

surrogate(tseries)                           R Documentation

_G_e_n_e_r_a_t_e _S_u_r_r_o_g_a_t_e _D_a_t_a

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

     Computes `ns' surrogate samples from the original data
     `x'. If `fft' is `FALSE', then `x' is mixed in temporal
     order, so that all temporal dependencies are elimi-
     nated, but the histogram of the original data is pre-
     served. If `fft', then surrogate data with the same
     spectrum as `x' is computed by randomizing the phases
     of the Fourier coefficients of `x'. If in addition
     `amplitude' is `TRUE', then also the amplitude distri-
     bution of the original series is preserved.

     Missing values are not allowed.

_U_s_a_g_e_:

     surrogate (x, ns = 1, fft = FALSE, amplitude = FALSE)

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

       x: a numeric vector or time series.

      ns: the number of surrogate series to compute.

     fft: a logical indicating whether phase randomized sur-
          rogate data is generated.

amplitude: a logical indicating whether amplitude-adjusted
          surrogate data is computed.

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

     To compute the phase randomized surrogate and the
     amplitude adjusted data algorithm 1 and 2 from Theiler
     et al. (1992), pp. 183, 184 are used.

_V_a_l_u_e_:

     A matrix with `ns' columns and `length{x}' rows con-
     taining the surrogate data. Each column contains one
     surrogate sample.

_A_u_t_h_o_r_(_s_)_:

     A. Trapletti

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

     J. Theiler, B. Galdrikian, A. Longtin, S. Eubank, and
     J. D. Farmer (1992): Using Surrogate Data to Detect
     Nonlinearity in Time Series, in Nonlinear Modelling and
     Forecasting, Eds. M. Casdagli and S. Eubank, Santa Fe
     Institute, Addison Wesley, pp. 163-188.

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

     `sample'

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

     x <- 1:10
     surrogate (x)

