Package 'RcppCensSpatial'

Covariance matrix for spatial models

Description

It computes the spatial variance-covariance matrix considering exponential, gaussian, matérn, or power exponential correlation function.

Usage

CovMat(phi, tau2, sig2, coords, type = "exponential", kappa = NULL)

Arguments

phi	spatial scaling parameter.
tau2	nugget effect parameter.
sig2	partial sill parameter.
coords	2D spatial coordinates of dimensions $n\times 2$.
type	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.

Details

The spatial covariance matrix is given by

$\Sigma = [Cov(s_i, s_j )] = \sigma^2 R(\phi) + \tau^2 I_n$,

where $\sigma^2 > 0$ is the partial sill, $\phi > 0$ is the spatial scaling parameter, $\tau^2 > 0$ is known as the nugget effect in the geostatistical framework, $R(\phi)$ is the $n\times n$ correlation matrix computed from a correlation function, and $I_n$ is the $n\times n$ identity matrix.

The spatial correlation functions available are:

Exponential:

$Corr(d) = exp(-d/\phi)$,

Gaussian:

$Corr(d) = exp(-(d/\phi)^2)$,

Matérn:

$Corr(d) = \frac{1}{2^{(\kappa-1)}\Gamma(\kappa)}\left(\frac{d}{\phi}\right)^\kappa K_\kappa \left( \frac{d}{\phi} \right)$,

Power exponential:

$Corr(d) = exp(-(d/\phi)^\kappa)$,

where $d \geq 0$ is the Euclidean distance between two observations, $\Gamma(.)$ is the gamma function, $\kappa$ is the smoothness parameter, and $K_\kappa(.)$ is the modified Bessel function of the second kind of order $\kappa$.

Value

An $n\times n$ spatial covariance matrix.

Author(s)

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

See Also

dist2Dmatrix, EM.sclm, MCEM.sclm, SAEM.sclm

Examples

set.seed(1000)
n = 20
coords = round(matrix(runif(2*n, 0, 10), n, 2), 5)
Cov = CovMat(phi=5, tau2=0.8, sig2=2, coords=coords, type="exponential")

---

Distance matrix computation

Description

It computes the Euclidean distance matrix for a set of coordinates.

Usage

dist2Dmatrix(coords)

Arguments

coords

2D spatial coordinates of dimensions $n\times 2$.

Value

An $n\times n$ distance matrix.

Author(s)

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

Examples

n = 100
set.seed(1000)
x = round(runif(n,0,10), 5)     # X coordinate
y = round(runif(n,0,10), 5)     # Y coordinate
Mdist = dist2Dmatrix(cbind(x, y))

---

ML estimation of spatial censored linear models via the EM algorithm

Description

It fits the left, right, or interval spatial censored linear model using the Expectation-Maximization (EM) algorithm. It provides estimates and standard errors of the parameters and supports missing values on the dependent variable.

Usage

EM.sclm(y, x, ci, lcl = NULL, ucl = NULL, coords, phi0, nugget0,
  type = "exponential", kappa = NULL, lower = c(0.01, 0.01),
  upper = c(30, 30), MaxIter = 300, error = 1e-04, show_se = TRUE)

Arguments

y	vector of responses of length $n$.
x	design matrix of dimensions $n\times q$, where $q$ is the number of fixed effects, including the intercept.
ci	vector of censoring indicators of length $n$. For each observation: 1 if censored/missing, 0 otherwise.
lcl, ucl	vectors of length $n$ representing the lower and upper bounds of the interval, which contains the true value of the censored observation. Default =NULL, indicating no-censored data. For each observation: lcl=-Inf and ucl=c (left censoring); lcl=c and ucl=Inf (right censoring); and lcl and ucl must be finite for interval censoring. Moreover, missing data could be defined by setting lcl=-Inf and ucl=Inf.
coords	2D spatial coordinates of dimensions $n\times 2$.
phi0	initial value for the spatial scaling parameter.
nugget0	initial value for the nugget effect parameter.
type	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. See CovMat.
lower, upper	vectors of lower and upper bounds for the optimization method. If unspecified, the default is c(0.01,0.01) for lower and c(30,30) for upper.
MaxIter	maximum number of iterations for the EM algorithm. By default =300.
error	maximum convergence error. By default =1e-4.
show_se	logical. It indicates if the standard errors should be estimated by default =TRUE.

