sol.tguh {breakfast}  R Documentation 
Solution path generation via the TailGreedy Unbalanced Haar method
Description
This function arranges all possible changepoints in the mean of the input vector in the order of importance, via the TailGreedy Unbalanced Haar method.
Usage
sol.tguh(x, type = "const", p = 0.01)
Arguments
x 
A numeric vector containing the data to be processed 
type 
The model type considered. 
p 
Specifies the number of region pairs merged
in each pass through the data, as the proportion of all remaining region pairs. The default is

Details
The TailGreedy Unbalanced Haar decomposition algorithm is described in "Tailgreedy bottomup data decompositions and fast multiple changepoint detection", P. Fryzlewicz (2018), The Annals of Statistics, 46, 3390–3421.
Value
An S3 object of class cptpath
, which contains the following fields:
solutions.nested 

solution.path 
Locations of possible changepoints in the mean of 
solution.set 
Empty list 
x 
Input vector 
type 
Input parameter 
p 
Input parameter 
cands 
Matrix of dimensions length( 
method 
The method used, which has value "tguh" here 
References
P. Fryzlewicz (2018). Tailgreedy bottomup data decompositions and fast multiple changepoint detection. The Annals of Statistics, 46, 3390–3421.
See Also
sol.idetect
, sol.idetect_seq
, sol.not
, sol.wbs
, sol.wbs2
Examples
r3 < rnorm(1000) + c(rep(0,300), rep(2,200), rep(4,300), rep(0,200))
sol.tguh(r3)