acfM                  package:dse1                  R Documentation

_C_a_l_c_u_l_a_t_e _A_u_t_o-_C_o_v_a_r_i_a_n_c_e

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

     Calculate a matrix with partitions [M0|...|Mi|...|Ml] giving  the
     auto-covariance.

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

         acfM(obj, ...)
         ## S3 method for class 'TSdata':
         acfM(obj, lag=round(6*log(periods(obj))), 
             type ="covariance", sub.mean=TRUE, ...)
         ## S3 method for class 'TSmodel':
         acfM(obj, lag=NULL, type ="covariance", Psi=NULL, ...)
         ## S3 method for class 'TSestModel':
         acfM(obj, ...)

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

     obj: An object of class TSdata or TSmodel.

     lag: Number of lags for which to calculate the autocorrelations.

    type: With the defaults the blocks are auto-covariances.  If type
          == 'correlation' the result is scaled to give 
          autocorrelations.

sub.mean: Only valid if object is of class TSdata. If FALSE then means 
          are not subtracted.

     Psi: A matrix of innovation covariance. Only valid if object  is
          of class TSmodel.

     ...: arguments passed to other methods.

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

     A matrix with partitions [M0|...|Mi|...|Ml] giving the covariance 
     or correlation, including the that between the output and input 
     series (as in the first block row of a Hankel matrix).

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

         if(is.R()) data("eg1.DSE.data.diff", package="dse1")
         z <- acfM(eg1.DSE.data.diff)
         model <- TSmodel(toSS(estVARXls(eg1.DSE.data.diff)))
         #  z <- acfM(model) not working

