lmSimulate              package:fractal              R Documentation

_S_t_o_c_h_a_s_t_i_c _f_r_a_c_t_a_l _t_i_m_e _s_e_r_i_e_s _s_i_m_u_l_a_t_i_o_n

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

     Create a simulation of a stochastic fractal time series according
     to a specfied model.

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

     lmSimulate(x, sampling.interval=1,
         mean=0, n.sample=128, generate.Sj=FALSE,
         Sj=NULL, rn=NULL)

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

       x: an object of class '"lmModel"'. Use the 'lmModel' function to
          create this input.

      Sj: a numeric vector of Davies-Harte frequency domain weights
          used to create the simulation(s). These weights are
          calculated if not supplied. Default: 'NULL' (not supplied).

generate.Sj: a logical value. If 'TRUE', the Davies-Harte frequency
          domain weights (Sj) are returned instead of a simulated
          series. See the references for details. Default: 'FALSE'.

    mean: the mean value of of the resulting simulation. Default:
          '0.0'.

n.sample: length of a time series. Default: '128'.

      rn: a vector of random normal deviates used to generate
          uncorrelated random variables for the Davies-Harte simulator.
          Default: 'rnorm(2 * length(Sj) - 2)'.

sampling.interval: the sampling interval for the process. The SDF is
          computed for frequencies on the interval [0, Nyquist] where
          Nyquist is '1/(2*sampling.interval)'. The value of
          'sampling.interval' must be a positive number. Default: '1'.

_D_e_t_a_i_l_s:

     Simulates a stochastic fractal time series via the Davies-Harte
     technique, which randomizes spectral weights and inverts the
     result back to the time domain. See the references for more
     details.

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

     an object of class 'signalSeries' containing the simulated series.

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

     D. Percival and A. Walden (2000), _Wavelet Methods for Time Series
     Analysis_, Cambridge University Press, Chapter 7.

     J. Beran (1994), _Statistics for Long-Memory Processes_, Chapman
     and Hall, Chapter 2.

     D. Percival and A. Walden (1993), _Spectral Analysis for Physical
     Applications_, Cambridge University Press, 1993, Chapter 9.

     Davies,R.B.and Harte,D.S.(1987). Tests for the Hurst effect,
     _Biometrika_, *74*, 95-102.

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

     'lmModel', 'lmACF', 'lmSDF', 'lmConfidence', 'FDSimulate'.

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

     old.plt <- par("plt")
     models <- c("ppl","fdp","fgn","dfbm")
     for (i in seq(along=models)){
         splitplot(2,2,i)
         plot(lmSimulate(lmModel(models[i])),
         reference.grid=FALSE)
     }
     par(plt=old.plt)

