lle {funfits} | R Documentation |
lle(jac, model=1, nprod=c(5, 10, 20, 40, 80), skip, statevector=F, lags=NA)
jac |
Jacobian matrix or a nnreg fit. |
model |
Model number of fit used to calculate Jacobians. |
nprod |
Vector of LLE products of Jacobians. |
skip |
Columns of Jacobian matrix to skip in calculating LLEs. For example, skip the columns associated with forcing functions. |
statevector |
If false, a time-delay reconstruction model is assumed and a Jacobian matrix n by d is expected, where n is the length of the time series and d is the dimension of the state space. If true, a state space vector model is assumed and a Jacobian matrix n by d^2 is expected. |
lags |
Lagged time values used in the Jacobian matrix. |
local |
Matrix of LLEs with columns corresponding to the LLEs of the nprod values. |
nprod |
Vector of LLE products of Jacobians. |
glb |
Global Lyapunov exponent. |
model |
Model number used to calculate Jacobians. |
S. Ellner, D.W. Nychka, and A.R. Gallant. 1992. LENNS, a program to estimate the dominant Lyapunov exponent of noisy nonlinear systems from time series data. Institute of Statistics Mimeo Series #2235, Statistics Department, North Carolina State University, Raleigh, NC 27695-8203.
make.lle
make.lags(rossler.state[1:200,1],c(1,2,3)) -> data.r # create # 3-d time delay vector model of the x variable of rossler system. nnreg(data.r$x,data.r$y,5,5) -> fit # fit time series model using nnreg. jac<- predict(fit, derivative=1) lle(jac) -> rossler.lle # LLEs of Rossler data summary(lle) plot(rossler.lle) # plot LLEs # here is an easier way nlar( rossler[1:200], lags=1:3, method="nnreg", k1=5)-> ou lle( out) -> rossler.lle