Lspec: logspline estimation of a spectral distribution
Autocorrelations, autocovariances
(clspec), spectral densities and line spectrum (dlspec),
spectral distributions (plspec) or a
random time series(rlspec) from a model fitted with lspec.
clspec(lag, fit, cov = TRUE, mm) dlspec(freq, fit) plspec(freq, fit, mm) rlspec(n, fit, mean = 0, cosmodel = FALSE, mm)
lag |
vector of integer-valued lags for which the autocorrelations or autocorrelations are to be computed. |
fit |
|
cov |
compute autocovariances ( |
mm |
number of points used in integration and the fft. Default is the
smallest power of two larger than |
freq |
vector of frequencies. For |
n |
length of the random time series to be generated. |
mean |
mean level of the time series to be generated. |
cosmodel |
indicate that the data should be generated from a model with constant harmonic terms rather than a true Gaussian time series. |
Autocovariances or autocorrelations (clspec);
values of the spectral distribution at the requested frequencies. (plspec);
random time series of length n (rlspec);
or a list with three components (dlspec):
d |
the spectral density evaluated at the vector of frequencies, |
modfreq |
modified frequencies of the form (2pi*j/T) that are close to the frequencies that were requested, |
m |
mass of the line spectrum at the modified frequencies. |
Charles Kooperberg clk@fredhutch.org.
Charles Kooperberg, Charles J. Stone, and Young K. Truong (1995). Logspline Estimation of a Possibly Mixed Spectral Distribution. Journal of Time Series Analysis, 16, 359-388.
Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371–1470.
data(co2) co2.detrend <- lm(co2~c(1:length(co2)))$residuals fit <- lspec(co2.detrend) clspec(0:12,fit) plspec((0:314)/100, fit) dlspec((0:314)/100, fit) rlspec(length(co2),fit)
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