predSCL {CensSpatial} | R Documentation |
Prediction for the SAEM algorithm for censored spatial data.
Description
This function uses the parameters estimates from SAEM to predict values at unknown locations through the MSE criterion assuming normal distribution.
Usage
predSCL(xpred, coordspred, est)
Arguments
xpred |
values of the x design matrix for prediction coordinates. |
coordspred |
points coordinates to be predicted. |
est |
object of the class SAEMSpatialCens (see |
Details
This function predicts using the Mean Square of error (MSE) criterion, that is, it takes the conditional
expectation E(Y|X)
as the predictor that minimizes the MSE.
Value
prediction |
prediction value. |
indpred |
indicator for the observed and predicted values (0:observed,1:predicted). |
sdpred |
standard deviation for prediction. |
coordspred |
points coordinates predicted. |
coordsobs |
observed coordinates. |
Author(s)
Alejandro Ordonez <<ordonezjosealejandro@gmail.com>>, Victor H. Lachos <<hlachos@ime.unicamp.br>> and Christian E. Galarza <<cgalarza88@gmail.com>>
Maintainer: Alejandro Ordonez <<ordonezjosealejandro@gmail.com>>
References
DELYON, B., LAVIELLE, M.,ANDMOULI NES, E. (1999). Convergence of a stochastic approximation version of the EM algorithm.Annals of Statistic-s27, 1, 94-128.
Diggle, P. & Ribeiro, P. (2007). Model-Based Geostatistics. Springer Series in Statistics.
See Also
Examples
n<-200 ### sample size for estimation.
n1=100 ### number of observation used in the prediction.
###simulated coordinates
r1=sample(seq(1,30,length=400),n+n1)
r2=sample(seq(1,30,length=400),n+n1)
coords=cbind(r1,r2)### coordinates for estimation and prediction.
coords1=coords[1:n,]####coordinates used in estimation.
cov.ini=c(0.2,0.1)###initial values for phi and sigma2.
type="matern"
xtot<-cbind(1,runif((n+n1)),runif((n+n1),2,3))###X matrix for estimation and prediction.
xobs=xtot[1:n,]###X matrix for estimation.
beta=c(5,3,1)
###simulated data
obj=rspacens(cov.pars=c(3,.3,0),beta=beta,x=xtot,coords=coords,kappa=1.2,cens=0.25,
n=(n+n1),n1=n1,cov.model=type,cens.type="left")
data2=obj$datare
cc=obj$cc
y=obj$datare[,3]
coords=obj$datare[,1:2]
#######SAEMSpatialCens object########
est=SAEMSCL(cc,y,cens.type="left",trend="other",x=xobs,coords=coords,kappa=1.2,M=15,
perc=0.25,MaxIter=10,pc=0.2,cov.model="exponential",fix.nugget=TRUE,nugget=0,
inits.sigmae=cov.ini[2],inits.phi=cov.ini[1],search=TRUE,lower=0.00001,upper=50)
coordspred=obj$coords1
xpred=xtot[(n+1):(n+n1),]
h=predSCL(xpred,coordspred,est)