Base class for changepoint models
mod_cpt.RdCreate changepoint detection model objects
Arguments
- x
a numeric vector coercible into a
tsobject- tau
indices of the changepoint set
- region_params
A
tibble::tibble()with one row for each region defined by the changepoint settau. Each variable represents a parameter estimated in that region.- model_params
A numeric vector of parameters estimated by the model across the entire data set (not just in each region).
- fitted_values
Fitted values returned by the model on the original data set.
- model_name
A
charactervector giving the model's name.- ...
currently ignored
Details
Changepoint detection models know how they were created, on what data set, about the optimal changepoint set found, and the parameters that were fit to the model. Methods for various generic reporting functions are provided.
All changepoint detection models inherit from mod_cpt: the
base class for changepoint detection models.
These models are created by one of the fit_*() functions, or by
as.model().
Examples
cpt <- mod_cpt(CET)
str(cpt)
#> List of 6
#> $ data : Time-Series [1:362] from 1 to 362: 8.87 9.1 9.78 9.52 8.63 9.34 8.29 9.86 8.52 9.51 ...
#> $ tau : int(0)
#> $ region_params: tibble [0 × 0] (S3: tbl_df/tbl/data.frame)
#> Named list()
#> $ model_params : num(0)
#> $ fitted_values: num(0)
#> $ model_name : chr(0)
#> - attr(*, "class")= chr "mod_cpt"
as.ts(cpt)
#> Time Series:
#> Start = 1
#> End = 362
#> Frequency = 1
#> [1] 8.87 9.10 9.78 9.52 8.63 9.34 8.29 9.86 8.52 9.51 9.02 8.96
#> [13] 9.08 8.82 8.38 8.12 7.88 8.84 8.78 8.45 8.76 8.89 8.75 9.05
#> [25] 8.49 7.95 9.16 10.15 8.99 7.86 8.56 8.94 8.17 7.73 8.47 7.67
#> [37] 7.29 8.52 8.05 7.67 8.83 8.60 8.75 9.31 9.09 9.07 8.75 9.82
#> [49] 9.41 9.68 8.74 9.49 9.42 9.14 8.64 9.44 9.44 8.38 9.04 9.29
#> [61] 9.49 9.10 8.91 9.37 9.80 9.28 8.69 9.36 9.97 9.54 9.29 10.07
#> [73] 9.90 9.69 10.50 9.82 9.57 10.33 9.95 9.84 9.21 6.86 9.32 8.38
#> [85] 9.83 8.80 8.85 8.66 9.84 8.79 9.49 9.71 8.47 9.21 9.12 8.87
#> [97] 8.58 8.78 8.97 8.98 10.03 9.85 10.02 9.61 8.95 8.73 8.55 8.66
#> [109] 8.70 8.95 8.81 8.53 8.58 9.17 9.29 9.09 10.11 9.02 9.12 9.23
#> [121] 10.41 9.12 10.23 8.05 9.31 7.85 8.58 8.27 9.31 9.22 8.93 9.46
#> [133] 9.29 9.20 9.13 9.90 8.71 9.02 9.03 9.63 7.92 9.28 9.63 8.98
#> [145] 9.09 9.59 9.00 9.84 8.69 8.86 8.96 8.78 9.69 8.21 8.72 7.78
#> [157] 9.07 7.89 8.89 9.88 9.27 8.56 9.55 10.06 8.40 9.32 9.76 10.09
#> [169] 9.51 10.32 8.18 8.72 10.13 9.49 9.52 10.51 9.57 8.88 8.85 8.10
#> [181] 8.71 8.52 8.75 9.25 9.10 8.60 8.30 10.16 9.26 9.42 9.32 9.10
#> [193] 9.18 9.82 8.41 9.34 8.09 9.10 10.11 9.16 9.64 7.92 9.15 9.21
#> [205] 9.69 8.87 9.72 9.68 9.03 10.40 9.62 9.02 9.07 9.76 9.03 9.33
#> [217] 9.48 9.53 9.19 9.26 7.44 9.10 8.58 9.47 9.04 9.85 8.58 8.74
#> [229] 8.30 8.24 9.02 8.76 8.51 8.18 10.01 9.32 8.70 9.34 9.44 10.10
#> [241] 9.71 9.60 9.15 8.88 9.33 9.02 9.16 9.47 8.88 9.28 8.59 9.20
#> [253] 10.09 9.37 9.83 9.90 8.96 9.20 8.55 9.53 8.53 9.58 10.51 8.70
#> [265] 9.11 9.29 9.20 9.74 9.24 9.59 9.06 9.47 9.02 9.41 9.86 10.03
#> [277] 9.74 9.35 9.59 10.21 9.70 9.06 9.12 9.11 10.05 9.59 10.29 9.46
#> [289] 9.65 10.03 10.64 9.43 9.30 9.12 9.87 9.26 9.33 8.87 10.06 9.45
#> [301] 10.52 9.75 9.95 8.61 8.52 9.48 8.99 9.47 9.64 9.32 9.32 9.60
#> [313] 9.72 9.22 9.57 9.64 10.04 10.10 9.51 9.47 8.85 9.42 9.28 9.86
#> [325] 10.10 9.75 8.90 8.81 9.08 9.80 10.54 10.65 9.58 9.87 9.52 10.29
#> [337] 10.55 9.22 10.56 10.35 10.65 10.32 9.97 10.63 10.54 10.50 10.48 10.87
#> [349] 10.50 9.97 10.14 8.86 10.72 9.72 9.61 10.95 10.31 10.34 10.58 10.68
#> [361] 10.34 10.76
changepoints(cpt)
#> integer(0)