Details

The spatial Gaussian model is given by

$Y = X\beta + \xi$,

where $Y$ is the $n\times 1$ response vector, $X$ is the $n\times q$ design matrix, $\beta$ is the $q\times 1$ vector of regression coefficients to be estimated, and $\xi$ is the error term. Which is normally distributed with zero-mean and covariance matrix $\Sigma=\sigma^2 R(\phi) + \tau^2 I_n$. We assume that $\Sigma$ is non-singular and $X$ has a full rank (Diggle and Ribeiro 2007).

The estimation process is performed via the EM algorithm, initially proposed by Dempster et al. (1977). The conditional expectations are computed using the function meanvarTMD available in the package MomTrunc.

Value

An object of class "sclm". Generic functions print and summary have methods to show the results of the fit. The function plot can extract convergence graphs for the parameter estimates.

Specifically, the following components are returned:

Theta	estimated parameters in all iterations, $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
theta	final estimation of $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
beta	estimated $\beta$.
sigma2	estimated $\sigma^2$.
phi	estimated $\phi$.
tau2	estimated $\tau^2$.
EY	first conditional moment computed in the last iteration.
EYY	second conditional moment computed in the last iteration.
SE	vector of standard errors of $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
InfMat	observed information matrix.
loglik	log-likelihood for the EM method.
AIC	Akaike information criterion.
BIC	Bayesian information criterion.
Iter	number of iterations needed to converge.
time	processing time.
call	RcppCensSpatial call that produced the object.
tab	table of estimates.
critFin	selection criteria.
range	effective range.
ncens	number of censored/missing observations.
MaxIter	maximum number of iterations for the EM algorithm.

Note

The EM final estimates correspond to the estimates obtained at the last iteration of the EM algorithm.

To fit a regression model for non-censored data, just set ci as a vector of zeros.

Author(s)

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

References

Dempster AP, Laird NM, Rubin DB (1977). “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–38.

Diggle P, Ribeiro P (2007). Model-based Geostatistics. Springer.

See Also

MCEM.sclm, SAEM.sclm, predict.sclm

Examples

# Simulated example: 10% of left-censored observations
set.seed(1000)
n = 50   # Test with another values for n
coords = round(matrix(runif(2*n,0,15),n,2), 5)
x = cbind(rnorm(n), runif(n))
data = rCensSp(c(-1,3), 2, 4, 0.5, x, coords, "left", 0.10, 0, "gaussian")

fit = EM.sclm(y=data$y, x=x, ci=data$ci, lcl=data$lcl, ucl=data$ucl,
              coords=coords, phi0=3, nugget0=1, type="gaussian")
fit

---

ML estimation of spatial censored linear models via the MCEM algorithm

Description

It fits the left, right, or interval spatial censored linear model using the Monte Carlo EM (MCEM) algorithm. It provides estimates and standard errors of the parameters and supports missing values on the dependent variable.

Usage

MCEM.sclm(y, x, ci, lcl = NULL, ucl = NULL, coords, phi0, nugget0,
  type = "exponential", kappa = NULL, lower = c(0.01, 0.01),
  upper = c(30, 30), MaxIter = 500, nMin = 20, nMax = 5000,
  error = 1e-04, show_se = TRUE)

