euclidean {DFA} | R Documentation |
Applies the euclidean method for detection of crossover points on the log-log curve.
euclidean(x,y,npoint)
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. |
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. |
Victor Barreto Mesquita
https://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line
# 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)