cpt.np | R Documentation |

Calculates the optimal positioning and number of changepoints for data given a user specified cost function and penalty.

cpt.np( data, penalty = "MBIC", pen.value = 0, method = "PELT", test.stat = "empirical_distribution", class = TRUE, minseglen = 1, nquantiles = 10, verbose = TRUE )

`data` |
A vector, ts object or matrix containing the data within which you wish to find a changepoint. If the data is a matrix, each row is considered as a separate dataset. |

`penalty` |
Choice of "None", "SIC", "BIC", "MBIC", AIC", "Hannan-Quinn", "Manual" and "CROPS" penalties. If Manual is specified, the manual penalty is contained in the pen.value parameter. If CROPS is specified, the penalty range is contained in the pen.value parameter; note this is a vector of length 2 which contains the minimum and maximum penalty value. Note CROPS can only be used if the method is "PELT". The predefined penalties listed DO count the changepoint as a parameter, postfix a 0 e.g."SIC0" to NOT count the changepoint as a parameter. |

`pen.value` |
The value of the penalty when using the Manual penalty option. A vector of length 2 (min,max) if using the CROPS penalty. |

`method` |
Currently the only method is "PELT". |

`test.stat` |
The assumed test statistic/distribution of the data. Currently only "empirical_distribution". |

`class` |
Logical. If TRUE then an object of class cpt is returned. |

`minseglen` |
Positive integer giving the minimum segment length (number of observations between changes), default is the minimum allowed by theory. |

`nquantiles` |
The number of quantiles to calculate when test.stat = "empirical_distribution". |

`verbose` |
Logical value. If TRUE then progress will be reported when penalty=CROPS. Default value is TRUE. |

This function is used to find multiple changes in a data set using the changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution. A changepoint is denoted as the first observation of the new segment.

If `class=TRUE`

then an object of S4 class "cpt" is returned. The slot `cpts`

contains the changepoints that are returned. For `class=FALSE`

the structure is as follows.

If data is a vector (single dataset) then a vector/list is returned depending on the value of method. If data is a matrix (multiple datasets) then a list is returned where each element in the list is either a vector or list depending on the value of method.

If method is PELT then a vector is returned containing the changepoint locations for the penalty supplied. If the penalty is CROPS then a list is returned with the elements:

`cpt.out` |
A data frame containing the value of the penalty value where the number of segmentations changes, the number of segmentations and the value of the cost at that penalty value. |

`changepoints` |
The optimal changepoints for the different penalty values starting with the lowest penalty value. |

Kaylea Haynes

Haynes2017changepoint.np

\insertRefKillick2012changepoint.np

\insertRefHaynes2015changepoint.np

PELT in parametric settings: `cpt.mean`

for changes in the mean, `cpt.var`

for changes in the variance and `cpt.meanvar`

for changes in the mean and variance.

#Example of a data set of length 1000 with changes in location #(model 1 of Haynes, K et al. (2016)) with the empirical distribution cost function. set.seed(12) J <- function(x){ (1+sign(x))/2 } n <- 1000 tau <- c(0.1,0.13,0.15,0.23,0.25,0.4,0.44,0.65,0.76,0.78,0.81)*n h <- c(2.01, -2.51, 1.51, -2.01, 2.51, -2.11, 1.05, 2.16, -1.56, 2.56, -2.11) sigma <- 0.5 t <- seq(0,1,length.out = n) data <- array() for (i in 1:n){ data[i] <- sum(h*J(n*t[i] - tau)) + (sigma * rnorm(1)) } out <- cpt.np(data, penalty = "SIC",method="PELT",test.stat="empirical_distribution", class=TRUE,minseglen=2, nquantiles =4*log(length(data))) cpts(out) #returns 100 130 150 230 250 400 440 650 760 780 810 as the changepoint locations. plot(out) #Example 2 uses the heart rate data . data(HeartRate) cptHeartRate <- cpt.np(HeartRate, penalty = "CROPS", pen.value = c(5,200), method="PELT", test.stat="empirical_distribution", class=TRUE,minseglen=2, nquantiles =4*log(length(HeartRate))) plot(cptHeartRate, diagnostic = TRUE) plot(cptHeartRate, ncpts = 11)

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