te                   package:mgcv                   R Documentation

_D_e_f_i_n_e _t_e_n_s_o_r _p_r_o_d_u_c_t _s_m_o_o_t_h_s _i_n _G_A_M _f_o_r_m_u_l_a_e

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     te(..., k=NA,bs="cr",m=0,d=NA,by=NA,fx=FALSE,mp=TRUE,np=TRUE)

_A_r_g_u_m_e_n_t_s:

     ...: 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.

      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. User defined bases can  also be used here (see
          'smooth.construct' for an example). If only one  identifier
          is given then this is used for all bases.

       m: The order of the penalty for each t.p.r.s. term (e.g. 2 for
          normal cubic spline penalty with 2nd derivatives). If a
          single number is given  then it is used for all terms. '0'
          autoinitializes. 'm' is ignored for the  '"cr"' and '"cc"'
          bases.

       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)'.

      by: specifies a covariate by which the whole smooth term is to be
          multiplied. This is particularly useful for creating models
          in which a smooth interacts with a factor: in this case the
          'by' variable would usually be the dummy variable coding one
          level of the factor. See the examples below.

      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 `natural parameterization' for a tensor
          product smooth. This parameterization represents any 1-d
          marginal smooths using a parameterization where the
          parameters are function values at `knots' spread evenly
          through the data. The parameterization make the penalties
          easily interpretable.

_D_e_t_a_i_l_s:

     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 `natural 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.

     The function does not evaluate the variable arguments.

_V_a_l_u_e:

     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. 

full.call: Text for pasting into a string to be converted to a gam
          formula, which has the values of function options given
          explicitly - this is useful for constructing a fully expanded
          gam formula which can be used without needing access to any
          variables that may have been used to define k, fx, bs or m in
          the original call. i.e. this is text which when parsed and
          evaluated generates a call to 's()' with all the options
          spelled out explicitly.

   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).

_A_u_t_h_o_r(_s):

     Simon N. Wood simon.wood@r-project.org

_R_e_f_e_r_e_n_c_e_s:

     Wood, S.N. (2004) Low rank scale invariant tensor product smooths
     for Generalized Additive Mixed Models. Technical Report of the
     Department of Statistics, University of Glasgow, UK.

     <URL: http://www.stats.gla.ac.uk/~simon/>

_S_e_e _A_l_s_o:

     's','gam','gamm'

_E_x_a_m_p_l_e_s:

     # 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 a 2-d thin plate regression spline and 1-d 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)

