Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/Finalised_coding.R
Using the IsolateDetect methodology, this function estimates the number and locations
of multiple changepoints in the mean of the noisy, piecewiseconstant input vector x
,
with noise that is not normally distributed. It also gives the estimated signal, as well as
the solution path defined in sol_path_pcm
. See Details for the relevant literature reference.
1 2  ht_ID_pcm(x, s.ht = 3, q_ht = 300, ht_thr_id = 1, ht_th_ic_id = 0.9,
p_thr = 1, p_ic = 3)

x 
A numeric vector containing the data in which you would like to find changepoints. 
s.ht 
A positive integer number with default value equal to 3. It is used to define the way we preaverage the given data sequence (see Details). 
q_ht 
A positive integer number with default value equal to 300. If the
length of 
ht_thr_id 
A positive real number with default value equal to 1. It is
used to define the threshold, if the thresholding approach is to be followed; see

ht_th_ic_id 
A positive real number with default value equal to 0.9. It is
useful only if the model selection based IsolateDetect method is to be followed
and it is used to define the threshold value that will be used at the first step
(changepoint overestimation) of the model selection approach described in

p_thr 
A positive integer with default value equal to 1. It is used only
when the threshold based approach (as described in 
p_ic 
A positive integer with default value equal to 3. It is used only
when the information criterion based approach (described in 
Firstly, in this function we call normalise
, in order to
create a new data sequence, \tilde{x}, by taking averages of observations in
x
. Then, we employ ID_pcm
on \tilde{x}_q to obtain the
changepoints, namely \tilde{r}_1, \tilde{r}_2, ..., \tilde{r}_{\hat{N}} in
increasing order. To obtain the original location of the changepoints with,
on average, the highest accuracy we define
\hat{r}_k = (\tilde{r}_{k}1)*\code{s.ht} + \lfloor \code{s.ht}/2 + 0.5 \rfloor, k=1, 2,..., \hat{N}.
More details can be found in “Detecting multiple generalized changepoints by
isolating single ones”, Anastasiou and Fryzlewicz (2018), preprint.
A list with the following components:
cpt  A vector with the detected changepoints. 
no_cpt  The number of changepoints detected. 
fit  A numeric vector with the estimated piecewiseconstant signal. 
solution_path  A vector containing the solution path. 
Andreas Anastasiou, a.anastasiou@lse.ac.uk
ID_pcm
and normalise
, which are functions that are
used in ht_ID_pcm
. In addition, see ht_ID_cplm
for the case
of continuous and piecewiselinear signals.
1 2 3 4 5 6 7  single.cpt < c(rep(4,3000),rep(0,3000))
single.cpt.student < single.cpt + rt(6000, df = 5)
cpts_detect < ht_ID_pcm(single.cpt.student)
three.cpt < c(rep(4,2000),rep(0,2000),rep(4,2000),rep(0,2000))
three.cpt.student < three.cpt + rt(8000, df = 5)
cpts_detect_three < ht_ID_pcm(three.cpt.student)

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