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).
1 2 3 
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

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 
fast 
logical; if 
demean 
logical. If 
detrend 
logical. If 
plot 
plot the periodogram? 
na.action 

... 
graphical arguments passed to 
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.
A list object of class "spec"
(see spectrum
)
with the following additional components:
kernel 
The 
df 
The distribution of the spectral density estimate can be
approximated by a (scaled) chi square distribution with 
bandwidth 
The equivalent bandwidth of the kernel smoother as defined by Bloomfield (1976, page 201). 
taper 
The value of the 
pad 
The value of the 
detrend 
The value of the 
demean 
The value of the 
The result is returned invisibly if plot
is true.
Originally Martyn Plummer; kernel smoothing by Adrian Trapletti, synthesis by B.D. Ripley
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.)
spectrum
, spec.taper
,
plot.spec
, fft
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  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")

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