Arguments

y	vector of responses of length $n$.
x	design matrix of dimensions $n\times q$, where $q$ is the number of fixed effects, including the intercept.
ci	vector of censoring indicators of length $n$. For each observation: 1 if censored/missing, 0 otherwise.
lcl, ucl	vectors of length $n$ representing the lower and upper bounds of the interval, which contains the true value of the censored observation. Default =NULL, indicating no-censored data. For each observation: lcl=-Inf and ucl=c (left censoring); lcl=c and ucl=Inf (right censoring); and lcl and ucl must be finite for interval censoring. Moreover, missing data could be defined by setting lcl=-Inf and ucl=Inf.
coords	2D spatial coordinates of dimensions $n\times 2$.
phi0	initial value for the spatial scaling parameter.
nugget0	initial value for the nugget effect parameter.
type	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. See CovMat.
lower, upper	vectors of lower and upper bounds for the optimization method. If unspecified, the default is c(0.01,0.01) for lower and c(30,30) for upper.
MaxIter	maximum number of iterations for the MCEM algorithm. By default =500.
nMin	initial sample size for Monte Carlo integration. By default =20.
nMax	maximum sample size for Monte Carlo integration. By default =5000.
error	maximum convergence error. By default =1e-4.
show_se	logical. It indicates if the standard errors should be estimated by default =TRUE.

Details

The spatial Gaussian model is given by

$Y = X\beta + \xi$,

where $Y$ is the $n\times 1$ response vector, $X$ is the $n\times q$ design matrix, $\beta$ is the $q\times 1$ vector of regression coefficients to be estimated, and $\xi$ is the error term. Which is normally distributed with zero-mean and covariance matrix $\Sigma=\sigma^2 R(\phi) + \tau^2 I_n$. We assume that $\Sigma$ is non-singular and $X$ has a full rank (Diggle and Ribeiro 2007).

The estimation process is performed via the MCEM algorithm, initially proposed by Wei and Tanner (1990). The Monte Carlo (MC) approximation starts with a sample of size nMin; at each iteration, the sample size increases (nMax-nMin)/MaxIter, and at the last iteration, the sample size is nMax. The random observations are sampled through the slice sampling algorithm available in package relliptical.

Value

An object of class "sclm". Generic functions print and summary have methods to show the results of the fit. The function plot can extract convergence graphs for the parameter estimates.

Specifically, the following components are returned:

Theta	estimated parameters in all iterations, $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
theta	final estimation of $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
beta	estimated $\beta$.
sigma2	estimated $\sigma^2$.
phi	estimated $\phi$.
tau2	estimated $\tau^2$.
EY	MC approximation of the first conditional moment.
EYY	MC approximation of the second conditional moment.
SE	vector of standard errors of $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
InfMat	observed information matrix.
loglik	log-likelihood for the MCEM method.
AIC	Akaike information criterion.
BIC	Bayesian information criterion.
Iter	number of iterations needed to converge.
time	processing time.
call	RcppCensSpatial call that produced the object.
tab	table of estimates.
critFin	selection criteria.
range	effective range.
ncens	number of censored/missing observations.
MaxIter	maximum number of iterations for the MCEM algorithm.

Note

The MCEM final estimates correspond to the mean of the estimates obtained at each iteration after deleting the half and applying a thinning of 3.

To fit a regression model for non-censored data, just set ci as a vector of zeros.

Author(s)

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

References

Diggle P, Ribeiro P (2007). Model-based Geostatistics. Springer.

Wei G, Tanner M (1990). “A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms.” Journal of the American Statistical Association, 85(411), 699–704. doi:10.1080/01621459.1990.10474930.

See Also

EM.sclm, SAEM.sclm, predict.sclm

Examples

# Example 1: left censoring data
set.seed(1000)
n = 50   # Test with another values for n
coords = round(matrix(runif(2*n,0,15),n,2), 5)
x = cbind(rnorm(n), rnorm(n))
data = rCensSp(c(2,-1), 2, 3, 0.70, x, coords, "left", 0.08, 0, "matern", 1)

fit = MCEM.sclm(y=data$y, x=x, ci=data$ci, lcl=data$lcl, ucl=data$ucl,
                coords, phi0=2.50, nugget0=0.75, type="matern",
                kappa=1, MaxIter=30, nMax=1000)
fit$tab

# Example 2: left censoring and missing data
yMiss = data$y
yMiss[20] = NA
ci = data$ci
ci[20] = 1
ucl = data$ucl
ucl[20] = Inf

fit1 = MCEM.sclm(y=yMiss, x=x, ci=ci, lcl=data$lcl, ucl=ucl, coords,
                 phi0=2.50, nugget0=0.75, type="matern", kappa=1,
                 MaxIter=300, nMax=1000)
summary(fit1)
plot(fit1)

---

TCDD concentration data

Description

The level of dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin or TCDD) data was collected in November 1983 by the U.S. Environmental Protection Agency (EPA) in several areas of a highway in Missouri, USA. The TCDD measurement was subject to a limit of detection (cens); thereby, the TCDD data is left-censored. Only the locations used in the geostatistical analysis by Zirschky and Harris (1986) are shown.

