| kalman.wge {tswge} | R Documentation |
Kalman filter for simple signal plus noise model
Description
Kalman filter program to predict, filter, and smooth related to the material in Section 10.6 4 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
Usage
kalman.wge(y, start, gam0, F, gamV, G, gamW)Arguments
y |
the univariate data set to be analyzed |
start |
the scalar version of Xo in item (c) following the state equation (10.47) of the text |
gam0 |
the scalar version of Gamma(0) discussed in item (c) following the state equation |
F |
scalar version of the matrix F in the state equation |
gamV |
the value Gamma(v) specified in item (b) following the state equation |
G |
the scalar observation matrix specified in the observation equation as G(t) |
gamW |
the variance of the (univariate) white noise denoted by Gamma(w) in item (c) following (10.48) |
Value
pfs |
a table giving results such as those in Table 10.1 in Woodward, Gray, and Elliott book |
Note
Requires CRAN package 'astsa'
Author(s)
Wayne Woodward
References
Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott
Examples
data(table10.1.signal)
data(table10.1.noise)
spn=table10.1.signal+table10.1.noise
kalman.wge(y=spn,start=0,gam0=1,F=.9,gamV=1,G=1,gamW=.75)