spec.pgram                package:ts                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 _f_r_o_m _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 = FALSE, na.action, ...)

_A_r_g_u_m_e_n_t_s:

       x: A 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: 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.

  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.

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

    plot: Plot the periodogram?

    fast: If `TRUE', pad the series to a highly composite length.

     ...: 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"' with the following elements. 

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

      df: The distribution of the spectral density estimate can be
          approximated by a 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. (1997) Modern Applied Statistics
     with S-PLUS. Second edition. Springer. (Especially pp. 437-442.)

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     ## Examples from Venables & Ripley
     data(UKLungDeaths)
     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")

     data(EuStockMarkets)
     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")

     data(BJsales)
     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")

