The functional form is given in chapter 4 of my book:
Wood S.N. (2006) Generalized Additive Models: An Introduction with R.
Chapman and Hall/CRC Press. (reserve your copy now for christmas)
... but note that by default mgcv reparameterises so that the
identifiability constraints on the smooths are satisfied automatically
(also covered in chapter 4 of the same book).
You can get at the evaluated basis functions from the columns of the
result of predict.gam(...,type=lpmatrix) - see help files for
details. The penalty matrices are stored in the `smooth' list element
of the fitted `gam' object. For example x$smooth[[1]]$S[[1]].
best,
Simon
Quoting Yan Li leemom.atlanta2...@gmail.com:
I have a question about the basis functions of cubic regression spline in
mgcv. Are there some ways I can get the exact forms of the basis functions
and the penalty matrix that are used in mgcv? Thanks in advance!
Yan
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