sqdft {qfa} | R Documentation |
Spline Quantile Discrete Fourier Transform (SQDFT) of Time Series
Description
This function computes spline quantile discrete Fourier transform (SQDFT) for univariate or multivariate time series through trigonometric spline quantile regression.
Usage
sqdft(
y,
tau,
spar = NULL,
d = 1,
weighted = FALSE,
method = c("AIC", "BIC"),
ztol = 1e-05,
n.cores = 1,
cl = NULL
)
Arguments
y |
vector or matrix of time series (if matrix, |
tau |
sequence of quantile levels in (0,1) |
spar |
smoothing parameter: if |
d |
subsampling rate of quantile levels (default = 1) |
weighted |
if |
method |
crietrion for smoothing parameter selection when |
ztol |
zero tolerance parameter used to determine the effective dimensionality of the fit |
n.cores |
number of cores for parallel computing (default = 1) |
cl |
pre-existing cluster for repeated parallel computing (default = |
Value
A list with the following elements:
coefficients |
matrix of regression coefficients |
qdft |
matrix or array of the spline quantile discrete Fourier transform of |
crit |
criteria for smoothing parameter selection: (AIC,BIC) |
Examples
y <- stats::arima.sim(list(order=c(1,0,0), ar=0.5), n=64)
tau <- seq(0.1,0.9,0.05)
y.sqdft <- sqdft(y,tau,spar=NULL,d=4,metho="AIC")$qdft