calculate.glla: Calculation of derivatives using the GLLA method
In doremi: Dynamics of Return to Equilibrium During Multiple Inputs

Description Usage Arguments Value Examples

calculate.glla estimates the derivatives of a variable using the Generalized Local Linear Approximation (GLLA) method described in doi: 10.4324/9780203864746Boker et al.(2010). This method estimates the derivatives over a number of measurement points called the embedding number assuming an equally spaced time series.

1	calculate.glla(signal, time, embedding = 3, n = 2)

`signal`	is the input vector containing the data from which the derivatives are estimated.
`time`	is a vector containing the time values corresponding to the signal. Arguments signal and time must have the same length.
`embedding`	is an integer indicating the embedding dimension, that is the number of points to consider for derivative calculation. Embedding must be at least #' 2 for the calculation of the first derivative (first order models) and at least 3 for the calculation of the second derivative (second order models).
`n`	is the maximum order of the derivative to calculate

Returns a list containing three columns:

dtime- contains the time values in which the derivative was calculated. That is, the moving average of the input time over embedding points.

dsignal- is a data.frame containing n+1 columns and the same number of rows as the signal. The column k is the k-1 order derivative of the signal over embedding points.

embedding- number of points used for the derivative calculation.

n - the maximum derivative order calculated n

#In the following example the derivatives for the function y(t) = t^2 are calculated.
#The expected results are:
#y'(t) = 2t and y''(t) = 2
time <- c(1:500)/100
signal <- time^2
result <- calculate.glla(signal = signal, time = time, embedding = 5)