mean_x2 <- mean(df$x2)

newdata = data.frame(x1=mean_x1, x2=mean_x2)

predict(logit_mod, newdata=newdata, 
        type="response")

plogis(coef(logit_mod)[1])

gam_mod <- gam(y ~ s(x1) + s(x2), 
               family=binomial, method="REML", 
               data=df, select=TRUE)

summary(gam_mod)

predict(gam_mod, newdata=newdata, 
          type="response")

plogis(coef(gam_mod)[1])

plot(gam_mod, pages=1, all.terms=TRUE)

sapply(list(logit_mod, gam_mod), AIC)

```

 A: Yes, they are comparable, but you shouldn't be using REML to compare models with different fixed effects. Use method = "ML" in the gam() call if you are comparing your polynomial fits with the smooth version.
You could fit your GLM via gam() instead and as there are no penalised terms it would be fitted using the same algorithm as used by glm().
r$> m_glm <- glm(y ~ poly(x1, 2, raw = TRUE) + x2, family = binomial, data = df)
r$> m_glm_2 <- gam(y ~ poly(x1, 2, raw = TRUE) + x2,
                     family = binomial, data = df, method = "ML")
r$> m_gam <- gam(y ~ s(x1) + s(x2),
                    family = binomial, data = df, method = "ML")           
r$> AIC(m_glm, m_glm_2, m_gam)            
              df      AIC
m_glm   4.000000 75.72616
m_glm_2 4.000000 75.72616
m_gam   5.072922 78.50021

As there may be slight implementation differences, I would fit the GLM via gam(), and note that AIC() for a GAM in mgcv is going to be correcting for the smoothing parameters having been estimated, which involves a correction to the EDF of the model (though this is taken care of for you via the logLik() method for "gam" classed objects.