Usage

data("Missouri")

Format

A data frame with 127 observations and five variables:

xcoord

x coordinate of the start of each transect (ft).

ycoord

y coordinate of the start of each transect (ft).

TCDD

TCDD concentrations (mg/kg).

transect

transect length (ft).

cens

indicator of censoring (left-censored observations).

Source

Zirschky JH, Harris DJ (1986). “Geostatistical analysis of hazardous waste site data.” Journal of Environmental Engineering, 112(4), 770–784.

See Also

EM.sclm, MCEM.sclm, SAEM.sclm

Examples

data("Missouri")
y = log(Missouri$TCDD)
cc = Missouri$cens
coord = cbind(Missouri$xcoord/100, Missouri$ycoord)
x = matrix(1, length(y), 1)
lcl = rep(-Inf, length(y))
ucl = y

## SAEM fit
set.seed(83789)
fit1 = SAEM.sclm(y, x, cc, lcl, ucl, coord, 5, 1, lower=c(1e-5,1e-5),
                 upper=c(50,50))
fit1$tab

## MCEM fit
fit2 = MCEM.sclm(y, x, cc, lcl, ucl, coord, 5, 1, lower=c(1e-5,1e-5),
                 upper=c(50,50), MaxIter=300, nMax=1000)
fit2$tab

## Imputed values
cbind(fit1$EY, fit2$EY)[cc==1,]

---

Prediction in spatial models with censored/missing responses

Description

It performs spatial prediction in a set of new S spatial locations.

Usage

## S3 method for class 'sclm'
predict(object, locPre, xPre, ...)

Arguments

object	object of class 'sclm' given as output of EM.sclm, MCEM.sclm, or SAEM.sclm function.
locPre	matrix of coordinates for which prediction is performed.
xPre	matrix of covariates for which prediction is performed.
...	further arguments passed to or from other methods.

Details

This function predicts using the mean squared error (MSE) criterion, which takes the conditional expectation E(Y|X) as the best linear predictor.

Value

The function returns a list with:

coord	matrix of coordinates.
predValues	predicted values.
sdPred	predicted standard deviations.

Author(s)

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

See Also

EM.sclm, MCEM.sclm, SAEM.sclm

Examples

set.seed(1000)
n = 120
coords = round(matrix(runif(2*n,0,15),n,2), 5)
x = cbind(rbinom(n,1,0.50), rnorm(n), rnorm(n))
data = rCensSp(c(1,4,-1), 2, 3, 0.50, x, coords, "left", 0.10, 20)

## Estimation
data1 = data$Data

# Estimation: EM algorithm
fit1 = EM.sclm(y=data1$y, x=data1$x, ci=data1$ci, lcl=data1$lcl,
               ucl=data1$ucl, coords=data1$coords, phi0=2.50, nugget0=1)

# Estimation: SAEM algorithm
fit2 = SAEM.sclm(y=data1$y, x=data1$x, ci=data1$ci, lcl=data1$lcl,
                 ucl=data1$ucl, coords=data1$coords, phi0=2.50, nugget0=1)

# Estimation: MCEM algorithm
fit3 = MCEM.sclm(y=data1$y, x=data1$x, ci=data1$ci, lcl=data1$lcl,
                 ucl=data1$ucl, coords=data1$coords, phi0=2.50, nugget0=1,
                 MaxIter=300)
cbind(fit1$theta, fit2$theta, fit3$theta)

# Prediction
data2 = data$TestData
pred1 = predict(fit1, data2$coords, data2$x)
pred2 = predict(fit2, data2$coords, data2$x)
pred3 = predict(fit3, data2$coords, data2$x)

# Cross-validation
mean((data2$y - pred1$predValues)^2)
mean((data2$y - pred2$predValues)^2)
mean((data2$y - pred3$predValues)^2)

---

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

---

ML estimation of spatial censored linear models via the SAEM algorithm

Description

It fits the left, right, or interval spatial censored linear model using the Stochastic Approximation EM (SAEM) algorithm. It provides estimates and standard errors of the parameters and supports missing values on the dependent variable.

