| aar {tsDyn} | R Documentation |
Additive nonlinear autoregressive model
Description
Additive nonlinear autoregressive model.
Usage
aar(x, m, d=1, steps=d, series)
Arguments
x |
time series |
m, d, steps |
embedding dimension, time delay, forecasting steps |
series |
time series name (optional) |
Details
Nonparametric additive autoregressive model of the form:
x_{t+s} = \mu + \sum_{j=1}^{m} s_j(x_{t-(j-1)d})
where s_j are nonparametric univariate functions of lagged time
series values. They are represented by cubic regression splines.
s_j are estimated together with their level of
smoothing using routines in the mgcv package (see references).
Value
An object of class nlar, subclass aar, i.e. a list
with mostly internal structures for the fitted gam object.
Author(s)
Antonio, Fabio Di Narzo
References
Wood, mgcv:GAMs and Generalized Ridge Regression for R. R News 1(2):20-25 (2001)
Wood and Augustin, GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecological Modelling 157:157-177 (2002)
Examples
#fit an AAR model:
mod <- aar(log(lynx), m=3)
#Summary informations:
summary(mod)
#Diagnostic plots:
plot(mod)