dlcca: Multiscale Lagged Regression Anlaysis Fast function for...

In fractalRegression: Performs Fractal Analysis and Fractal Regression

Multiscale Lagged Regression Anlaysis Fast function for computing MLRA on long time series

Description

Multiscale Lagged Regression Anlaysis Fast function for computing MLRA on long time series

Usage

dlcca(x, y, order, scales, lags, direction)

Arguments

x	is a real valued vector of time series data
y	is a real valued vector of time series data
order	is an integer indicating the polynomial order used for detrending the local windows
scales	integer vector of scales over which to compute correlation. Performance is best when scales are evenly spaced in log units. Choosing a logarithm base between 1 and 2 may also improve performance of regression.
lags	integer indicating the maximum number of lags to include
direction	string indicating a positive ('p') or negative ('n') lag

Value

The object returned from the dlcca() function is a list containing rho coefficients for each lag at each of the scales.

fractalRegression documentation built on Aug. 20, 2023, 1:06 a.m.