Stem.Simulation: Simulation of spatio-temporal data

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

The function Stem.Simulation simulates spatio-temporal data.

Usage

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Stem.Simulation(StemModel)

Arguments

StemModel

an object of class “Stem.Model” given as output by the Stem.Model function.

Details

Note that the values contained in StemModel$skeleton$phi are used as the true values of the parameters.

Value

The functions return a n * d matrix of data.

Author(s)

Michela Cameletti [email protected]

References

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.

See Also

pm10 and Stem.Model

Examples

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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)

Example output

Loading required package: mvtnorm
Loading required package: MASS
[1] "coords"     "covariates" "z"         
[1] "Stem.Model"
[1] TRUE

Stem documentation built on May 29, 2017, 11:12 p.m.