xKfilter1 function

Kalman Filter - This script has been superseded by Kfilter.

Returns both the predicted and filtered values for a linear state space model. Also evaluates the likelihood at the given parameter values. NOTE: This script has been superseded by Kfilter. Note that scripts starting with an x are scheduled to be phased out.

xKfilter1(num, y, A, mu0, Sigma0, Phi, Ups, Gam, cQ, cR, 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
• 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

Returns

• 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

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

Author(s)

D.S. Stoffer

Details

NOTE: This script has been superseded by Kfilter