| Type: | Package |
| Title: | Detection of Chaotic and Regular Intervals in the Data |
| Version: | 0.1.0 |
| Description: | Finds regular and chaotic intervals in the data using the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) <doi:10.1137/080718851>. |
| Depends: | R (≥ 3.5.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | no |
| Imports: | Chaos01 |
| RoxygenNote: | 6.1.1 |
| Packaged: | 2019-03-22 12:14:11 UTC; radek |
| Author: | Radek Halfar [aut, cre] |
| Maintainer: | Radek Halfar <radek.halfar@vsb.cz> |
| Repository: | CRAN |
| Date/Publication: | 2019-03-25 11:00:03 UTC |
Find chaotic motions in the data.
Description
Find chaotic motions in the data.
Usage
find_chaos(data, window_length, skip_window, skip_test01 = 1,
test01_thresh = 0.05, find_thresh = 20)
Arguments
data |
Analyzed data. |
window_length |
Length of the window for in which the 0-1 test for chaos will be computed. |
skip_window |
Length of the skip of the window moving in the data. |
skip_test01 |
Length of the skip to take data for calculation the 0-1 test for chaos in the window. |
test01_thresh |
The threshold to decide about motion. |
find_thresh |
Precision of found intervals. |
Value
The list of optimized chaotic motion borders.
Examples
# Calculate the logistic map.
cons <- 0.5
data.len <- 17000
chaos.start <- c(5536, 9768)
vec.x <- matrix(cons, data.len, 1)
vec.x[1] <- (2^0.5)/2
for (i in 2:data.len){
# x_n+1 = r*x_n(1-x_n)
vec.x[i] <- 3.7*vec.x[i-1]*(1-vec.x[i-1])
}
vec.x[1:(chaos.start[1]-1)] <-cons
vec.x[(chaos.start[2]+1):data.len] <-cons
tr1 <- seq(from = cons, to = vec.x[chaos.start[1]], length.out = 2001)
tr2 <- seq(from = vec.x[chaos.start[2]], to = cons, length.out = 2001)
vec.x[(chaos.start[1]-2000):chaos.start[1]] <- tr1
vec.x[chaos.start[2]:(chaos.start[2]+2000)] <- tr2
# Find chaotic intervals in vec.x and plot results.
chaotic_borders <- find_chaos(vec.x, "skip_window" = 1000,
"window_length" = 3000, "find_thresh" = 300)
Find regular and chaotic motions in the data and plots the results.
Description
Find regular and chaotic motions in the data and plots the results.
Usage
find_motions(data, window_length, skip_window, skip_test01 = 1,
test01_thresh = 0.05, find_thresh = 20)
Arguments
data |
Analyzed data. |
window_length |
Length of the window for in which the 0-1 test for chaos will be computed |
skip_window |
Length of the skip of the window moving in the data. |
skip_test01 |
Length of the skip to take data for calculation the 0-1 test for chaos in the window. |
test01_thresh |
The threshold to decide about motion. |
find_thresh |
Precision of found intervals. |
Value
The list of optimized regular and chaotic motion borders.
Examples
# Calculate the logistic map.
cons <- 0.5
data.len <- 17000
chaos.start <- c(5536, 9768)
vec.x <- matrix(cons, data.len, 1)
vec.x[1] <- (2^0.5)/2
for (i in 2:data.len){
# x_n+1 = r*x_n(1-x_n)
vec.x[i] <- 3.7*vec.x[i-1]*(1-vec.x[i-1])
}
vec.x[1:(chaos.start[1]-1)] <-cons
vec.x[(chaos.start[2]+1):data.len] <-cons
tr1 <- seq(from = cons, to = vec.x[chaos.start[1]], length.out = 2001)
tr2 <- seq(from = vec.x[chaos.start[2]], to = cons, length.out = 2001)
vec.x[(chaos.start[1]-2000):chaos.start[1]] <- tr1
vec.x[chaos.start[2]:(chaos.start[2]+2000)] <- tr2
# Find chaotic and regular intervals in vec.x and plot results.
find_motions(vec.x, "skip_window" = 1000, "window_length" = 3000, "find_thresh" = 300)
Find regular motions in the data.
Description
Find regular motions in the data.
Usage
find_regularity(data, window_length, skip_window, skip_test01 = 1,
test01_thresh = 0.05, find_thresh = 20)
Arguments
data |
Analyzed data. |
window_length |
Length of the window for in which the 0-1 test for chaos will be computed. |
skip_window |
Length of the skip of the window moving in the data. |
skip_test01 |
Length of the skip to take data for calculation the 0-1 test for chaos in the window. |
test01_thresh |
The threshold to decide about motion. |
find_thresh |
Precision of found intervals. |
Value
The list of optimized regular and chaotic motion borders.
Examples
# Calculate the logistic map.
cons <- 0.5
data.len <- 17000
chaos.start <- c(5536, 9768)
vec.x <- matrix(cons, data.len, 1)
vec.x[1] <- (2^0.5)/2
for (i in 2:data.len){
# x_n+1 = r*x_n(1-x_n)
vec.x[i] <- 3.7*vec.x[i-1]*(1-vec.x[i-1])
}
vec.x[1:(chaos.start[1]-1)] <-cons
vec.x[(chaos.start[2]+1):data.len] <-cons
tr1 <- seq(from = cons, to = vec.x[chaos.start[1]], length.out = 2001)
tr2 <- seq(from = vec.x[chaos.start[2]], to = cons, length.out = 2001)
vec.x[(chaos.start[1]-2000):chaos.start[1]] <- tr1
vec.x[chaos.start[2]:(chaos.start[2]+2000)] <- tr2
# Find regular intervals in vec.x and plot results.
regular_borders <- find_regularity(vec.x, "skip_window" = 1000,
"window_length" = 3000, "find_thresh" = 300)