chaoticInvariant           package:fractal           R Documentation

_C_l_a_s_s _f_o_r _c_h_a_o_t_i_c _i_n_v_a_r_i_a_n_t _o_b_j_e_c_t_s

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

     Class constructor for 'chaoticInvariant'.

_S_3 _M_E_T_H_O_D_S:


     _e_d_a._p_l_o_t plots an extended data analysis plot, which graphically
          summarizes the process of obtaining a correlation dimension
          estimate. A time history, phase plane embeddding, correlation
          summation curves, and the slopes of correlation summation
          curves as a function of scale are plotted.

     _p_l_o_t plots the correlation summation curves on a log-log scale.
          The following options may be used to adjust the plot
          components:

          _t_y_p_e Character string denoting the type of data to be
               plotted. The '"stat"' option plots the correlation
               summation curves while the '"dstat"' option plots a
               3-point estimate of the derivatives of the correlation
               summation curves. The '"slope"' option plots the
               estimated slope of the correlation summation curves as a
               function of embedding dimension. Default: '"stat"'.

          _f_i_t Logical flag. If 'TRUE', a regression line is overlaid
               for each curve. Default: 'TRUE'.

          _g_r_i_d Logical flag. If 'TRUE', a grid is overlaid on the plot.
               Default: 'TRUE'.

          _l_e_g_e_n_d Logical flag. If 'TRUE', a legend of the estimated
               slopes as a function of embedding dimension is
               displayed. Default: 'TRUE'.

          ... Additional plot arguments (set internally by the 'par'
               function). .in -5


          _p_r_i_n_t prints a qualitiative summary of the results.

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

     'infoDim', 'corrDim', 'lyapunov'.

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

     ## create a faux object of class chaoticInvariant 
     faux.data <- list(matrix(rnorm(1024), ncol=2), matrix(1:512))
     chaoticInvariant(faux.data,
         dimension   = 1:2,
         n.embed     = 10,
         n.reference = 50,
         n.neighbor  = 35,
         tlag        = 10,
         olag        = 15,
         resolution  = 2,
         series.name = "my series",
         series      = 1:10,
         ylab        = "log2(C2)",
         xlab        = "log2(scale)",
         metric      = Inf,
         invariant   = "correlation dimension")

