lin.order.cls {nlts}

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.

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.)

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.

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

    data(plodia)
    fit <- lin.order.cls(sqrt(plodia), order=1:5)
    ## Not run: plot(fit)
    summary(fit)