ace                 package:acepack                 R Documentation

_A_l_t_e_r_n_a_t_i_n_g _C_o_n_d_i_t_i_o_n_a_l _E_x_p_e_c_t_a_t_i_o_n_s

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

     Uses the alternating conditional expectations algorithm to find
     the transformations of y and x that maximise the proportion of
     variation in y explained by x.

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

     ace(x, y, wt, cat, mon, lin, circ, delrsq)

_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).

_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 ace

       m: not used in this version of ace

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

     Breiman and Friedman, Journal of the American Statistical
     Association (September, 1985).

     The R code is adapted from S code for avas() by Tibshirani, in the
     Statlib S archive; the FORTRAN is a double-precision version of
     FORTRAN code by Friedman and Spector in the Statlib general
     archive.

_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 <- ace(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

