acf                    package:ts                    R Documentation

_A_u_t_o- _a_n_d _C_r_o_s_s- _C_o_v_a_r_i_a_n_c_e _a_n_d -_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.

_U_s_a_g_e:

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

_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 number of lags at which to calculate the acf. Default
          is 10*log10(N) where N is the number of observations.

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

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

na.action: function to be called to handle missing values. `na.pass'
          can be used.

  demean: logical.  Should the covariances be about the sample means?

     ...: further arguments to be passed to `plot.acf'.

_D_e_t_a_i_l_s:

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

     By default, no missing values are allowed.  If the `na.action'
     function passes through missing values (as `na.pass' does), the
     covariances are computed from the complete cases.  This means that
     the estimate computed may well not be a valid autocorrelation
     sequence, and may contain missing values.  Missing values are not
     allowed when computing the PACF of a multivariate time series.

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

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

     The lag is returned and plotted in units of time, and not numbers
     of observations.

_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'.

_A_u_t_h_o_r(_s):

     Original: Paul Gilbert, Martyn Plummer. Extensive modifications
     and univariate case of `pacf' by B.D. Ripley.

_S_e_e _A_l_s_o:

     `plot.acf'

_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.

     data(presidents) # contains missing values
     acf(presidents, na.action = na.pass)
     pacf(presidents, na.action = na.pass)

