QRegEstimator-constructor: Create an instance of the 'QRegEstimator' class.
In quantspec: Quantile-Based Spectral Analysis of Time Series

Description Usage Arguments Value Examples

The parameter type.boot can be set to choose a block bootstrapping procedure. If "none" is chosen, a moving blocks bootstrap with l=length(Y) and N=length(Y) would be done. Note that in that case one would also chose B=0 which means that getPositions would never be called. If B>0 then each bootstrap replication would be the undisturbed time series.

qRegEstimator(
  Y,
  frequencies = 2 * pi/lenTS(Y) * 0:(lenTS(Y) - 1),
  levels = 0.5,
  isRankBased = TRUE,
  B = 0,
  l = 0,
  type.boot = c("none", "mbb"),
  method = c("br", "fn", "pfn", "fnc", "lasso", "scad"),
  parallel = FALSE
)

`Y`	A `vector` of real numbers containing the time series from which to determine the quantile periodogram or a `ts` object or a `zoo` object.
`frequencies`	A vector containing frequencies at which to determine the `QRegEstimator`.
`levels`	A vector of length `K` containing the levels `x` at which the `QRegEstimator` is to be determined.
`isRankBased`	If true the time series is first transformed to pseudo data [cf. `FreqRep`].
`B`	number of bootstrap replications
`l`	(expected) length of blocks
`type.boot`	A flag to choose a method for the block bootstrap; currently two options are implemented: `"none"` and `"mbb"` which means to do a moving blocks bootstrap with `B` and `l` as specified.
`method`	method used for computing the quantile regression estimates. The choice is passed to `qr`; see the documentation of `quantreg` for details.
`parallel`	a flag to allow performing parallel computations.

Returns an instance of QRegEstimator.

library(snowfall)

Y <- rnorm(100) # Try 2000 and parallel computation will in fact be faster.

# Compute without using snowfall capabilities
system.time(
  qRegEst1 <- qRegEstimator(Y, levels=seq(0.25,0.75,0.25), method="fn", parallel=FALSE)
)

# Set up snowfall
sfInit(parallel=TRUE, cpus=2, type="SOCK")
sfLibrary(quantreg)
sfExportAll()

# Compare how much faster the computation is when done in parallel
system.time(
  qRegEst2 <- qRegEstimator(Y, levels=seq(0.25,0.75,0.25), method="fn", parallel=TRUE)
)

sfStop()

# Compare results
V1 <- getValues(qRegEst1)
V2 <- getValues(qRegEst2)
sum(abs(V1-V2)) # Returns: [1] 0