| normalise {IDetect} | R Documentation |
Transform the noise to be closer to the Gaussian distribution
Description
This function pre-processes the given data in order to obtain a noise structure that is closer to satisfying the Gaussianity assumption. See details for more information and for the relevant literature reference.
Usage
normalise(x, sc = 3)
Arguments
x |
A numeric vector containing the data. |
sc |
A positive integer number with default value equal to 3. It is used to define the way we pre-average the given data sequence. |
Details
For a given natural number sc and data x of length T, let us
denote by Q = \lceil T/sc \rceil. Then, normalise calculates
\tilde{x}_q = 1/sc\sum_{t=(q-1) * sc + 1}^{q * sc}x_t,
for q=1, 2, ..., Q-1, while
\tilde{x}_Q = (T - (Q-1) * sc)^{-1}\sum_{t = (Q-1) * sc + 1}^{T}x_t.
More details can be found in the preprint “Detecting multiple generalized change-points by isolating single ones”, Anastasiou and Fryzlewicz (2018).
Value
The “normalised” vector \tilde{x} of length Q, as explained in Details.
Author(s)
Andreas Anastasiou, a.anastasiou@lse.ac.uk
See Also
ht_ID_pcm and ht_ID_cplm, which are
functions that employ normalise.
Examples
t5 <- rt(n = 10000, df = 5)
n5 <- normalise(t5, sc = 3)