smoothCon                package:mgcv                R Documentation

_P_r_e_d_i_c_t_i_o_n/_C_o_n_s_t_r_u_c_t_i_o_n _w_r_a_p_p_e_r _f_u_n_c_t_i_o_n_s _f_o_r _G_A_M _s_m_o_o_t_h _t_e_r_m_s

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     smoothCon(object,data,knots,absorb.cons=FALSE,scale.penalty=TRUE)
     PredictMat(object,data)

_A_r_g_u_m_e_n_t_s:

  object: is a smooth specification object or a smooth object.

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

   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.

_D_e_t_a_i_l_s:

     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 parameterization used by 'gam' can be controlled via
     'gam.control'.

_V_a_l_u_e:

     From 'smoothCon' a 'smooth' object returned by the appropriate
     'smooth.construct' method function. If constraints are to be
     absorbed then the object 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'.

_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:

     <URL: http://www.maths.bath.ac.uk/~sw283/>

_S_e_e _A_l_s_o:

     'gam.control', 'smooth.construct', 'Predict.matrix'

