This function arranges all possible change-points in the mean of the input vector in the order of importance, via the Tail-Greedy Unbalanced Haar method.
sol.tguh(x, type = "const", p = 0.01)An S3 object of class cptpath, which contains the following fields:
TRUE, i.e., the change-point outputs are nested
Locations of possible change-points in the mean of x, arranged in decreasing order of change-point importance
Empty list
Input vector x
Input parameter type
Input parameter p
Matrix of dimensions length(x) - 1 by 4. The first two columns are (start, end)-points of the detection intervals of the corresponding possible change-point location in the third column. The fourth column is a measure of strength of the corresponding possible change-point. The order of the rows is the same as the order returned in solution.path
The method used, which has value "tguh" here
A numeric vector containing the data to be processed
The model type considered. type = "const" means piecewise-constant; this is the only type currently supported in sol.tguh
Specifies the number of region pairs merged
in each pass through the data, as the proportion of all remaining region pairs. The default is
p = 0.01
The Tail-Greedy Unbalanced Haar decomposition algorithm is described in "Tail-greedy bottom-up data decompositions and fast multiple change-point detection", P. Fryzlewicz (2018), The Annals of Statistics, 46, 3390--3421.
P. Fryzlewicz (2018). Tail-greedy bottom-up data decompositions and fast multiple change-point detection. The Annals of Statistics, 46, 3390--3421.
sol.idetect, sol.idetect_seq, sol.not, sol.wbs, sol.wbs2
r3 <- rnorm(1000) + c(rep(0,300), rep(2,200), rep(-4,300), rep(0,200))
sol.tguh(r3)
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