acf                    package: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 computed.
          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 sample 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 following
     elements:

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

     acf: An array with the same dimensions as `lag' containing the
          estimated acf.

    type: The type of correlation (same as the `type' argument).

  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 modifications
     and univariate case of `pacf' by B.D. Ripley.

_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.

