pepacf                 package:pear                 R Documentation

_p_e_r_i_o_d_i_c _p_a_r_t_i_a_l _a_u_t_o_c_o_r_r_e_l_a_t_i_o_n _f_u_n_c_t_i_o_n

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

     The periodic partial autocorrelation function of a periodic time
     series is calculated and plotted if the argument plot=TRUE. When
     the period, p=1, this reduces to the usual partial 
     autocorrelation function as defined in Box and Jenkins (1976) and
     is equivalent then to the Splus function acf(type="partial"). As
     discussed in Box and Jenkins (1976), McLeod (1994) and  Hipel and
     McLeod (1994) the partial autocorrelation is a  valuable tool in
     selecting the model order.

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

     pepacf(z, lag.max, plot=TRUE, acf.out)

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

       z: a univariate time series object. Note that the period of z is
          given by attr(z, "tsp")[3]. Additional information about the
          time series can be provided in a title string by setting
          attr(z, "title") to the desired string. This title will then
          be displayed on the plot. Abbreviations for the periods may
          be provided in attr(z, "abb"). For example, to use the
          standard monthly abbreviations: attr(z, "abb")<-month.abb.
          These abbreviations  will be used to aid one in interpreting
          the output. 

 lag.max: maximum lag, if missing default is 0.25*length(z)/p, where p
          = attr(z, "tsp")[3] 

    plot: if plot=TRUE, a plot of the periodic autocorrelations is
          produced. 

 acf.out: output from peacf function. If this is provided, execution
          will proceed faster, otherwise it is calculated from scratch. 

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

     For the detailed derivation of the algorithm see Sakai (1982).
     Note that our partial autocorrelation is the negative of that
     given in Sakai's paper.

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

     a list containing the following components: acf.out             
     output list from peacf pacf                 matrix of partial
     autocorrelations residual.sd          matrix of residual standard
     deviations of the fitted models of order m, m=1,2,...,lag.max phi 
                     matrix of autoregressive coefficients in the final
      model of order lag.max for each period aic                 
     matrix of aic values for each period and lag bic                 
     matrix of bic values for each period and lag maice               
     vector of length p of the minimum aic models mbice               
     vector of length p of the minimum bic models

_S_i_d_e _E_f_f_e_c_t_s:

     a plot is produced if plot=TRUE

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

     Box, G.E.P. and Jenkins, G.M. (1976), "Time Series Analysis:
     Forecasting and Control", Holden-Day: San Franciso. Hipel, K.W.
     and McLeod, A.I. (1994) "Time Series Modelling of  Water Resources
     and Environmental Systems"  Elsevier, Amsterdam ISBN
     0-444-89270-2.  (1013 pages). McLeod, A.I. (1994), "Diagnostic
     Checking of Periodic Autoregression" Journal of Time Series
     Analysis, Vol. 15, No. 2, pp.221-233. Sakai, H. (1982), "Circular
     lattice filtering using Pagano's Method", IEEE Transactions,
     Acoust. Speech, Signal Processing, Vol. 30, pp.279-287.

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

     peacf, peacf.plot, peplot, acf, acf.plot

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

     data(fraser)
     pepacf(log(fraser))

