residuals.dlmFiltered {dlm} | R Documentation |
The function computes one-step forecast errors for a filtered dynamic linear model.
## S3 method for class 'dlmFiltered' residuals(object, ..., type = c("standardized", "raw"), sd = TRUE)
object |
an object of class |
... |
unused additional arguments. |
type |
should standardized or raw forecast errors be produced? |
sd |
when |
A vector or matrix (in the multivariate case) of one-step forecast
errors, standardized if type = "standardized"
. Time series
attributes of the original observation vector (matrix) are retained by
the one-step forecast errors.
If sd = TRUE
then the returned value is a list with the
one-step forecast errors in component res
and the corresponding
standard deviations in component sd
.
The object
argument must include a component y
containing the data. This component will not be present if
object
was obtained by calling dlmFilter
with
simplify = TRUE
.
Giovanni Petris GPetris@uark.edu
Giovanni Petris (2010), An R Package for Dynamic Linear
Models. Journal of Statistical Software, 36(12), 1-16.
http://www.jstatsoft.org/v36/i12/.
Petris, Petrone, and Campagnoli, Dynamic Linear Models with
R, Springer (2009).
West and Harrison, Bayesian forecasting and
dynamic models (2nd ed.), Springer (1997).
## diagnostic plots nileMod <- dlmModPoly(1, dV = 15100, dW = 1468) nileFilt <- dlmFilter(Nile, nileMod) res <- residuals(nileFilt, sd=FALSE) qqnorm(res) tsdiag(nileFilt)