

qspreg(funfits)                              R Documentation

_Q_u_a_n_t_i_l_e _s_p_l_i_n_e _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n_:

     This is an experimental function to find the smoothing
     parameter for a quantile spline using a more appropri-
     ate criterion than mean squared error prediction.  The
     quantile spline is found by an iterative algorithm
     using weighted least squares cubic splines. At conver-
     gence the estimate will also be a weighted natural
     cubic spline but the weights will depend on the esti-
     mate.  Of course these weights are crafted so that the
     resulting spline is an estimate of the alpha quantile
     instead of the mean.

_U_s_a_g_e_:

     qspreg(x, y, lam=NA, maxit=50, maxit.cv=10, tol=0.0001, cost=1,
      offset=0, sc=sqrt(var(y)) * 1e-07, alpha=0.5, wt=rep(1, length(x)),
      nstep.cv=50, xgrid=sort(unique(x)), deriv=0, hmin=-35, hmax=-15)

_A_r_g_u_m_e_n_t_s_:

       x: Vector of independent variables

       y: Vector dependent variables

     lam: Values of the smoothing parameter. If omitted is
          found by GCV based on the the quantile criterion

   maxit: Maximum number of iterations used to estimate each
          quantile spline.

maxit.cv: Maximum number of iterations to find GCV minimum.

     tol: Tolerance for convergence when computing quantile
          spline.

    cost: Cost value used in the GCV criterion. Cost=1 is
          the usual GCV denominator.

  offset: Constant added to the effective degrees of freedom
          in the GCV function.

      sc: Scale factor for quantile function. Default is a
          scale on the order of machine precision. Scales on
          the order of the residuals will result is a robust
          regression fit using the Huber weight function.

   alpha: Quantile to be estimated. Default is find the
          median.

      wt: Weight vector default is constant values. Passing
          nonconstant weights is a pretty strange thing to
          do.

   xgrid: Grid of x values to evaluate the estimated quan-
          tile function. Default is the unique sorted values
          of x.

derivative: Specifies whether the function itself of deriva-
          tives should be evaluated at xgrid.

    hmin: Minimum value of log( lambda) used for GCV grid
          search.

    hmax: Maximum value of log( lambda) used for GCV grid
          search.

_V_a_l_u_e_:

     Object of class qspreg with many arguments similar to
     sreg object.

_R_e_f_e_r_e_n_c_e_s_:

     Nychka,D. Oconnell, M. (1996)  "

_S_e_e _A_l_s_o_:

     sreg

_E_x_a_m_p_l_e_s_:

