Supplement. Demonstration of Kalman filtering smoothing and confidence interval calibration.

File List kf_algorithm.R Description This file contains R-code which demonstrates the Kalman filter and smoother and construction of CIC curves. No estimation of parameters is shown, however, the negative log-likelihood is calculated and so this code could be modified to be used for mMaximum-likelihood estimation of parameters. Note that estimation routines used in practice are best coded in a compiled language (e.g., C/Fortran) for optimal speed. Modifications to the code below will allow for parameter estimation, but it is likely to be quite slow. The function sim.track generates some simulated data from a bivariate random walk. For simplicity the simulation gives a regular time series of positions. Also demonstrated is the construction of confidence interval calibration (CIC) curves. In this case because the correct parameter values are used the CIC curve is the best possible case. By tinkering with the parameter values in the error covariance, the user will be able to see how the CIC curves change. To run the code open an R session and source the code then run the kfdemo function: > source('kfalgorithm.R') > kfdemo()