| Type: | Package |
| Title: | Peak Functions for Peak Detection in Univariate Time Series |
| Version: | 0.1.2 |
| Maintainer: | Shota Ochi <shotaochi1990@gmail.com> |
| Description: | Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series. |
| License: | GPL-3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | checkmate (≥ 1.9.1), Rcpp (≥ 1.0.0) |
| Suggests: | knitr, rmarkdown, testthat (≥ 2.0.0), cluster |
| URL: | https://github.com/ShotaOchi/scorepeak |
| BugReports: | https://github.com/ShotaOchi/scorepeak/issues |
| NeedsCompilation: | yes |
| LinkingTo: | Rcpp |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| Packaged: | 2019-08-20 12:16:24 UTC; shota |
| Author: | Shota Ochi [aut, cre, cph] |
| Repository: | CRAN |
| Date/Publication: | 2019-08-21 08:20:02 UTC |
scorepeak: Peak Functions for Peak Detection in Univariate Time Series
Description
scorepeak provides peak functions and its building blocks. Peak functions enable us to detect peaks.
Building Blocks of Peak Functions
Description
Computes max, min, mean, and standard deviation of temporal neighbors.
Usage
max_neighbors(data, w, side, boundary = "reflecting")
min_neighbors(data, w, side, boundary = "reflecting")
mean_neighbors(data, w, side, boundary = "reflecting")
sd_neighbors(data, w, side, boundary = "reflecting")
Arguments
data |
a numeric vector. Length of data must be greater than 1. |
w |
window size. w must be odd and greater than 2 and smaller than double length of data. |
side |
determines which side of neighbors of data point will be used in calculation. "left", "l": left temporal neighbors, "right", "r": right temporal neighbors, "both", "b": left and right temporal neighbors, "all", "a": data point and its left and right temporal neighbors. |
boundary |
determines how data points in the beginning and end of the time series will be treated. "reflecting", "r": reflecting boundary condition, "periodic", "p": periodic boundary condition, "discard", "d", discarding data points in the beginning and end of the time series. See the vignette "Introduction to scorepeak" for detail. |
Value
a numeric vector
Author(s)
Shota Ochi
Examples
data("ecgca102")
max_neighbors(ecgca102, 11, "all")
min_neighbors(ecgca102, 11, "all")
mean_neighbors(ecgca102, 11, "all")
sd_neighbors(ecgca102, 11, "all")
detect local maxima in univariate time series data
Description
detect local maxima in univariate time series data
Usage
detect_localmaxima(data, w = 3, boundary = "reflecting")
Arguments
data |
a numeric vector. Length of data must be greater than 1. |
w |
window size. w must be odd and greater than 2 and smaller than double length of data. |
boundary |
determines how data points in the beginning and end of the time series will be treated. "reflecting", "r": reflecting boundary condition, "periodic", "p": periodic boundary condition, "discard", "d", discarding data points in the beginning and end of the time series. See the vignette "Introduction to scorepeak" for detail. |
Value
a logical vector. TRUE indicates local peak. FALSE indicates not local peak.
Author(s)
Shota Ochi
Examples
data("ecgca102")
peaks <- detect_localmaxima(ecgca102)
plot(ecgca102, type = "l")
points(which(peaks), ecgca102[peaks], pch = 1, col = "red")
Time Series Data of Electrocardiogram
Description
This data is a part of ecgca102.edf file of Non-Invasive Fetal Electrocardiogram Database.
Usage
data("ecgca102")
Format
a numeric vector
Source
Non-Invasive Fetal Electrocardiogram Database (https://doi.org/10.13026/C2X30H)
References
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).
Peak Functions for Peak Detection in Univariate Time Series
Description
scorepeak package provides several types of peak function. See the vignette "Introduction to scorepeak" for detail.
Usage
score_type1(data, w, boundary = "reflecting")
score_type2(data, w, boundary = "reflecting")
score_type3(data, w, boundary = "reflecting")
Arguments
data |
a numeric vector. Length of data must be greater than 1. |
w |
window size. w must be odd and greater than 2 and smaller than double length of data. |
boundary |
determines how data points in the beginning and end of the time series will be treated. "reflecting", "r": reflecting boundary condition, "periodic", "p": periodic boundary condition, "discard", "d", discarding data points in the beginning and end of the time series. See the vignette "Introduction to scorepeak" for detail. |
Value
a numeric vector
Author(s)
Shota Ochi
Examples
data("ecgca102")
plot(ecgca102, type = "l", ylim = c(-0.38, 0.53))
points(seq(length(ecgca102)), score_type1(ecgca102, 51), col = "red", type = "l")
points(seq(length(ecgca102)), score_type2(ecgca102, 51), col = "blue", type = "l")
points(seq(length(ecgca102)), score_type3(ecgca102, 51), col = "green", type = "l")