euclidean {DFA} R Documentation

## euclidean method for detection of crossover points

### Description

Applies the euclidean method for detection of crossover points on the log-log curve.

### Usage

euclidean(x,y,npoint)


### Arguments

 x Vector of the decimal logarithm of the boxes sizes. y Vector of the decimal logarithm of the DFA calculated in each boxe. npoint Number of crossover points calculated on the log-log curve.

### Value

 position Position of the crossover point identified by the euclidean method. sugestion_before Sugestion for the position of the second crossover point identified by the euclidean method and calculated in the area before the first crossover point. sugestion_after Sugestion for the position of the second crossover point identified by the euclidean method and calculated in the area after the first crossover point.

### Author(s)

Victor Barreto Mesquita

### References

https://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line

### Examples

# Example with crossover point fixed in position=20.

library(DFA)
data(lrcorrelation)
x<-lrcorrelation$log10(boxes) y<-c(lrcorrelation$log10(DFA(alpha = 0.1))[1:20],lrcorrelation$log10(DFA(alpha = 0.3))[21:40]) plot(x,y,xlab="log10(boxes)",ylab="log10(DFA)",pch=19) fit<- lm(y[1:20] ~ x[1:20]) fit2<-lm(y[21:40] ~ x[21:40]) abline(fit,col="blue") abline(fit2,col="red") euclidean(x,y,npoint=1) # Example with crossover point fixed in position=13 and 26. library(DFA) data(lrcorrelation) x<-lrcorrelation$log10(boxes)
y<-c(lrcorrelation$log10(DFA(alpha = 0.2))[1:13],lrcorrelation$log10(DFA(alpha = 0.6))[14:26]
,lrcorrelation\$log10(DFA(alpha = 0.9))[27:40])
plot(x,y,xlab="log10(boxes)",ylab="log10(DFA)",pch=19)
fit<- lm(y[1:13] ~ x[1:13])
fit2<-lm(y[14:26] ~ x[14:26])
fit3<-lm(y[27:40] ~ x[27:40])
abline(fit,col="blue")
abline(fit2,col="red")
abline(fit3,col="brown")
euclidean(x,y,npoint=2)



[Package DFA version 0.9.0 Index]