t2 {mgcv}  R Documentation 
Alternative to te
for defining tensor product smooths
in a gam
formula. Results in a construction in which the penalties are
nonoverlapping multiples of identity matrices (with some rows and columns zeroed).
The construction, which is due to Fabian Scheipl (mgcv
implementation, 2010), is analogous to Smoothing Spline ANOVA
(Gu, 2002), but using low rank penalized regression spline marginals. The main advantage of this construction
is that it is useable with gamm4
from package gamm4
.
t2(..., k=NA,bs="cr",m=NA,d=NA,by=NA,xt=NULL, id=NULL,sp=NULL,full=FALSE,ord=NULL,pc=NULL)
... 
a list of variables that are the covariates that this
smooth is a function of. Transformations whose form depends on
the values of the data are best avoided here: e.g. 
k 
the dimension(s) of the bases used to represent the smooth term.
If not supplied then set to 
bs 
array (or single character string) specifying the type for each
marginal basis. 
m 
The order of the spline and its penalty (for smooth classes that use this) for each term.
If a single number is given then it is used for all terms. A vector can be used to
supply a different 
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 1dimensional basis, then
set 
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 
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

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 autoinitialization. Overrides values supplied in

full 
If 
ord 
an array giving the orders of terms to retain. Here order means number of marginal range spaces
used in the construction of the component. 
pc 
If not 
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. These are reparameterized so that the penalty is zero everywhere, except for some elements on the leading diagonal, which all have the same nonzero value. This reparameterization results in an unpenalized and a penalized subset of parameters, for each marginal basis (see e.g. appendix of Wood, 2004, for details).
The reparameterized marginal bases are then combined to produce a basis for a single function of all the covariates (dimension given by the product of the dimensions of the marginal bases). In this set up there are multiple penalty matrices — all zero, but for a mixture of a constant and zeros on the leading diagonal. No two penalties have a nonzero entry in the same place.
Essentially the basis for the tensor product can be thought of as being constructed from a set of
products of the penalized (range) or unpenalized (null) space bases of the marginal smooths (see Gu, 2002, section 2.4).
To construct one of the set, choose either the
null space or the range space from each marginal, and from these bases construct a product basis. The result is subject to a ridge
penalty (unless it happens to be a product entirely of marginal null spaces). The whole basis for the smooth is constructed from
all the different product bases that can be constructed in this way. The separately penalized components of the smooth basis each
have an interpretation in terms of the ANOVA  decomposition of the term.
See pen.edf
for some further information.
Note that there are two ways to construct the product. When full=FALSE
then the null space bases are treated as a whole in each product,
but when full=TRUE
each null space column is treated as a separate null space. The latter results in more penalties, but is the strict
analog of the SSANOVA approach.
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.)
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 t2.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 
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 
fx 
logical array with element for each penalty of the term
(tensor product smooths have multiple penalties). 
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 

np 

id 
the 
sp 
the 
Simon N. Wood simon.wood@rproject.org and Fabian Scheipl
Wood S.N., F. Scheipl and J.J. Faraway (2013, online Feb 2012) Straightforward intermediate rank tensor product smoothing in mixed models. Statistical Computing. 23(3):341360
Gu, C. (2002) Smoothing Spline ANOVA, Springer.
Alternative approaches to functional ANOVA decompositions, *not* implemented by t2 terms, are discussed in:
Belitz and Lang (2008) Simultaneous selection of variables and smoothing parameters in structured additive regression models. Computational Statistics & Data Analysis, 53(1):6181
Lee, DJ and M. Durban (2011) Pspline ANOVA type interaction models for spatiotemporal smoothing. Statistical Modelling, 11:4969
Wood, S.N. (2006) LowRank ScaleInvariant Tensor Product Smooths for Generalized Additive Mixed Models. Biometrics 62(4): 10251036.
# following shows how tensor product deals nicely with # badly scaled covariates (range of x 5% of range of z ) require(mgcv) test1<function(x,z,sx=0.3,sz=0.4) { x<x*20 (pi**sx*sz)*(1.2*exp((x0.2)^2/sx^2(z0.3)^2/sz^2)+ 0.8*exp((x0.7)^2/sx^2(z0.8)^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~t2(x,z)) vis.gam(b2);title("tensor product") b3<gam(y~t2(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((v0.2)^2/sv^2(w0.3)^2/sw^2)+ 0.8*exp((v0.7)^2/sv^2(w0.8)^2/sw^2)))*(u0.5)^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 Duchon spline and 1D cr spline m < list(c(1,.5),0) b < gam(y~t2(v,w,u,k=c(30,5),d=c(2,1),bs=c("ds","cr"),m=m)) ## look at the edf per penalty. "rr" denotes interaction term ## (range space range space). "rn" is interaction of null space ## for u with range space for v,w... pen.edf(b) ## plot results... 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) b < gam(y~t2(v,w,u,k=c(25,5),d=c(2,1),bs=c("tp","cr"),full=TRUE), method="ML") ## more penalties now. numbers in labels like "r1" indicate which ## basis function of a null space is involved in the term. pen.edf(b)