summary.naive | R Documentation |
summary
method for class "naive".
## S3 method for class 'naive' summary(object,...)
object |
object of the class "naive" (see |
... |
Additional arguments. |
mean.str1 |
Estimates for the mean structure parameters \mathbf{beta} for Naive 1 method. |
var.str1 |
Estimates for the variance structure parameters σ^2, φ for Naive 1 method. |
mean.str2 |
Estimates for the mean structure parameters \mathbf{beta} for Naive 2 method. |
var.str2 |
Estimates for the variance structure parameters σ^2, φ for Naive 2 method. |
predictions1 |
predictions for Naive 1 method. |
predictions2 |
predictions for Naive 1 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>>
Schelin, L. & Sjostedt-de Luna, S. (2014). Spatial prediction in the presence of left-censoring. Computational Statistics and Data Analysis, 74.
SAEMSCL
n<-200 ### sample size for estimation. n1=100 ### number of observation used for prediction. ###simulated coordinates 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)### total coordinates (used in estimation and prediction). coords1=coords[1:n,]####coordinates used for estimation. type="matern"### covariance structure. 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. ###simulated data obj=rspacens(cov.pars=c(3,.3,0),beta=c(5,3,1),x=xtot,coords=coords,kappa=1.2, cens=0.25,n=(n+n1),n1=n1,cov.model=type,cens.type="left") data2=obj$datare data2[,4:5]=xobs[,-1] cc=obj$cc y=obj$datare[,3] cutoff=rep(obj$cutoff,length(y[cc==1])) aux2=algnaive12(data=data2,cc=obj$cc,covar=TRUE,covar.col=4:5, copred=obj$coords1,thetaini=c(.1,.2),y.col=3,coords.col=1:2, fix.nugget=TRUE,nugget=0,kappa=1.2,cutoff=cutoff,trend=~V4+V5, cov.model=type) summary(aux2)
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