denoise.poisson {haarfisz} | R Documentation |
denoise.poisson
Description
Main routine of the package. Estimates the deterministic discretised intensity of a one-dimensional Poisson process using the Haar-Fisz transformation and partial cycle spinning.
Usage
denoise.poisson(y, meth.1 = hf.bt, cs.1 = 50, meth.2 = hf.cv, cs.2 = 50, hybrid = TRUE)
Arguments
y |
The vector of Poisson counts, its length must be a power of 2. |
meth.1 |
Unquoted name of an S-Plus routine for denoising Gaussian contaminated vectors. Must take and return a vector of length |
cs.1 |
The number of cycle spins to be performed with |
meth.2 |
Of the same type as |
cs.2 |
The number of cycle spins to be performed with |
hybrid |
If set to TRUE, then the estimates are computed using both |
Details
For a given input sequence, basic operation of the code
performs a cyclic shift on
the data. Then applies the Haar-Fisz transform, then one
of the denoising methods (specified by meth.1
),
then the inverse Haar-Fisz transform and then a shift back.
This is repeated for cs.1
cyclic shifts and the results of
all shifts returned.
Value
Returns vector of the same length as the input y
but is the
denoised estimate.
Author(s)
Piotr Fryzlewicz
References
Fryzlewicz, P. and Nason, G.P. (2004) A Haar-Fisz algorithm for Poisson intensity estimation. Journal of Computational and Graphical Statistics, 13, 621-638. doi:10.1198/106186004X2697
See Also
hft
, hft.inv
,
hf.u
, hf.cv
, hf.bt
, hf.tiu
Examples
#
# Apply denoise.poisson to xquake data
#
data(xquake)
xquake.denoised <- denoise.poisson(xquake)
#
# Now plot the original data and it's denoised version in red
#
plot(xquake, type="l")
lines(xquake.denoised, col=2)