| smoothCon {mgcv} | R Documentation |
Wrapper functions for construction of and prediction from smooth terms in a GAM. The purpose of the wrappers is to allow user-transparant re-parameterization of smooth terms, in order to allow identifiability constraints to be absorbed into the parameterization of each term, if required.
smoothCon(object,data,knots,absorb.cons=FALSE,scale.penalty=TRUE) PredictMat(object,data)
object |
is a smooth specification object or a smooth object. |
data |
A data frame containing the values of the (named) covariates at which the smooth term is to be evaluated. |
knots |
An optional data frame supplying any knot locations to be supplied for basis construction. |
absorb.cons |
Set to TRUE in order to have identifiability
constraints absorbed into the basis. |
scale.penalty |
should the penalty coefficient matrix be scaled to have
approximately the same `size' as the inner product of the terms model matrix
with itself? This can improve the performance of gamm fitting. |
These wrapper functions exist to allow smooths specified using
smooth.construct and Predict.matrix method
functions to be re-parameterized so that identifiability constraints are no
longer required in fitting. This is done in a user transparent
manner, but is typically of no importance in use of GAMs.
The parameterization used by gam can be controlled via
gam.control.
From smoothCon a smooth object returned by the
appropriate smooth.construct method function. If constraints are
to be absorbed then the object will have an attributes "qrc" and
"nCons", the qr decomposition of the constraint matrix (returned by
qr) and the number of constraints, respectively: these are used in
the re-parameterization.
For predictMat a matrix which will map the parameters associated with
the smooth to the vector of values of the smooth evaluated at the covariate
values given in object.
Simon N. Wood simon.wood@r-project.org
http://www.stats.gla.ac.uk/~simon/
gam.control,
smooth.construct, Predict.matrix