

nlar(funfits)                                R Documentation

_N_o_n_l_i_n_e_a_r _a_u_t_o_r_e_g_r_e_s_s_i_v_e _m_o_d_e_l

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

     his function fits a model of the form: Y_t =  f( Y_(t-
     l1),...{},Y_(t-ld),S_t) + e_t Where e_t is assumed to
     mean zero, uncorrelated errors. Such a form is useful
     for testing whether a system is chaotic.

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

     nlar(Y, lags, cov=NA, method="nnreg", ...)

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

       Y: The time series

    lags: A vector that specifies which lags of Y to use in
          the autoregressive function

     cov: A vector or matrix of covariates as long as the Y
          series these are additional variables that will be
          used in the regression function

  method: Name of S function to fit the nonparametric model
          e.g. nnreg tps addreg

     ...: Optional argument that as passed through to the
          regression method

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

     An object of class nlar

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

     FUNFITS manual

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

     lle, predict.nlar

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

     # Fit the rossler series. A toy dynamical system that is chaotic
     # Use a neural network with 4 hidden units based on lags 1, 2 and 3 of
     the series.
     nlar( rossler,lags=c(1,2,3), method="nnreg",k1=4)-> out
     summary(out)
     plot( out)
     lle( out) # calculate local and global Lyapunov exponents

