| Type: | Package |
| Title: | Complete Change Point Analysis |
| Version: | 0.1.2 |
| Description: | Provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point. |
| Depends: | R (≥ 3.1) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | geigen, ggplot2, magrittr, matrixcalc, purrr, rlang, stats |
| RoxygenNote: | 7.3.1 |
| Maintainer: | Vasileios Pavlopoulos <vasileios.pavlopoulos@uah.edu> |
| NeedsCompilation: | no |
| Packaged: | 2024-08-21 23:49:55 UTC; Business |
| Author: | Vasileios Pavlopoulos [cre, aut], Hieu Pham [aut, ctb], Paras Bhatt [aut, ctb], Yi Tan [aut, ctb], Ravi Patnayakuni [aut, ctb] |
| Repository: | CRAN |
| Date/Publication: | 2024-08-22 09:10:02 UTC |
Adjusted Ratio Binary Segmentation
Description
Adjusted ratio binary segmentation.
Usage
adjusted_ratio_bin_seg(input_data, minseglen, alpha)
Arguments
input_data |
A numeric matrix of observations for multivariate time series data where the dimension is not greater than the observations. Date columns should not be inputted. |
minseglen |
Minimum segment length for detecting change points. |
alpha |
Level of significance for calculating the confidence intervals. |
Value
A list with change points and segments.
Examples
# Example usage
data <- matrix(rnorm(1000), ncol = 10)
result <- adjusted_ratio_bin_seg(data, minseglen = 30, alpha = 0.05)
Detect and Estimate Change Points
Description
Detect and estimate change points.
Usage
decp(
input_data,
alpha = 0.05,
num_simulations = 10000,
num_iterations = 100,
verbose = TRUE
)
Arguments
input_data |
A numeric matrix of observations for multivariate time series data where the dimension is not greater than the observations. Date columns should not be inputted. |
alpha |
Level of significance for calculating the confidence intervals |
num_simulations |
Specifies the number of simulations to be conducted during the estimation process. It is recommended to set num_simulations to a large value to ensure greater certainty and reliability of the results. A higher number of simulations helps in capturing the variability and improves the accuracy of the estimation. |
num_iterations |
Determines the size of the two-sided random walk in the estimation process (each path). If the jump size of the change point is small, num_iterations should be set to higher values to achieve accurate results. For jump size >= 1, the default value is 100. |
verbose |
Logical value indicating whether to print messages during the function execution. Default is TRUE. |
Value
An object of class 'decp_result' containing the ordered change points, the summary of the jump sizes for each pair of segments, the Confidence Interval (C.I.) of each detected change point, the maximum zhta, confidence interval level, and warnings in case that the C.I. of two adjacent change points overlap.
Examples
# Example usage
data_part1 <- matrix(rnorm(1500, mean = 0, sd = 1), ncol = 5)
data_part2 <- matrix(rnorm(1500, mean = 3, sd = 1), ncol = 5)
data <- rbind(data_part1, data_part2)
result <- decp(data, alpha = 0.05, num_simulations = 100, num_iterations = 50)
print(result)
MLE Change Point Detection
Description
Maximum likelihood estimation change point detection.
Usage
mle_change_point_detection(input_data, verbose = TRUE)
Arguments
input_data |
A numeric matrix of observations for multivariate time series data where the dimension is not greater than the observations. Date columns should not be inputted. |
verbose |
Logical value indicating whether to print messages during the function execution. Default is TRUE. |
Value
An object of class 'mle_change_point_result' containing the index of the change point estimate, its MLE value, and the MLE data.
Examples
# Example usage
data <- matrix(rnorm(1000), ncol = 10)
tau_range <- 30:(nrow(data) - 30)
result <- mle_change_point_detection(data)
print(result)
Plot method for decp_result
Description
Plot method for decp_result
Usage
## S3 method for class 'decp_result'
plot(x, ...)
Arguments
x |
An object of class 'decp_result' |
... |
Additional arguments passed to the plotting function |
Plot method for mle_change_point_result
Description
Plot method for mle_change_point_result
Usage
## S3 method for class 'mle_change_point_result'
plot(x, ...)
Arguments
x |
An object of class 'mle_change_point_result' |
... |
Additional arguments passed to the plotting function |
Print method for decp_result
Description
Print method for decp_result
Usage
## S3 method for class 'decp_result'
print(x, ...)
Arguments
x |
An object of class 'decp_result' |
... |
Additional arguments (not used) |
Print method for 'mle_change_point_result' class
Description
Print method for 'mle_change_point_result' class
Usage
## S3 method for class 'mle_change_point_result'
print(x, ...)
Arguments
x |
An object of class 'mle_change_point_result'. |
... |
Additional arguments (not used). |
Simulate Estimation
Description
The estimation of the detected change point.
Usage
simulate_estimation(
lambda1,
lambda2,
term1,
term2,
num_simulations,
num_iterations
)
Arguments
lambda1 |
Eigenvalues of the first segment. |
lambda2 |
Eigenvalues of the second segment. |
term1 |
The negative drift term of the left hand side of the random walk. |
term2 |
The negative drift term of the right hand side of the random walk. |
num_simulations |
Specifies the number of simulations to be conducted during the estimation process. It is recommended to set num_simulations to a large value to ensure greater certainty and reliability of the results. A higher number of simulations helps in capturing the variability and improves the accuracy of the estimation. |
num_iterations |
Determines the size of the two-sided random walk in the estimation process (each path). If the jump size of the change point is small, num_iterations should be set to higher values to achieve accurate results. For jump size >= 1, the default value is 100. |
Value
A numeric vector of the estimation results centered around zero. The spike of the histogram is represents estimated change point, and it is expected to be at zero.
Examples
# Example usage
lambda1 <- rnorm(10)
lambda2 <- rnorm(10)
term1 <- -1
term2 <- -2
result <- simulate_estimation(lambda1, lambda2, term1, term2,
num_iterations = 100, num_simulations = 100)
Summary method for decp_result
Description
Summary method for decp_result
Usage
## S3 method for class 'decp_result'
summary(object, ...)
Arguments
object |
An object of class 'decp_result' |
... |
Additional arguments (not used) |