rspacens {CensSpatial} | R Documentation |
Censored Spatial data simulation
Description
It simulates spatial data with linear structure for one type of censure (left or right).
Usage
rspacens(cov.pars,beta,x=as.matrix(rep(1,n)),coords,kappa=0,cens,n,n1,
cov.model="exponential",cens.type)
Arguments
cov.pars |
covariance structure parameters for the errors distribution ( |
beta |
linear regression parameters. |
x |
design matrix. |
coords |
coordinates of simulated data. |
kappa |
|
cens |
percentage of censure in the data (number between 0 and 1). |
n |
number of simulated data used in estimation. |
n1 |
number of simulated data used for cross validation (Prediction). |
cov.model |
covariance structure for the data (see |
cens.type |
type of censure ( |
Details
This function analyses prediction in spatial data. It returns a spatial dataset for estimation (n length) and a spatial dataset (n1 length) used to evaluate the prediction power of a model through cross validation. The covariance functions used here were provided by cov.spatial from the geoR package.
Value
y |
complete simulated data ( |
datare |
data frame that will be used for the model estimation (coordinates and response). |
valre |
data that will be used for cross validation studies (just response). |
cc |
indicator of censure (1:censored 0:observed). |
cutoff |
limit of detection simulated for censure (left: <=cutoff, right: > cutoff). |
coords1 |
coordinates of value data. |
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
Diggle, P. & Ribeiro, P. (2007). Model-Based Geostatistics. Springer Series in Statistics.
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
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")