summary.SAEMSpatialCens | R Documentation |
summary
method for class "SAEMSpatialCens".
## S3 method for class 'SAEMSpatialCens' summary(object,...)
object |
object of the class "SAEMSpatialCens" (see |
... |
Additional arguments. |
mean.str |
Estimates for the mean structure parameters \mathbf{beta} for SAEMSCL method. |
var.str |
Estimates for the variance structure parameters σ^2, φ for SAEMSCL method. |
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>>
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
n<-200 ### sample size for estimation. n1=50 ### 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) coords1=coords[1:n,] type="matern" #xtot<-cbind(1,runif((n+n1)),runif((n+n1),2,3)) xtot=as.matrix(rep(1,(n+n1))) xobs=xtot[1:n,] beta=5 #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] est=SAEMSCL(cc,y,cens.type="left",trend="cte",coords=coords, kappa=1.2,M=15,perc=0.25,MaxIter=10,pc=0.2,cov.model=type, fix.nugget=TRUE,nugget=0,inits.sigmae=cov.ini[1], inits.phi=cov.ini[2],search=TRUE,lower=0.00001,upper=100) summary(est)
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