tswge (version 2.1.0)

kalman.miss.wge: Kalman filter for simple signal plus noise model with missing data

Description

Kalman function to predict, filter, and smooth in the presence of missing data; see Section 10.6 4 in Applied Time Series Analysis with R

Usage

kalman.miss.wge(y,start, gam0, F, gamV, Gtmiss, gamW)

Value

pfs

a table giving results such as those in Table 10.1 in Woodward, Gray, and Elliott book

Arguments

y

the univariate data set to be analyzed

start

the scalar version of X(0) 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

Gtmiss

specifies which items that are missing

gamW

the variance of the (univariate) white noise denoted by Gamma(w) in item (c) following (10.48)

Author

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
n=75
Gtmiss=array(1,dim=c(1,1,n))
Gtmiss[1,1,2]=0
Gtmiss[1,1,5]=0
kalman.miss.wge(y=spn,start=0,gam0=1,F=.9,gamV=1,Gtmiss,gamW=.75)