summary.naive {CensSpatial} R Documentation

## Summary of a naive object

### Description

summary method for class "naive".

### Usage

## S3 method for class 'naive'
summary(object,...)


### Arguments

 object object of the class "naive" (see algnaive12 function). ... Additional arguments.

### Value

 mean.str1 Estimates for the mean structure parameters \mathbf{beta} for Naive 1 method. var.str1 Estimates for the variance structure parameters \sigma^2, \phi 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 \sigma^2, \phi for Naive 2 method. predictions1 predictions for Naive 1 method. predictions2 predictions for Naive 1 method.

### 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

Schelin, L. & Sjostedt-de Luna, S. (2014). Spatial prediction in the presence of left-censoring. Computational Statistics and Data Analysis, 74.

SAEMSCL

### Examples

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)


[Package CensSpatial version 3.6 Index]