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 SAEMSCL function).

### 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.

SAEMSCL

### 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)



[Package CensSpatial version 3.6 Index]