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

In BivinSadler/tswge: Time Series for Data Science

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)

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)

Value

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

Note

Calls Ksmooth1 in 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
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)

BivinSadler/tswge documentation built on Sept. 15, 2023, 4:07 p.m.