sqdft.fit {qfa} | R Documentation |
Spline Quantile Discrete Fourier Transform (SQDFT) of Time Series Given Smoothing Parameter
Description
This function computes spline quantile discrete Fourier transform (SQDFT) for univariate or multivariate time series through trigonometric spline quantile regression with user-supplied spar.
Usage
sqdft.fit(
y,
tau,
spar = 1,
d = 1,
weighted = FALSE,
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 |
d |
subsampling rate of quantile levels (default = 1) |
weighted |
if |
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 Fouror BICier 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.fit(y,tau,spar=1,d=4)$qdft