GAM

• gam slower but gamm gives lm and gam part.
• marginal effects doesn't work with gamm (need gam)
• https://github.com/LabNeuroCogDevel/LNCDR/blob/master/R/growthrate_gam.R

• checkgam
• k.check https://rdrr.io/cran/mgcv/man/k.check.html
• knots in mgcv
• scam if monotonic

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#In gamm4
gamm4::gamm4(myelin ~ s(age, k = 4, fx = F) + sex, 
             REML = TRUE,
             random=~(1|subject_id), #random intercept
             family = gaussian(link = "identity"), 
             data = df) 

#In mgcv:gamm
mgcv::gamm(myelin ~ s(age, k = 4, fx = F) + sex, 
           method = c("REML"),
           random = list(subject_id=~1), #random intercept
           family = gaussian(link = "identity"),
           data = df)

#In mgcv:gam
mgcv:gam(myelin ~ s(age, k = 4, fx = F) + sex + 
         method = c("REML"),
         s(subject_id, bs = 're'), #random intercept, subject_id *must* be a factor
         family = gaussian(link = "identity"),
         data = df)

#### Random intercept + slopes in GAM ####

#In mgcv:gamm 
mgcv::gamm(myelin ~ s(age, k = 4, fx = F) + sex, 
           method = c("REML"),
           random=list(subject_id=~1, subject_id=~0+age), #uncorrelated random intercepts and slopes
           family = gaussian(link = "identity"),
           data = df)

mgcv::gamm(myelin ~ s(age, k = 4, fx = F) + sex, 
           method = c("REML"),
           random=list(subject_id=~1+age), #correlated random intercepts and slopes
           family = gaussian(link = "identity"),
           data = df)

#In mgcv:gam 
mgcv:gam(myelin ~ s(age, k = 4, fx = F) + sex,
      s(subject_id, bs = 're') +
      s(subject_id, age, bs = 're'),
    data = myelin.glasser.7T$projfrac0.3, method = 'REML')
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