astsa: Kfilter1 – R documentation

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Kfilter1

Kalman Filter - Model may be time varying or have inputs

Returns both the predicted and filtered values for a linear state space model. Also evaluates the likelihood at the given parameter values.

Kfilter1(num, y, A, mu0, Sigma0, Phi, Ups, Gam, cQ, cR, input)

`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
`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
`input`	matrix or vector of inputs having the same row dimension as y; use `input = 0` if not needed

`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
`innov`	innovation series
`sig`	innovation covariances
`Kn`	last value of the gain, needed for smoothing

D.S. Stoffer

See also https://www.stat.pitt.edu/stoffer/tsa4/chap6.htm for an explanation of the difference between levels 0, 1, and 2.

Applied Statistical Time Series Analysis

v1.12

GPL-3

Authors

David Stoffer

Initial release

2020-12-20