Stem.Simulation {Stem} | R Documentation |
The function Stem.Simulation
simulates spatio-temporal data.
Stem.Simulation(StemModel)
StemModel |
an object of class “Stem.Model” given as output by the Stem.Model function. |
Note that the values contained in StemModel$skeleton$phi
are used as the true values of the parameters.
The functions return a n * d matrix of data.
Michela Cameletti michela.cameletti@unibg.it
Amisigo, B.A., Van De Giesen, N.C. (2005) Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series. Hydrology and Earth System Sciences 9, 209–224.
Fasso', A., Cameletti, M., Nicolis, O. (2007) Air quality monitoring using heterogeneous networks. Environmetrics 18, 245–264.
Fasso', A., Cameletti, M. (2007) A general spatio-temporal model for environmental data. Tech.rep. n.27 Graspa - The Italian Group of Environmental Statistics - http://www.graspa.org .
Fasso', A., Cameletti, M. (2009) A unified statistical approach for simulation, modelling, analysis and mapping of environmental data. Accepted for publication by Simulation: transaction of the Society for Modeling and Simulation International.
Mc Lachlan, G.J., Krishnan, T. (1997) The EM Algorithm and Extensions. Wiley, New York.
Shumway, R.H., Stoffer, D.S. (2006) Time Series Analysis and Its Applications: with R Examples. Springer, New York.
Xu, K., Wikle, C.K. (2007) Estimation of parameterized spatio-temporal dynamic models. Journal of Statistical Inference and Planning 137, 567–588.
pm10
and Stem.Model
data(pm10) names(pm10) #extract the data coordinates <- pm10$coords covariates <- pm10$covariates z <- pm10$z #build the parameter list phi <- list(beta=matrix(c(3.65,0.046,-0.904),3,1), sigma2eps=0.1, sigma2omega=0.2, theta=0.01, G=matrix(0.77,1,1), Sigmaeta=matrix(0.3,1,1), m0=as.matrix(0), C0=as.matrix(1)) K <-matrix(1,ncol(z),1) mod1 <- Stem.Model(z=z,covariates=covariates, coordinates=coordinates,phi=phi,K=K) class(mod1) is.Stem.Model(mod1) simulateddata = Stem.Simulation(mod1)