ll.order {nlts}

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)

Arguments

x

A time series without missing values.

order

The candidate orders. The default is 1:5.

step

The time step for prediction.

deg

The degree of the local polynomial.

bandwidth

The candidate bandwidths to be considered.

cv

if TRUE leave-one-out cross-validation will be performed.

echo

if TRUE a counter shows the progress

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 cross-validation. 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 expectation against lagged-abundances (Cheng and Tong 1992, 1994).

Value

An object of class "ll.order" is returned consisting of the following components:

grid

the grid of orders, bandwidths, and CV's.

grid$order

the orders.

grid$CV

the cross-validation score across the grid of orders and bandwidths. (If cv = TRUE).

grid$GCV

the generalized cross-validation score.

grid$bandwidth

the bandwidths.

grid$df

the degrees of freedom of the fitted model.

order

the vector of orders considered.

deg

The degree of the local polynomial.

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. https://doi.org/10.1098/rsta.1994.0094

Fan, J., Yao, Q., & Tong, H. (1996) Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika, 83, 189-206. ttps://doi.org/10.1093/biomet/83.1.189

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. https://doi.org/10.1038/35059003

Loader, C. (1999) Local Regression and Likelihood. Springer, New York. https://doi.org/10.1007/b98858

Examples

   data(plodia)

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

    ## Not run: plot(fit)

    summary(fit)