Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates the standardized (or auxilliary) residuals sensu Harvey, Koopman and Penzer (1998).
1 2 |
object |
An object of class |
... |
Not used. |
Uses the algorithm on page 112 of Harvey, Koopman and Penzer (1998) to compute the standardized model residuals.
A list with the following components
model.residuals |
The smoothed model residuals y(t)-E(y(t)|y(1:T),Theta), where Theta is the set of model parameters. Sometimes called the smoothations. This is different than the Kalman filter innovations which are y(t)-E(y(t)|y(1:t-1),Theta). |
state.residuals |
The smoothed stated residuals E(x(t)|y(1:T))-E(x(t)|E(x(t-1)|y(1:T))). |
residuals |
The model residuals as a (n+m) x TT matrix with |
var.residuals |
The variance of the model residuals as a (n+m) x (n+m) x TT matrix. This is var(hat( |
std.residuals |
The standardized model residuals as a (n+m) x TT matrix. This is |
Eli Holmes, NOAA, Seattle, USA.
eli(dot)holmes(at)noaa(dot)gov
Harvey, A., S. J. Koopman, and J. Penzer. 1998. Messy time series: a unified approach. Advances in Econometrics 13: 103-144 (see page 112).
Koopman, S. J., N. Shephard, and J. A. Doornik. 1999. Statistical algorithms for models in state space using SsfPack 2.2. Econometrics Journal 2: 113-166. (see pages 147-148).
1 2 3 4 5 6 7 8 9 | dat = t(harborSeal)
dat = dat[c(2,11),]
MLEobj = MARSS(dat)
#not standardized model residuals
residuals(MLEobj)$model.residuals
#standardized (by variance) model & state residuals
residuals(MLEobj)$std.residuals
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