nlts (version 1.0-2)

lin.order.cls: The order of a time series using cross-validation of the linear autoregressive model (conditional least-squares).

Description

A function to estimate the order of a time series using cross-validation of the linear autoregressive model. Coefficients are estimated using conditional least-squares. I coded this functions to estimate the order of ecological time series. Bjornstad et al. (1998, 2001)

Usage

lin.order.cls(x, order = 1:5, n.cond = 5, echo = TRUE)

Arguments

x

A time series without missing values

order

The candidate orders. The default is 1:5

n.cond

The number of observation to condition on. The default is 5 (must be >= max(order))

echo

if TRUE a counter for the data points and the orders is produced to monitor progress.

Value

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

order

the grid of orders considered.

CVd

the cross-validation errors across the grid of orders.

Details

The time series is normalized prior to cross-validation.

Note that if the dynamics is highly nonlinear, the nonparametric order-estimators (ll.order) may be more appropriate. (I coded this function to use for comparison with the nonparametric methods, because these also uses (nonlinear) conditional least-squares.)

References

Bjornstad, O.N., Begon, M., Stenseth, N. C., Falck, W., Sait, S. M. and Thompson, D. J. 1998. Population dynamics of the Indian meal moth: demographic stochasticity and delayed regulatory mechanisms. Journal of Animal Ecology 67:110-126. https://doi.org/10.1046/j.1365-2656.1998.00168.x 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 401: 1001-1006. https://doi.org/10.1038/35059003

See Also

ll.order

Examples

# NOT RUN {
    data(plodia)
    fit <- lin.order.cls(sqrt(plodia), order=1:5)
    
# }
# NOT RUN {
plot(fit)
# }
# NOT RUN {
    summary(fit)
# }