Seminaive {CensSpatial} R Documentation

## Seminaive algorithm for spatial censored prediction.

### Description

This function executes the seminaive algorithm proposed by Schelin et al. (2014)

### Usage

Seminaive(data, y.col, coords.col, covar, covar.col, copred,cov.model = "exponential",
thetaini, fix.nugget = TRUE, nugget,kappa = 0, cons, MaxIter, cc, cutoff, trend)


### Arguments

 data data.frame containing the coordinates, covariates and response variable. y.col (numeric) column of data.frame that corresponds to the response variable. coords.col (numeric) columns of data.frame that corresponds to the coordinates of the spatial data. covar (logical) indicates the presence of covariates in the spatial censored estimation (FALSE: without covariates, TRUE: with covariates). covar.col (numeric) columns of data.frame that corresponds to the covariates in the spatial censored linear model estimation. copred coordinates used in the prediction procedure. cov.model covariance model in the structure of covariance (see cov.spatial from geoR). thetaini initial values for the \sigma^2 and \phi values in the covariance structure. fix.nugget (logical) it indicates if the \tau^2 parameter must be fixed. nugget (numeric) values of the \tau^2 parameter, if fix.nugget=F, this value corresponds to an initial value. kappa value of \kappa involved in some covariance functions. cons (vector) vector containing the (c_1,c_2,c_3) constants used in the convergence criterion for the algorithm (see Schedlin). MaxIter maximum of iterations for the algorithm. cc (binary vector) indicator of censure (1: censored, 0: observed) cutoff (vector) limit of detection for censure ( rc: >cutoff, lc:

### Details

This function estimates and computes predictions following Schedlin et al. (2014). See reference.

### Value

 zk vector with observed and estimate censored observations by kriging prediction. AIC AIC of the estimated model. BIC BIC of the estimated model. beta beta parameter for the mean structure. theta vector of estimate parameters for the mean and covariance structure (\beta,\sigma^2,\phi,\tau^2). predictions Predictions obtained for the seminaive algorithm. sdpred Standard deviations of predictions. loglik log likelihood from the estimated model.

### 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 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]))

###seminaive algorithm
r=Seminaive(data=data2,y.col=3,covar=TRUE,coords.col=1:2,covar.col=4:5,cov.model="matern",
thetaini=c(.1,.2),fix.nugget=TRUE,nugget=0,kappa=1.5,cons=c(0.1,2,0.5),MaxIter=100,
cc=obj$cc,cutoff=cutoff,copred=obj$coords1,trend=~V4+V5)

summary(r)



[Package CensSpatial version 3.6 Index]