

_D_e_s_i_g_n _M_a_t_r_i_x _f_o_r _B-_s_p_l_i_n_e_s

     splineDesign(knots, x, ord, derivs)
     spline.des(knots, x, ord, derivs)

_A_r_g_u_m_e_n_t_s:

   knots: a numeric vector of knot positions with non-
          decreasing values.

       x: a numeric vector of values at which to evaluate
          the B-spline functions or derivatives. The values
          in `x' must be between `knots[ord]' and `knots[
          length(knots) + 1 - ord ]'.

     ord: a positive integer giving the order of the spline
          function.  This is the number of coefficients in
          each piecewise polynomial segment, thus a cubic
          spline has order 4.  Defaults to 4.

  derivs: an integer vector of the same length as `x' and
          with values between `0' and `ord - 1'.  The
          derivative of the given order is evaluated at the
          `x' positions.  Defaults to a vector of zeroes of
          the same length as `x'.

_D_e_s_c_r_i_p_t_i_o_n:

     Evaluate the design matrix for the B-splines defined by
     `knots' at the values in `x'.

_V_a_l_u_e:

     A matrix with `length( x )' rows and `length( knots ) -
     ord' columns.  The i'th row of the matrix contains the
     coefficients of the B-splines (or the indicated deriva-
     tive of the B-splines) defined by the `knot' vector and
     evaluated at the i'th value of `x'.  Each B-spline is
     defined by a set of `ord' successive knots so the total
     number of B-splines is `length(knots)-ord'.

_N_o_t_e:

     The older `spline.des' function takes the same argument
     but returns a list with several components including
     `knots', `ord', `derivs', and `design'.  The `design'
     component is the same as the value of the
     `splineDesign' function.

_A_u_t_h_o_r(_s):

     Douglas Bates and Bill Venables

_E_x_a_m_p_l_e_s:

     splineDesign(knots = 1:10, x = 4:7)

