

acf {ts}                                     R Documentation

_A_u_t_o_c_o_v_a_r_i_a_n_c_e _a_n_d _A_u_t_o_c_o_r_r_e_l_a_t_i_o_n _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 (and by default plots)
     estimates of the autocovariance or autocorrelation
     function.  Function `pacf' is the function used for the
     partial autocorrelations.

     Function `ccf' computes the cross-correlation or cross-
     covariance of two univariate series.

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

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

     acf(x, lag.max = NULL,
         type = c("correlation", "covariance", "partial"),
         plot = TRUE, na.action, demean = TRUE, ...)
     pacf(x, lag.max = NULL, plot = TRUE, na.action, ...)
     ccf(x, y, lag.max = NULL, type = c("correlation", "covariance"),
         plot = TRUE,na.action, ...)

     plot.acf(acf.obj, ci=0.95, ci.col="blue", ci.type=c("white", "ma"), ...)

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

    x, y: a univariate or multivariate (not `ccf') time
          series object or a numeric vector or matrix.

 lag.max: maximum lag at which to calculate the acf.
          Default is 10*log10(N) where N is the number of
          observations.

    plot: logical. If `TRUE' the acf is plotted.

    type: character string giving the type of acf to be com-
          puted.  Allowed values are `"correlation"' (the
          default), `"covariance"' or `"partial"'.

na.action: function to be called to handle missing values.

  demean: logical. Should the covariances be about the sam-
          ple means?

 acf.obj: an object of class `acf'.

      ci: coverage probability for confidence interval.
          Plotting of the confidence interval is suppressed
          if `ci' is zero or negative.

  ci.col: colour to plot the confidence interval lines.

 ci.type: should the confidence limits assume a white noise
          input or for lag `k' an MA(`k-1') input?

     ...: graphical parameters.

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

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

     The partial correlation coefficient is estimated by
     fitting autoregressive models of successively higher
     orders up to `lag.max'.

_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 `x'.

  snames: The series names for a multivariate time series.

          The result is returned invisibly if `plot' is
          `TRUE'.

_N_o_t_e_:

     The confidence interval plotted in `plot.acf' is based
     on an uncorrelated series and should be treated with
     appropriate caution. Using `ci.type = "ma"' may be less
     potentially misleading.

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

     Original: Paul Gilbert, Martyn Plummer.  Extensive mod-
     ifications and univariate case of `pacf' by B.D. Rip-
     ley.

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

     ## Examples from Venables & Ripley
     data(lh)
     acf(lh)
     acf(lh, type="covariance")
     pacf(lh)

     data(UKLungDeaths)
     acf(ldeaths)
     acf(ldeaths, ci.type="ma")
     acf(ts.union(mdeaths, fdeaths))
     ccf(mdeaths, fdeaths) # just the cross-correlations.

