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 Savitzky-Golay filtering to accomplish the task.
glla(x, n.embed, tau, delta, order)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.
Vector. An observed time series.
Integer.
Number of embedded dimensions (the number of observations to be used in the Embed function).
Integer.
Number of observations to offset successive embeddings in the Embed function. A tau of one uses adjacent observations.
Default is "tau = 1".
Integer.
The time between successive observations in the time series.
Default is "delta = 1".
Integer.
The maximum order of the derivative to be estimated. For example,
"order = 2" will return a matrix with three columns with the estimates
of the observed scores and the first and second derivative for each row of the embedded
matrix (i.e. the reorganization of the time series implemented via
the Embed function).
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. 161-178). 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), 367-386.
Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639.
# A time series with 8 time points
tseries <- 49:56
deriv.tseries <- glla(tseries, n.embed = 4, tau = 1, delta = 1, order = 2)
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