lmenssp-package: Linear Mixed Effects Models with Non-stationary Stochastic Processes

Description

Obtains maximum likelihood estimates of the model parameters, filters, smooths and forecasts random components of the model for the following processes: 1) Brownian motion, 2) integrated Brownian motion, 3) integrated Ornstein-Uhlenbeck process, 4) stationary process with powered correlation function, 5) stationary process with Matern correlation function, under multivariate normal and t response distributions. It also contains miscellaneous functions for diagnostic checks, boostrap standard error calculation, etc.

Arguments

Details

Package:

lmenssp

Type:

Package

Version:

1.2

Date:

2016-07-23

License:

GPL (>=2)

References

Asar O, Ritchie J, Kalra P, Diggle PJ (2015) Acute kidney injury amongst chronic kidney disease patients: a case-study in statistical modelling. To be submitted.

Diggle PJ (1988) An approach to the analysis of repeated measurements. Biometrics, 44, 959-971.

Diggle PJ, Heagerty PJ, Liang K-Y, Zeger SL. (2002) Analysis of Longitudinal Data, 2nd edition. Oxford University Press: Oxford.

Diggle PJ, Ribeiro PJ Jr. (2007) Model-based Geostatistics. Springer-Verlag: New York.

Diggle PJ, Sousa I, Asar O (2015) Real time monitoring of progression towards renal failure in primary care patients. Biostatistics, 16(3), 522-536.

Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics, 38, 134-147.

Matern B. (1960) Spatial Variation. Statens Skogsforsningsinstitut, Stockholm.

Pinheiro JC, Liu C, Wu YN. (2001) Efficient algorithms for robust estimation in linear mixed-effects models using the multivariate t distribution. Journal of Computational and Graphical Statistics 10, 249-276.

Ross SM (1996) Stochastic processes. John Wiley & Sons, New Jersey.

Taylor JMG, Cumberland WG, Sy JP (1994) A Stochastic Model for Analysis of Longitudinal AIDS Data. Journal of the American Statistical Association, 89, 727-736.