avas                 package:acepack                 R Documentation

_A_d_d_i_t_i_v_i_t_y _a_n_d _v_a_r_i_a_n_c_e _s_t_a_b_i_l_i_z_a_t_i_o_n _f_o_r _r_e_g_r_e_s_s_i_o_n

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

     Estimate transformations of 'x' and 'y' such that the regression
     of 'y' on 'x' is approximately linear with constant variance

_U_s_a_g_e:

     avas(x, y, wt, cat, mon, lin, circ, delrsq, yspan)

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

       x: a matrix containing the independent variables.

       y: a vector containing the response variable.

      wt: an optional vector of weights.

     cat: an optional integer vector specifying which variables assume
          categorical values.  Positive values in 'cat' refer to
          columns of the 'x' matrix and zero to the response variable.

     mon: an optional integer vector specifying which variables are to
          be transformed by monotone transformations.  Positive values
          in 'mon' refer to columns of the 'x' matrix and zero to the
          response variable.

     lin: an optional integer vector specifying which variables are to
          be transformed by linear transformations.  Positive values in
          'lin' refer to columns of the 'x' matrix and zero to the
          response variable.

    circ: an integer vector specifying which variables assume circular
          (periodic) values.  Positive values in 'circ' refer to
          columns of the 'x' matrix and zero to the response variable.

  delrsq: termination threshold.  Iteration stops when R-squared
          changes by less than 'delrsq' in 3 consecutive iterations
          (default 0.01).

   yspan: Optional window size parameter for smoothing the variance. 
          Range is [0,1].  Default is 0 (cross validated choice). .5 is
          a reasonable alternative to try.

_V_a_l_u_e:

     A structure with the following components: 

       x: the input x matrix.

       y: the input y vector.

      tx: the transformed x values.

      ty: the transformed y values.

     rsq: the multiple R-squared value for the transformed values.

       l: not used in this version of avas

       m: not used in this version of avas

   yspan: span used for smoothing the variance

   iters: iteration number and rsq for that iteration

  niters: number of iterations used

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

     Rob Tibshirani (1987), ``Estimating optimal transformations for
     regression''.  _Journal of the American Statistical Association_
     *83*, 394ff.

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

     TWOPI <- 8*atan(1)
     x <- runif(200,0,TWOPI)
     y <- exp(sin(x)+rnorm(200)/2)
     a <- avas(x,y)
     par(mfrow=c(3,1))
     plot(a$y,a$ty)  # view the response transformation
     plot(a$x,a$tx)  # view the carrier transformation
     plot(a$tx,a$ty) # examine the linearity of the fitted model

