xKsmooth1 function

Kalman Filter and Smoother - This script has been superseded by Ksmooth

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 NOTE: This script has been superseded by Ksmooth.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

xKsmooth1(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

• xs: state smoothers

• Ps: smoother mean square error

• x0n: initial mean smoother

• P0n: initial smoother covariance

• J0: initial value of the J matrix

• 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

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NOTE: This script has been superseded by Ksmooth