glla: Generalized Local Linear Approximation

In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Generalized Local Linear Approximation

Description

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.

Usage

glla(x, n.embed, tau, delta, order)

Arguments

x	Numeric vector. An observed time series
n.embed	Numeric (length = 1). Number of embedded dimensions (the number of observations to be used in the Embed function)
tau	Numeric (length = 1). Number of observations to offset successive embeddings in the Embed function. A tau of one uses adjacent observations. Default is 1
delta	Numeric (length = 1). The time between successive observations in the time series. Default is 1
order	Numeric (length = 1). 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)

Value

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

Author(s)

Hudson Golino <hfg9s at virginia.edu>

References

GLLA implementation
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.

Filtering procedure
Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639.

Examples

# A time series with 8 time points
tseries <- 49:56
deriv.tseries <- glla(tseries, n.embed = 4, tau = 1, delta = 1, order = 2)

EGAnet documentation built on Sept. 28, 2024, 9:06 a.m.