glla {EGAnet}  R Documentation 
Estimates the derivatives of a time series using generalized local linear approximation (GLLA). GLLA is a filtering method for estimating derivatives from data that uses time delay embedding and a variant of SavitzkyGolay filtering to accomplish the task.
glla(x, n.embed, tau, delta, order)
x 
Vector. An observed time series. 
n.embed 
Integer.
Number of embedded dimensions (the number of observations to be used in the 
tau 
Integer.
Number of observations to offset successive embeddings in the 
delta 
Integer.
The time between successive observations in the time series.
Default is 
order 
Integer.
The maximum order of the derivative to be estimated. For example,

Returns a matrix containing n columns, in which n is one plus the maximum order of the derivatives to be estimated via generalized local linear approximation.
Hudson Golino <hfg9s at virginia.edu>
Boker, S. M., Deboeck, P. R., Edler, C., & Keel, P. K. (2010) Generalized local linear approximation of derivatives from time series. In S.M. Chow, E. Ferrer, & F. Hsieh (Eds.), The Notre Dame series on quantitative methodology. Statistical methods for modeling human dynamics: An interdisciplinary dialogue, (p. 161178). Routledge/Taylor & Francis Group.
Deboeck, P. R., Montpetit, M. A., Bergeman, C. S., & Boker, S. M. (2009) Using derivative estimates to describe intraindividual variability at multiple time scales. Psychological Methods, 14(4), 367386.
Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 16271639.
# A time series with 8 time points
tseries < 49:56
deriv.tseries < glla(tseries, n.embed = 4, tau = 1, delta = 1, order = 2)