xKsmooth2: Kalman Filter and Smoother - This script has been superseded...
In astsa: Applied Statistical Time Series Analysis

xKsmooth2

R Documentation

Kalman Filter and Smoother - This script has been superseded by `Ksmooth`

Description

Returns the filtered and smoothed values for the state-space model. This is the smoother companion to Kfilter2. NOTE: This script has been superseded by Ksmooth. Note that scripts starting with an x are scheduled to be phased out.

Usage

xKsmooth2(num, y, A, mu0, Sigma0, Phi, Ups, Gam, Theta, cQ, cR, 
          S, input)

Arguments

`num`	number of observations
`y`	data matrix, vector or time series
`A`	time-varying observation matrix, an array with `dim=c(q,p,n)`
`mu0`	initial state mean
`Sigma0`	initial state covariance matrix
`Phi`	state transition matrix
`Ups`	state input matrix; use `Ups = 0` if not needed
`Gam`	observation input matrix; use `Gam = 0` if not needed
`Theta`	state error pre-matrix
`cQ`	Cholesky-type decomposition of state error covariance matrix Q – see details below
`cR`	Cholesky-type decomposition of observation error covariance matrix R – see details below
`S`	covariance matrix of state and observation errors
`input`	matrix or vector of inputs having the same row dimension as y; use `input = 0` if not needed

Details

NOTE: This script has been superseded by Ksmooth

Value

`xs`	state smoothers
`Ps`	smoother mean square error
`J`	the J matrices
`xp`	one-step-ahead prediction of the state
`Pp`	mean square prediction error
`xf`	filter value of the state
`Pf`	mean square filter error
`like`	the negative of the log likelihood
`Kn`	last value of the gain