qspec.sar {qfa} | R Documentation |
Spline Autoregression (SAR) Estimator of Quantile Spectrum
Description
This function computes spline autoregression (SAR) estimate of quantile spectrum/cross-spectrum.
Usage
qspec.sar(
y,
y.qser = NULL,
tau,
d = 1,
p = NULL,
order.max = NULL,
spar = NULL,
method = c("AIC", "BIC", "GCV"),
weighted = FALSE,
freq = NULL,
n.cores = 1,
cl = NULL
)
Arguments
y |
vector or matrix of time series (if matrix, |
y.qser |
matrix or array of pre-calculated QSER (default = |
tau |
sequence of quantile levels in (0,1) |
d |
subsampling rate of quantile levels (default = 1) |
p |
order of SAR model (default = |
order.max |
maximum order for AIC if |
spar |
penalty parameter alla |
method |
criterion for penalty parameter selection: |
weighted |
if |
freq |
sequence of frequencies in [0,1) (default = |
n.cores |
number of cores for parallel computing of QDFT if |
cl |
pre-existing cluster for repeated parallel computing of QDFT (default = |
Value
a list with the following elements:
spec |
matrix or array of SAR quantile spectrum |
freq |
sequence of frequencies |
fit |
object of SAR model |
qser |
matrix or array of quantile series if |
Examples
y1 <- stats::arima.sim(list(order=c(1,0,0), ar=0.5), n=64)
y2 <- stats::arima.sim(list(order=c(1,0,0), ar=-0.5), n=64)
tau <- seq(0.1,0.9,0.05)
n <- length(y1)
ff <- c(0:(n-1))/n
sel.f <- which(ff > 0 & ff < 0.5)
# compute from time series
y.sar <- qspec.sar(cbind(y1,y2),tau=tau,p=1)
qfa.plot(ff[sel.f],tau,Re(y.sar$spec[1,1,sel.f,]))
# compute from quantile series
y.qser <- qser(cbind(y1,y2),tau)
y.sar <- qspec.sar(y.qser=y.qser,tau=tau,p=1)
qfa.plot(ff[sel.f],tau,Re(y.sar$spec[1,1,sel.f,]))