makeTSnoise              package:tframe              R Documentation

_G_e_n_e_r_a_t_e _a _r_a_n_d_o_m _t_i_m_e _s_e_r_i_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Generate a random time series (matrix). This is a utility
     typically used in a time series model simulate method and not
     called directly by the user.

_U_s_a_g_e:

         makeTSnoise(sampleT,p,lags,noise=NULL, rng=NULL,
                  SIGMA=NULL, sd=1, noise.model=NULL, noise.baseline=0,
                  tf=NULL, start=NULL,frequency=NULL)

_A_r_g_u_m_e_n_t_s:

 sampleT: an integer indicating the number of periods.

       p: an integer indicating the number of series.

    lags: an integer indicating the number of periods prior to the
          sample (initial data w0) for which random numbers should be
          generated. This is useful in ARMA models.

   noise: Noise can be supplied. Otherwise it will be generated. If
          supplied it should be a list as described below under
          returned value.

   SIGMA: The covariance of the noise process. If this is specified
          then sd is ignored. A vector or scalar is treated as a
          diagonal matrix. For an object of class TSestModel, if
          neither SIGMA nor sd are specified, then SIGMA is set to the
          estimated covariance ('model$estimates$cov').

      sd: The standard deviation of the noise. This can be a vector.

noise.model: A TSmodel to be used for generating noise (not yet
          supported by SS methods).

noise.baseline: a constant or matrix to be added to noise. Alternately
          this can be a vector of length p, each value of which is
          treated as a constant to add to the coresponding noise
          series.

     rng: The random number generator information needed to regenerate
          a simulation.

      tf: a time frame to use for the generated matrix. (alternately
          use start and frequency)

   start: a time start date to use for the generated matrix.

frequency: a time frequency to use for the generated matrix.

_V_a_l_u_e:

     A time series matrix.

