control: Parameters for Predict and Numerical Optimization in Kalman...
In cts: Continuous Time Autoregressive Models

Description Usage Arguments Details Value See Also

Setup in the predict and numerical optimization in the Kalman filter algorithm.

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car_control(fty=1, n.ahead=10, trace=FALSE, ari=TRUE, vri=FALSE, vr=0, pfi="MAPS",
ccv="CTES", lpv=TRUE, scc=TRUE,  nit=40, opm=1, rgm=1, req=0.5, con=1.0e-5, rpe=1.0, 
ivl=1.0e-2, fac=1.0e1, stl=1.0e-5, sml=1.0e2, gtl=1.0e5, kst=TRUE, fct=TRUE)

`fty`	`fty=1` forecast past the end. `fty=2` forecast last L-steps. `fty=3` forecast last L-steps updated (filtering)types. See also `fct`. If `fct=TRUE`, all time series is used to fit the model for `fty=1 or 3`. If `fty=2`, only the first (length of time - n.ahead) is used to fit the model. Thus, only the first (length of time - n.head) prediction values are the same for `fty=1 or 3` vs `fty=2`. See also `pre2, prv2`.
`n.ahead`	number of steps ahead at which to predict.
`trace`	a logical value triggering printout of information during the fitting process, and major results for the fitted model.
`ari`	`ari=TRUE`: parameter starting values. `ari=FALSE`: they are taken as zero. This is obsolete.
`vri`	`vri=FALSE`, observation noise not included in the model. `vri=TRUE`, observation noise included
`vr`	0.5, initial value of observation noise ratio: only if `vri=TRUE`
`pfi`	always use the option pfi="MAPS".
`ccv`	`ccv="CTES"` for constant term estimation. `ccv="MNCT"` if mean correction, `ccv=NULL` if omitted.
`lpv`	`lpv=TRUE` always use this option.
`scc`	`scc=TRUE` always use this option.
`nit`	number of iteations.
`opm`	`opm=1` always use this.
`rgm`	`rgm=1` always use this.
`req`	root equality switch value.
`con`	convergence criterion.
`rpe`	relative size of parameter perturbations.
`ivl`	initial value of step size constraint parameter.
`fac`	step size constraint modification parameter. This value may be setup to `fac=5` for better convergency.
`stl`	typical smallest step size parameter.
`sml`	typical small step size parametrr.
`gtl`	typical greatest step size parameter.
`kst`	`kst=TRUE` to save estimated states.
`fct`	`fct=TRUE` to use all time series to fit the model.

Objects returned by this function specify predict and numerical optimization parameters of the Kalman filter algorithms implemented in car, (via the ctrl argument).

An object of class car_control, a list.

car for the usage

cts documentation built on May 2, 2019, 2:46 a.m.