| Type: | Package |
| Title: | Backward Procedure for Change-Point Detection |
| Version: | 0.1.0 |
| Maintainer: | Seung Jun Shin <sjshin@korea.ac.kr> |
| Description: | Implements a backward procedure for single and multiple change point detection proposed by Shin et al. <doi:10.48550/arXiv.1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection. |
| License: | GPL-2 |
| Depends: | R (≥ 3.4.0) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | yes |
| Packaged: | 2019-01-03 02:41:40 UTC; seungjunshin |
| Author: | Seung Jun Shin [aut, cre], Yichao Wu [aut], Ning Hao [aut] |
| Repository: | CRAN |
| Date/Publication: | 2019-01-04 11:10:03 UTC |
Backward procedure for the change point detection
Description
Implements backward procedure for detecting single or multiple change points.
Usage
bwd(y, alpha = 0.05, kmin = 3, lastkgroup = floor(0.01 * n),
mu0 = NULL, normal = T, n.permute = 1000, h = 10)
Arguments
y |
observed data |
alpha |
target level that detemines stopping criterion. Default is 0.05 |
kmin |
minimum length of segements for checking possible change points |
lastkgroup |
We can abvoid chekcing possible change points when we have less groups than "lastkgroup" to improve computational efficiency. Default is 0.01 * n |
mu0 |
Baseline mean value whe detecting epidemic chang points. Defalut is |
normal |
if |
n.permute |
number of permutation when computing the permuted cutoff. Defalut is 1000 |
h |
bandwidth size for variance esitimator |
Value
bwd object that contains information of detected segments and significance levels
Author(s)
Seung Jun Shin, Yicaho Wu, Ning Hao
References
Shin, Wu, and Hao (2018+) A backward procedure for change-point detection with applications to copy number variation detection, arXiv:1812.10107.
See Also
Examples
# simulated data
set.seed(1)
n <- 1000
L <- 10
mu0 <- -0.5
mu <- rep(mu0, n)
mu[(n/2 + 1):(n/2 + L)] <- mu0 + 1.6
mu[(n/4 + 1):(n/4 + L)] <- mu0 - 1.6
y <- mu + rnorm(n)
alpha <- c(0.01, 0.05)
# BWD
obj1 <- bwd(y, alpha = alpha)
# Modified for epidemic changes with a known basline mean, mu0.
obj2 <- bwd(y, alpha = alpha, mu0 = 0)
par(mfrow = c(2,1))
plot(obj1, y)
plot(obj2, y)
plot for the backward procedure for the change point detection
Description
A plot of segments estimated by the backward procedure.
Usage
## S3 method for class 'bwd'
plot(x, y, ...)
Arguments
x |
bwd object |
y |
observed data |
... |
graphical parameters |
Value
plot of estimated segments
Author(s)
Seung Jun Shin, Yicaho Wu, Ning Hao
References
Shin, Wu, and Hao (2018+) A backward procedure for change-point detection with applications to copy number variation detection, arXiv:1812.10107.
See Also
Examples
# simulated data
set.seed(1)
n <- 1000
L <- 10
mu0 <- -0.5
mu <- rep(mu0, n)
mu[(n/2 + 1):(n/2 + L)] <- mu0 + 1.6
mu[(n/4 + 1):(n/4 + L)] <- mu0 - 1.6
y <- mu + rnorm(n)
alpha <- c(0.01, 0.05)
# BWD
obj1 <- bwd(y, alpha = alpha)
# Modified for epidemic changes with a known basline mean, mu0.
obj2 <- bwd(y, alpha = alpha, mu0 = 0)
par(mfrow = c(2,1))
plot(obj1, y)
plot(obj2, y)