| 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. The routine also handles `by' variables and construction of identifiability constraints automatically, although this behaviour can be over-ridden.
smoothCon(object,data,knots,absorb.cons=FALSE,
scale.penalty=TRUE,n=nrow(data),dataX=NULL,
null.space.penalty=FALSE)
PredictMat(object,data,n=nrow(data))
object |
is a smooth specification object or a smooth object. |
data |
A data frame, model frame or list containing the values of the
(named) covariates at which the smooth term is to be
evaluated. If it's a list then n must be supplied. |
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. |
n |
number of values for each covariate, or if a covariate is a matrix,
the number of rows in that matrix: must be supplied explicitly if data is a list. |
dataX |
Sometimes the basis should be set up using data in data, but the model matrix
should be constructed with another set of data provided in dataX — n is assumed to
be the same for both. Facilitates smooth id's. |
null.space.penalty |
Should an extra penalty be added to the smooth which will penalize the components of the smooth in the penalty null space: provides a way of penalizing terms out of the model altogether. |
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 routine's
also handle by variables and will create default identifiability
constraints.
If a user defined smooth constructor handles by variables itself, then its
returned smooth object should contain an object by.done. If this does not exist
then smoothCon will use the default code. Similarly if a user defined Predict.matrix
method handles by variables internally then the returned matrix should have a
"by.done" attribute.
Default centering constraints, that terms should sum to zero over the covariates, are produced unless
the smooth constructor includes a matrix C of constraints. To have no constraints (in which case
you had better have a full rank penalty!) the matrix C should have no rows.
smoothCon returns a list of smooths because factor by variables result in multiple copies
of a smooth, each multiplied by the dummy variable associated with one factor level. smoothCon modifies
the smooth object labels in the presence of by variables, to ensure that they are unique, it also stores
the level of a by variable factor associated with a smooth, for later use by PredMat.
The parameterization used by gam can be controlled via
gam.control.
From smoothCon a list of smooth objects returned by the
appropriate smooth.construct method function. If constraints are
to be absorbed then the objects will have 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.maths.bath.ac.uk/~sw283/
gam.control,
smooth.construct, Predict.matrix