qspreg                package: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 appropriate criterion than
     mean squared  error prediction.  The quantile spline is found by
     an iterative algorithm using weighted  least squares cubic
     splines. At convergence the estimate will also be a  weighted
     natural  cubic spline but the weights will depend on the estimate.
     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 quantile function.
          Default  is the unique sorted values of x. 

derivative: Specifies whether the function itself of derivatives 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:

