| te {mgcv} | R Documentation |
Function used in definition of tensor product smooth terms within
gam model formulae. The function does not evaluate a
smooth - it exists purely to help set up a model using tensor product
based smooths. Designed to construct tensor products from any marginal
smooths with a basis-penalty representation (with the restriction that each
marginal smooth must have only one penalty).
te(..., k=NA,bs="cr",m=NA,d=NA,by=NA,fx=FALSE,
mp=TRUE,np=TRUE,xt=NULL,id=NULL,sp=NULL)
... |
a list of variables that are the covariates that this smooth is a function of. |
k |
the dimension(s) of the bases used to represent the smooth term.
If not supplied then set to 5^d. If supplied as a single number then this
basis dimension is used for each basis. If supplied as an array then the elements are
the dimensions of the component (marginal) bases of the tensor
product. See choose.k for further information. |
bs |
array (or single character string) specifying the type for each
marginal basis. "cr" for cubic regression spline; "cs" for cubic
regression spline with shrinkage; "cc" for periodic/cyclic
cubic regression spline; "tp" for thin plate regression spline;
"ts" for t.p.r.s. with extra shrinkage. See smooth.terms for details
and full list. User defined bases can
also be used here (see smooth.construct for an example). If only one
basis code is given then this is used for all bases. |
m |
The order of the penalty (for smooth classes that use this) for each term.
If a single number is given then it is used for all terms. NA autoinitializes.
m is ignored by some bases (e.g. "cr"). |
d |
array of marginal basis dimensions. For example if you want a smooth for 3 covariates
made up of a tensor product of a 2 dimensional t.p.r.s. basis and a 1-dimensional basis, then
set d=c(2,1). Incompatibilities between built in basis types and dimension will be
resolved by resetting the basis type. |
by |
a numeric or factor variable of the same dimension as each covariate.
In the numeric vector case the elements multiply the smooth evaluated at the corresponding
covariate values (a `varying coefficient model' results).
In the factor case causes a replicate of the smooth to be produced for
each factor level. See gam.models for further details. May also be a matrix
if covariates are matrices: in this case implements linear functional of a smooth
(see gam.models and linear.functional.terms for details). |
fx |
indicates whether the term is a fixed d.f. regression
spline (TRUE) or a penalized regression spline (FALSE). |
mp |
TRUE to use multiple penalties for the smooth. FALSE to use only
a single penalty: single penalties are not recommended - they tend to allow only rather
wiggly models. |
np |
TRUE to use the `normal parameterization' for a tensor
product smooth. This represents any 1-d marginal smooths
via parameters that are function values at `knots',
spread evenly through the data. The parameterization makes the penalties
easily interpretable, however it can reduce numerical stability in some cases. |
xt |
Either a single object, providing any extra information to be passed to each marginal basis constructor, or a list of such objects, one for each marginal basis. |
id |
A label or integer identifying this term in order to link its smoothing
parameters to others of the same type. If two or more smooth terms have the same
id then they will have the same smoothing paramsters, and, by default,
the same bases (first occurance defines basis type, but data from all terms
used in basis construction). |
sp |
any supplied smoothing parameters for this term. Must be an array of the same
length as the number of penalties for this smooth. Positive or zero elements are taken as fixed
smoothing parameters. Negative elements signal auto-initialization. Over-rides values supplied in
sp argument to gam. Ignored by gamm. |
Smooths of several covariates can be constructed from tensor products of the bases
used to represent smooths of one (or sometimes more) of the covariates. To do this `marginal' bases
are produced with associated model matrices and penalty matrices, and these are then combined in the
manner described in tensor.prod.model.matrix and tensor.prod.penalties, to produce
a single model matrix for the smooth, but multiple penalties (one for each marginal basis). The basis dimension
of the whole smooth is the product of the basis dimensions of the marginal smooths.
An option for operating with a single penalty (The Kronecker product of the marginal penalties) is provided, but it is rarely of practical use: the penalty is typically so rank deficient that even the smoothest resulting model will have rather high estimated degrees of freedom.
Tensor product smooths are especially useful for representing functions of covariates measured in different units, although they are typically not quite as nicely behaved as t.p.r.s. smooths for well scaled covariates.
Note also that GAMs constructed from lower rank tensor product smooths are nested within GAMs constructed from higher rank tensor product smooths if the same marginal bases are used in both cases (the marginal smooths themselves are just special cases of tensor product smooths.)
The `normal parameterization' (np=TRUE) re-parameterizes the marginal
smooths of a tensor product smooth so that the parameters are function values
at a set of points spread evenly through the range of values of the covariate
of the smooth. This means that the penalty of the tensor product associated
with any particular covariate direction can be interpreted as the penalty of
the appropriate marginal smooth applied in that direction and averaged over
the smooth. Currently this is only done for marginals of a single
variable. This parameterization can reduce numerical stability when used
with marginal smooths other than "cc", "cr" and "cs": if
this causes problems, set np=FALSE.
Note that tensor product smooths should not be centred (have identifiability constraints imposed) if any marginals would not need centering. The constructor for tensor product smooths ensures that this happens.
The function does not evaluate the variable arguments.
A class tensor.smooth.spec object defining a tensor product smooth
to be turned into a basis and penalties by the smooth.construct.tensor.smooth.spec function.
The returned object contains the following items:
margin |
A list of smooth.spec objects of the type returned by s,
defining the basis from which the tensor product smooth is constructed. |
term |
An array of text strings giving the names of the covariates that the term is a function of. |
by |
is the name of any by variable as text ("NA" for none). |
fx |
logical array with element for each penalty of the term
(tensor product smooths have multiple penalties). TRUE if the penalty is to
be ignored, FALSE, otherwise. |
label |
A suitable text label for this smooth term. |
dim |
The dimension of the smoother - i.e. the number of covariates that it is a function of. |
mp |
TRUE is multiple penalties are to be used (default). |
np |
TRUE to re-parameterize 1-D marginal smooths in terms of function
values (defualt). |
id |
the id argument supplied to te. |
sp |
the sp argument supplied to te. |
Simon N. Wood simon.wood@r-project.org
Wood, S.N. (2006a) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036
http://www.maths.bath.ac.uk/~sw283/
s,gam,gamm,
smooth.construct.tensor.smooth.spec
# following shows how tensor pruduct deals nicely with
# badly scaled covariates (range of x 5% of range of z )
test1<-function(x,z,sx=0.3,sz=0.4)
{ x<-x*20
(pi**sx*sz)*(1.2*exp(-(x-0.2)^2/sx^2-(z-0.3)^2/sz^2)+
0.8*exp(-(x-0.7)^2/sx^2-(z-0.8)^2/sz^2))
}
n<-500
old.par<-par(mfrow=c(2,2))
x<-runif(n)/20;z<-runif(n);
xs<-seq(0,1,length=30)/20;zs<-seq(0,1,length=30)
pr<-data.frame(x=rep(xs,30),z=rep(zs,rep(30,30)))
truth<-matrix(test1(pr$x,pr$z),30,30)
f <- test1(x,z)
y <- f + rnorm(n)*0.2
b1<-gam(y~s(x,z))
persp(xs,zs,truth);title("truth")
vis.gam(b1);title("t.p.r.s")
b2<-gam(y~te(x,z))
vis.gam(b2);title("tensor product")
b3<-gam(y~te(x,z,bs=c("tp","tp")))
vis.gam(b3);title("tensor product")
par(old.par)
test2<-function(u,v,w,sv=0.3,sw=0.4)
{ ((pi**sv*sw)*(1.2*exp(-(v-0.2)^2/sv^2-(w-0.3)^2/sw^2)+
0.8*exp(-(v-0.7)^2/sv^2-(w-0.8)^2/sw^2)))*(u-0.5)^2*20
}
n <- 500
v <- runif(n);w<-runif(n);u<-runif(n)
f <- test2(u,v,w)
y <- f + rnorm(n)*0.2
# tensor product of 2D thin plate regression spline and 1D cr spline
b <- gam(y~te(v,w,u,k=c(30,5),d=c(2,1),bs=c("tp","cr")))
op <- par(mfrow=c(2,2))
vis.gam(b,cond=list(u=0),color="heat",zlim=c(-0.2,3.5))
vis.gam(b,cond=list(u=.33),color="heat",zlim=c(-0.2,3.5))
vis.gam(b,cond=list(u=.67),color="heat",zlim=c(-0.2,3.5))
vis.gam(b,cond=list(u=1),color="heat",zlim=c(-0.2,3.5))
par(op)