spec.pgram               package:stats               R Documentation

_E_s_t_i_m_a_t_e _S_p_e_c_t_r_a_l _D_e_n_s_i_t_y _o_f _a _T_i_m_e _S_e_r_i_e_s _b_y _a _S_m_o_o_t_h_e_d
_P_e_r_i_o_d_o_g_r_a_m

_D_e_s_c_r_i_p_t_i_o_n:

     'spec.pgram' calculates the periodogram using a fast Fourier
     transform, and optionally smooths the result with a series of
     modified Daniell smoothers (moving averages giving half weight to
     the end values).

_U_s_a_g_e:

     spec.pgram(x, spans = NULL, kernel, taper = 0.1,
                pad = 0, fast = TRUE, demean = FALSE, detrend = TRUE,
                plot = TRUE, na.action = na.fail, ...)

_A_r_g_u_m_e_n_t_s:

       x: univariate or multivariate time series.

   spans: vector of odd integers giving the widths of modified Daniell
          smoothers to be used to smooth the periodogram.

  kernel: alternatively, a kernel smoother of class '"tskernel"'.

   taper: specifies the proportion of data to taper.  A split cosine
          bell taper is applied to this proportion of the data at the
          beginning and end of the series.

     pad: proportion of data to pad. Zeros are added to the end of the
          series to increase its length by the proportion 'pad'.

    fast: logical; if 'TRUE', pad the series to a highly composite
          length.

  demean: logical. If 'TRUE', subtract the mean of the series.

 detrend: logical. If 'TRUE', remove a linear trend from the series.
          This will also remove the mean.

    plot: plot the periodogram?

na.action: 'NA' action function.

     ...: graphical arguments passed to 'plot.spec'.

_D_e_t_a_i_l_s:

     The raw periodogram is not a consistent estimator of the spectral
     density, but adjacent values are asymptotically independent. Hence
     a consistent estimator can be derived by smoothing the raw
     periodogram, assuming that the spectral density is smooth.

     The series will be automatically padded with zeros until the
     series length is a highly composite number in order to help the
     Fast Fourier Transform. This is controlled by the 'fast' and not
     the 'pad' argument.

     The periodogram at zero is in theory zero as the mean of the
     series is removed (but this may be affected by tapering): it is
     replaced by an interpolation of adjacent values during smoothing,
     and no value is returned for that frequency.

_V_a_l_u_e:

     A list object of class '"spec"' (see 'spectrum') with the
     following additional components: 

  kernel: The 'kernel' argument, or the kernel constructed from
          'spans'.

      df: The distribution of the spectral density estimate can be
          approximated by a (scaled) chi square distribution with 'df'
          degrees of freedom.

bandwidth: The equivalent bandwidth of the kernel smoother as defined
          by Bloomfield (1976, page 201).

   taper: The value of the 'taper' argument.

     pad: The value of the 'pad' argument.

 detrend: The value of the 'detrend' argument.

  demean: The value of the 'demean' argument.


     The result is returned invisibly if 'plot' is true.

_A_u_t_h_o_r(_s):

     Originally Martyn Plummer; kernel smoothing by Adrian Trapletti,
     synthesis by B.D. Ripley

_R_e_f_e_r_e_n_c_e_s:

     Bloomfield, P. (1976) _Fourier Analysis of Time Series: An
     Introduction._ Wiley.

     Brockwell, P.J. and Davis, R.A. (1991) _Time Series: Theory and
     Methods._ Second edition. Springer.

     Venables, W.N. and Ripley, B.D. (2002) _Modern Applied Statistics
     with S._ Fourth edition. Springer. (Especially pp. 392-7.)

_S_e_e _A_l_s_o:

     'spectrum', 'spec.taper', 'plot.spec', 'fft'

_E_x_a_m_p_l_e_s:

     require(graphics)

     ## Examples from Venables & Ripley
     spectrum(ldeaths)
     spectrum(ldeaths, spans = c(3,5))
     spectrum(ldeaths, spans = c(5,7))
     spectrum(mdeaths, spans = c(3,3))
     spectrum(fdeaths, spans = c(3,3))

     ## bivariate example
     mfdeaths.spc <- spec.pgram(ts.union(mdeaths, fdeaths), spans = c(3,3))
     # plots marginal spectra: now plot coherency and phase
     plot(mfdeaths.spc, plot.type = "coherency")
     plot(mfdeaths.spc, plot.type = "phase")

     ## now impose a lack of alignment
     mfdeaths.spc <- spec.pgram(ts.intersect(mdeaths, lag(fdeaths, 4)),
        spans = c(3,3), plot = FALSE)
     plot(mfdeaths.spc, plot.type = "coherency")
     plot(mfdeaths.spc, plot.type = "phase")

     stocks.spc <- spectrum(EuStockMarkets, kernel("daniell", c(30,50)),
                            plot = FALSE)
     plot(stocks.spc, plot.type = "marginal") # the default type
     plot(stocks.spc, plot.type = "coherency")
     plot(stocks.spc, plot.type = "phase")

     sales.spc <- spectrum(ts.union(BJsales, BJsales.lead),
                           kernel("modified.daniell", c(5,7)))
     plot(sales.spc, plot.type = "coherency")
     plot(sales.spc, plot.type = "phase")