Use a p-spline (penalised spine) to model main effects or interactions. Typically used with age, but can be used with any variable where outcomes are expected to vary smoothly from one element to the next.
Details
If Sp()
is used with an interaction,
separate splines are used for the "along" variable within
each combination of the
"by" variables.
Mathematical details
When Sp()
is used with a main effect,
$$\pmb{\beta} = \pmb{X} \pmb{\alpha}$$
and when it is used with an interaction,
$$\pmb{\beta}_u = \pmb{X} \pmb{\alpha}_u$$
where
\(\pmb{\beta}\) is the main effect or interaction, with \(J\) elements;
\(\pmb{beta}_u\) is a subvector of \(\pmb{\beta}\) holding values for the \(u\)th combination of the "by" variables;
\(J\) is the number of elements of \(\pmb{\beta}\);
\(U\) is the number of elements of \(\pmb{\beta}_u\);
\(X\) is a \(J \times n\) or \(V \times n\) matrix of spline basis functions; and
\(n\) is
n_comp
.
The elements of \(\pmb{\alpha}\) or \(\pmb{alpha}_u\) are assumed to follow a second-order random walk.
References
Eilers, P.H.C. and Marx B. (1996). "Flexible smoothing with B-splines and penalties". Statistical Science. 11 (2): 89–121.
See also
RW()
Smoothing via random walkRW2()
Smoothing via second-order random walkSVD()
Smoothing of age via singular value decompositionpriors Overview of priors implemented in bage
set_prior()
Specify prior for intercept, main effect, or interactionbage uses function
splines::bs()
to construct spline basis functions