simulate.STmodel {SpatioTemporal} | R Documentation |
Data is simulated for the space-time locations in object
using the
parameters in x
.
## S3 method for class 'STmodel' simulate(object, nsim = 1, seed = NULL, x, nugget.unobs = 0, ...)
object |
A |
nsim |
Number of replicates to simulate. |
seed |
if !=NULL used in a call to |
x |
Parameters to use when simulating the data; both regression and
covariance parameters must be given, see |
nugget.unobs |
Value of nugget at unonserved locations, either a scalar or a vector with one element per unobserved site. |
... |
Additional parameters for |
A list containing:
param |
Parameters used in the simulation, i.e. |
B |
The simulated beta fields in a (number of locations) - by - (number of temporal trends) - by - (number of replicates) array. |
X |
The simulated spatio-temporal fields in a (number of timepoints) - by - (number of locations) - by - (number of replicates) array. Row and column names indicate the time and locations for each point. |
obs |
A list with one element per replicate, containing the simulated
observations extracted at space-time locations matching those in
|
Johan Lindstrom
Other STmodel methods: MCMC.STmodel
,
c.STmodel
, createSTmodel
,
estimate.STmodel
,
estimateCV.STmodel
,
plot.STdata
, predict.STmodel
,
print.STmodel
,
print.summary.STmodel
,
qqnorm.predCVSTmodel
,
scatterPlot.predCVSTmodel
,
summary.STmodel
##load the data data(mesa.model) data(est.mesa.model) ##Get estimated parameters x <- coef(est.mesa.model)$par ##Simulate 5 replicates from these parameters sim.data <- simulate(mesa.model, nsim=5, x=x) ##compute average beta fields beta <- calc.mu.B(mesa.model$LUR, loglikeSTgetPars(x, mesa.model)$alpha) ##plot the simulated observations as a function of time par(mfrow=c(2,2), mar=c(4,4,.5,.5)) plot(sim.data$obs[[1]]$date, sim.data$obs[[1]]$obs, type="n", ylab="obs", xlab="Date") for(i in 1:5){ points(sim.data$obs[[i]]$date, sim.data$obs[[i]]$obs, col=i) } ##and the latent beta-fields for(i in 1:3){ plot(sim.data$B[,i,1], ylim=range(sim.data$B[,i,]), type="n", xlab="loc", ylab=paste("beta",colnames(sim.data$B)[i])) for(j in 1:5){ points(sim.data$B[,i,j], col=j) } lines( beta[,i], col="grey") }