Predict.matrix             package:mgcv             R Documentation

_P_r_e_d_i_c_t_i_o_n _m_e_t_h_o_d_s _f_o_r _s_m_o_o_t_h _t_e_r_m_s _i_n _a _G_A_M

_D_e_s_c_r_i_p_t_i_o_n:

     Takes 'smooth' objects produced by 'smooth.construct' methods and
     obtains the matrix mapping  the parameters associated with such a
     smooth to the predicted values of the smooth at a set of new
     covariate values.

     In practice this method is often called via the wrapper function
     'PredictMat'.

_U_s_a_g_e:

     Predict.matrix(object,data)

_A_r_g_u_m_e_n_t_s:

  object: is a smooth object produced by a 'smooth.construct' method
          function. The object  contains all the information required
          to specify the basis for a term of its class, and this
          information is used by the appropriate 'Predict.matrix'
          function to produce a prediction matrix for new covariate
          values. Further details are given in 'smooth.construct'.

    data: A data frame containing the values of the (named) covariates
          at which the smooth term is to be  evaluated.

_D_e_t_a_i_l_s:

     Smooth terms in a GAM formula are turned into smooth specification
     objects of  class 'xx.smooth.spec' during processing of the
     formula. Each of these objects is converted to a smooth object
     using an appropriate 'smooth.construct' function. The
     'Predict.matrix'  functions are used to obtain the matrix that
     will map the parameters associated with a smooth term to the
     predicted values for the term at new covariate values.

     Note that new smooth classes can be added by writing a new
     'smooth.construct' method function and a  corresponding
     'Predict.matrix' method function: see the example code provided
     for  'smooth.construct' for details.

_V_a_l_u_e:

     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'.

_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. (2000)  Modelling and Smoothing Parameter Estimation
     with Multiple  Quadratic Penalties. J.R.Statist.Soc.B
     62(2):413-428

     Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B
     65(1):95-114

     Wood, S.N. (in press) Stable and efficient multiple smoothing
     parameter estimation for generalized additive models. J. Amer.
     Statist. Ass.

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

_S_e_e _A_l_s_o:

     'gam','gamm', 'smooth.construct', 'PredictMat'

_E_x_a_m_p_l_e_s:

     # See smooth.construct examples

