

acf(bats)                                    R Documentation

_A_u_t_o_c_o_v_a_r_i_a_n_c_e _f_u_n_c_t_i_o_n _E_s_t_i_m_a_t_i_o_n

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

     The function `acf' computes estimates of the autoco-
     variance function.

     The generic function `plot' has a method for acf
     objects.

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

     acf(x, lag.max=NULL, plot=FALSE, type=c("correlation",
             "covariance", "partial"))
     plot.acf(acf.obj, ci, ...)

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

     For `type' = "correlation" and "covariance", the esti-
     mates are based on the sample covariance.

     The partial correlation coefficient is estimated by
     fitting autoregressive models of successively higher
     orders up to `lag.max'.  The function `ar.yw' is called
     to do this.

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

     An object of class `acf', which is a list with the fol-
     lowing elements:

     lag: A three dimensional array containing the lags at
          which the acf is estimated.

     acf: An array with the same dimensions as `lag' con-
          taining the estimated acf.

    type: The type of correlation (same as the `type' argu-
          ment).

  n.used: The number of observations in the time series.

  series: The name of the series

  snames: The series names for multivariate time series.

_N_o_t_e_:

     The confidence interval plotted in `plot.acf' is based
     on an uncorrelated series and should be treated with
     appropriate caution.

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

     Paul Gilbert, Martyn Plummer

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

     `ar.yw'

