nlar {funfits} | R Documentation |
his function fits a model of the form: Y_t = f( Y_(t-l1),...{},Y_(t-ld),S_t) + e_t Where e_t is assumed to mean zero, uncorrelated errors. Such a form is useful for testing whether a system is chaotic.
nlar(Y, lags, cov=NA, method="nnreg", ...)
Y |
The time series |
lags |
A vector that specifies which lags of Y to use in the autoregressive function |
cov |
A vector or matrix of covariates as long as the Y series these are additional variables that will be used in the regression function |
method |
Name of S function to fit the nonparametric model e.g. nnreg tps addreg |
... |
Optional argument that as passed through to the regression method |
An object of class nlar
FUNFITS manual
lle, predict.nlar
# Fit the rossler series. A toy dynamical system that is chaotic # Use a neural network with 4 hidden units based on lags 1, 2 and 3 of the series. nlar( rossler,lags=c(1,2,3), method="nnreg",k1=4)-> out summary(out) plot( out) lle( out) # calculate local and global Lyapunov exponents