nlts (version 0.1-9)

ll.order: Consistent nonlinear estimate of the order using local polynomial regression.

Description

A function to estimate the order of a time series using the nonparametric order selection method of Cheng and Tong (1992, 1994) as modified by Yao & Tong (1994; see also Fan, Yao & Tong 1996). The method uses leave-one-out cross-validation of the locally linear regression against lagged-abundances.

Usage

ll.order(x, order = 1:5, step = 1, deg = 2, bandwidth = c(seq(0.3,
    1.5, by = 0.1), 2:10), cv = TRUE, echo = TRUE)

Value

An object of class "ll.order" is returned consisting of the following components:gridthe grid of orders, bandwidths, and CV's.grid$orderthe orders.grid$CVthe cross-validation score across the grid of orders and bandwidths. (If cv = TRUE).grid$GCVthe generalized cross-validation score.grid$bandwidththe bandwidths.grid$dfthe degrees of freedom of the fitted model.orderthe vector of orders considered.degThe degree of the local polynomial.

Details

The time series is normalized prior to cross-validation. A Gaussian kernel is used for the locally linear regression. The bandwidth is optimized using crossvalidation. If a single bandwidth is provided, no cross validation of bandwidth will be carried out. Highly nonlinear data will require more narrow bandwidths. If NA is returned it may be because the min bandwidth considered is too small relative to the density of data. Missing values are NOT permitted. If deg is set to 0, the order is estimated on the basis of the Nadaraya-Watson (locally constant) estimator of the conditional expecation against lagged-abundances (Cheng and Tong 1992, 1994). The function subsumes the nw.order of the previous S-plus nlt-library. The function requires Loader's locfit-library.

References

Cheng, B. & Tong, H. (1992) On consistent nonparametric order determination and chaos. Journal of Royal Statistical Society B, 54, 427-449. Cheng, B. & Tong, H. (1994) Orthogonal projection, embedding dimension and sample size in chaotic time series from a statistical perspective. Philosophical Transactions of the Royal Society London, A. , 348, 325-341. Fan, J., Yao, Q., & Tong, H. (1996) Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika, 83, 189-206. Yao, Q. & Tong, H. (1994) Quantifying the influence of initial values on non-linear prediction. Journal of Royal Statistical Society B, 56, 701-725. Bjornstad, O.N., Sait, S.M., Stenseth, N.C., Thompson, D.J., & Begon, M. (2001) Coupling and the impact of specialised enemies on the dimensionality of prey dynamics. Nature, 409, 1001-1006. Loader, C. (1999) Local Regression and Likelihood. Springer, New York.

See Also

summary.ll.order plot.ll.order

Examples

data(plodia)

   fit1 <- ll.order(sqrt(plodia), order=1:3, bandwidth
               = seq(0.5, 1.5, by = 0.5)) 

    plot.ll.order(fit1)

    summary.ll.order(fit1)