predgraphics {CensSpatial} R Documentation

## Prediction graphics for SAEM Algortihm for censored spatial data.

### Description

This function provides prediction raster graphics representation and its standard deviation.

### Usage

predgraphics(xpred = NULL, grid1, est, points = TRUE,obspoints = 1:sum(est$cc == 0), colors = terrain.colors(100),sdgraph = TRUE,xlab="X Coord",ylab="Y Coord", main1="Predicted response", main2="Standard deviation predicted", xlim=c(min(est$coords[,1]),max(est$coords[,1])),ylim=c(min(est$coords[,2]),

### Value

in addition to the raster graphics for prediction, the next values are retorned:

 datapred data.frame with the coordinates and the predicted points used in the prediction raster graphic. datasdpred data.frame with the coordinates and the standard deviation predicted points used in the standard deviation prediction raster graphic.

### 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 ofa 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



data(depth)
cc=depth$cc y=depth$depth
coords=depth[,1:2]

cov.ini=c(1500,30)
est=SAEMSCL(cc,y,cens.type="left",trend="cte",coords=coords,M=15,perc=0.25,
MaxIter=100,pc=0.2,cov.model="gaussian",fix.nugget=FALSE,nugget=10,
inits.sigmae=cov.ini[2],inits.phi=cov.ini[1], search=TRUE,lower=c(0.00001,0.00001),
upper=c(10000,100))

coorgra1=seq(min(coords[,1]),max(coords[,1]),length=50)
coorgra2=seq(min(coords[,2]),max(coords[,2]),length=50)

grid1=expand.grid(x=coorgra1,y=coorgra2)
xpred=rep(1,2500)

predgraphics(xpred=xpred,est=est,grid1=grid1,points=TRUE,sdgraph=TRUE)



[Package CensSpatial version 3.6 Index]