rCensSp: Censored spatial data simulation

In RcppCensSpatial: Spatial Estimation and Prediction for Censored/Missing Responses

Censored spatial data simulation

Description

It simulates censored spatial data with a linear structure for an established censoring rate.

Usage

rCensSp(beta, sigma2, phi, nugget, x, coords, cens = "left", pcens = 0.1,
  npred = 0, cov.model = "exponential", kappa = NULL)

Arguments

beta	linear regression parameters.
sigma2	partial sill parameter.
phi	spatial scaling parameter.
nugget	nugget effect parameter.
x	design matrix of dimensions n\times q.
coords	2D spatial coordinates of dimensions n\times 2.
cens	'left' or 'right' censoring. By default ='left'.
pcens	desired censoring rate. By default =0.10.
npred	number of simulated data used for cross-validation (Prediction). By default =0.
cov.model	type of spatial correlation function: 'exponential', 'gaussian', 'matern', and 'pow.exp' for exponential, gaussian, matérn, and power exponential, respectively.
kappa	parameter for some spatial correlation functions. For exponential and gaussian kappa=NULL, for power exponential 0 < kappa <= 2, and for matérn correlation function kappa > 0.

Value

If npred > 0, it returns two lists: Data and TestData; otherwise, it returns a list with the simulated data.

Data

y	response vector.
ci	censoring indicator.
lcl	lower censoring bound.
ucl	upper censoring bound.
coords	coordinates matrix.
x	design matrix.

TestData

y	response vector.
coords	coordinates matrix.
x	design matrix.

Author(s)

Katherine L. Valeriano, Alejandro Ordoñez, Christian E. Galarza, and Larissa A. Matos.

Examples

n = 100
set.seed(1000)
coords = round(matrix(runif(2*n,0,15),n,2), 5)
x = cbind(1, rnorm(n))
data = rCensSp(beta=c(5,2), sigma2=2, phi=4, nugget=0.70, x=x,
               coords=coords, cens="left", pcens=0.10, npred=10,
               cov.model="gaussian")
data$Data
data$TestData

RcppCensSpatial documentation built on June 28, 2022, 1:07 a.m.