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 ( |
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 |
thetaini |
initial values for the |
fix.nugget |
(logical) it indicates if the |
nugget |
(numeric) values of the |
kappa |
value of |
cons |
(vector) vector containing the |
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: <cutoff) |
trend |
it specifies the mean part of the model. See documentation of |
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 ( |
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.
See Also
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)