# seminaive_1: Seminaive algorithm for spatial censored prediction. In CensSpatial: Censored Spatial Models

## Description

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

## Usage

 ```1 2``` ```Seminaive(data, y.col, coords.col, covar, covar.col, copred,cov.model = "exponential", thetaini, fix.nugget = T, nugget,kappa = 0, cons, MaxIter, cc, 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 σ^2 and φ values in the covariance structure. `fix.nugget` (logical) it indicates if the τ^2 parameter must be fixed. `nugget` (numeric) values of the τ^2 parameter, if `fix.nugget=F`, this value corresponds to an initial value. `kappa` value of κ 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) `trend` it specifies the mean part of the model. See documentation of `trend.spatial` from `geoR` for further details. By default `"cte"`.

## 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 (β,σ^2,φ,τ^2). `predictions` Predictions obtained for the seminaive algorithm. `sdpred` Standard deviations of predictions. `loglik` log likelihood from the estimated model.

## Author(s)

Alejandro Ordonez <<[email protected]>>, Victor H. Lachos <<[email protected]>> and Christian E. Galarza <<[email protected]>>

Maintainer: Alejandro Ordonez <<[email protected]>>

## References

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

`SAEMSCL`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32``` ```## Not run: 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] ###seminaive algorithm r=Seminaive(data=data2,y.col=3,covar=T,coords.col=1:2,covar.col=4:5,cov.model="matern", thetaini=c(.1,.2),fix.nugget=T,nugget=0,kappa=1.5,cons=c(0.1,2,0.5),MaxIter=100, cc=obj\$cc,copred=obj\$coords1,trend=~V4+V5) ## End(Not run) ```