GAM
R: gam vs gamm vs bam
gam
slower butgamm
giveslm
andgam
part.- marginal effects doesn't work with
gamm
(needgam
)
Knots
checkgam
scam
if monotonic
Random effects in GAM
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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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