ACA (version 1.1)

SDScan: This is the workhorse function of the ACA. It detects significant change-points in serial data.

Description

This is the workhorse function of the ACA. It detects significant change-points in serial data.

Usage

SDScan(namefi = NULL, xleg = NULL, yleg = NULL, titl = NULL,
  onecol = NULL, daty = NULL, gray = NULL)

Arguments

namefi

- a character string specifying the data file to be loaded

xleg

- character. The x-label of the plot

yleg

- character. The y-label of the plot

titl

- character. The title of the plot

onecol

- character. Option for the data format. If onecol is "y", it is assumed that the input file is a single column file (varying parameter) else the input file is a 2 column file (independent variable, varying parameter)

daty

- character. Option for the data processing. If daty is "y", the scan of the series is launched with the gradients (rates of change) of the data else it is launched with the data itself

gray

- character. Option for the plot. If gray is "y", the background of the plot is gray else it is white

Details

if one of the arguments above is NULL, then the user will be prompted to enter the missing value. SDScan() produces two files: the SDS.res file includes the statistics for each detected breakpoint; the SDS.png file is the plot of the series where the detected breakpoints are shown. In the SDS.res file, there is a line for each breakpoint: it includes the x and y values for the breakpoint, its index in the series, the noise variance due to the discontinuity, the noise variance due to the trend, the noise variance due to the discontinuity (posterior value), the noise variance due to the trend (posterior value), the change-point Signal-to-Noise Ratio (posterior value), the biweight mean of the left segment, the biweight mean of the right segment. Values are separated by the ''&'' symbol. A change-point plot is returned by SDScan(). This plot shows the series and the detected change-points. Horizontal lines are drawn to represent the biweight means of the two segments defined by each change-point. The legend of the plot shows 4 numerical values for each change-point: from left to right, the rank of the change-point (as defined by the detection sequence), its location along the X-axis, its signal-to-noise ratio, and the probability value for the two-tail robust rank-order test, that was obtained right after the change-point detection

References

D. Amorese, "Applying a change-point detection method on frequency-magnitude distributions", Bull. seism. Soc. Am. (2007) 97, doi:10.1785\/0120060181 Lanzante, J. R., "Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data", International Journal of Climatology (1996) 16(11), 1197-1226 Amorese, D., Grasso, J. R., Garambois, S., and Font, M., "Change-point analysis of geophysical time-series: application to landslide displacement rate (Sechilienne rock avalanche, France)", Geophysical Journal International (2018) 213(2), 1231-1243

Examples

# NOT RUN {
data <- system.file("extdata","soccer.data.txt", package = "ACA")
SDScan(namefi=data, xleg="Time", yleg="Goals per game", titl="Goals in 
England: 1888-2014", onecol="n", daty="n", gray="y")
# }
# NOT RUN {
data <- system.file("extdata","amorese.data.txt", package = "ACA")
# }
# NOT RUN {
SDScan(namefi=data, xleg="Index", yleg="Value", titl="Change in 
a Gaussian Sequence (with trend)", onecol="n", daty="n", gray="y")
# }