Usage

SAEM.sclm(y, x, ci, lcl = NULL, ucl = NULL, coords, phi0, nugget0,
  type = "exponential", kappa = NULL, lower = c(0.01, 0.01),
  upper = c(30, 30), MaxIter = 300, M = 20, pc = 0.2, error = 1e-04,
  show_se = TRUE)

Arguments

y	vector of responses of length $n$.
x	design matrix of dimensions $n\times q$, where $q$ is the number of fixed effects, including the intercept.
ci	vector of censoring indicators of length $n$. For each observation: 1 if censored/missing, 0 otherwise.
lcl, ucl	vectors of length $n$ representing the lower and upper bounds of the interval, which contains the true value of the censored observation. Default =NULL, indicating no-censored data. For each observation: lcl=-Inf and ucl=c (left censoring); lcl=c and ucl=Inf (right censoring); and lcl and ucl must be finite for interval censoring. Moreover, missing data could be defined by setting lcl=-Inf and ucl=Inf.
coords	2D spatial coordinates of dimensions $n\times 2$.
phi0	initial value for the spatial scaling parameter.
nugget0	initial value for the nugget effect parameter.
type	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. See CovMat.
lower, upper	vectors of lower and upper bounds for the optimization method. If unspecified, the default is c(0.01,0.01) for lower and c(30,30) for upper.
MaxIter	maximum number of iterations of the SAEM algorithm. By default =300.
M	number of Monte Carlo samples for stochastic approximation. By default =20.
pc	percentage of initial iterations of the SAEM algorithm with no memory. It is recommended that 50<MaxIter*pc<100. By default =0.20.
error	maximum convergence error. By default =1e-4.
show_se	logical. It indicates if the standard errors should be estimated by default =TRUE.

Details

The spatial Gaussian model is given by

$Y = X\beta + \xi$,

where $Y$ is the $n\times 1$ response vector, $X$ is the $n\times q$ design matrix, $\beta$ is the $q\times 1$ vector of regression coefficients to be estimated, and $\xi$ is the error term which is normally distributed with zero-mean and covariance matrix $\Sigma=\sigma^2 R(\phi) + \tau^2 I_n$. We assume that $\Sigma$ is non-singular and $X$ has full rank (Diggle and Ribeiro 2007).

The estimation process is performed via the SAEM algorithm, initially proposed by Delyon et al. (1999). The spatial censored (SAEM) algorithm was previously proposed by Lachos et al. (2017) and Ordoñez et al. (2018) and is available in the package CensSpatial. These packages differ in the random number generation and optimization procedure.

This model is also a particular case of the spatio-temporal model defined by Valeriano et al. (2021) when the number of temporal observations is equal to one. The computing codes of the spatio-temporal SAEM algorithm are available in the package StempCens.

Value

An object of class "sclm". Generic functions print and summary have methods to show the results of the fit. The function plot can extract convergence graphs for the parameter estimates.

Specifically, the following components are returned:

Theta	estimated parameters in all iterations, $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
theta	final estimation of $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
beta	estimated $\beta$.
sigma2	estimated $\sigma^2$.
phi	estimated $\phi$.
tau2	estimated $\tau^2$.
EY	stochastic approximation of the first conditional moment.
EYY	stochastic approximation of the second conditional moment.
SE	vector of standard errors of $\theta = (\beta, \sigma^2, \phi, \tau^2)$.
InfMat	observed information matrix.
loglik	log-likelihood for the SAEM method.
AIC	Akaike information criterion.
BIC	Bayesian information criterion.
Iter	number of iterations needed to converge.
time	processing time.
call	RcppCensSpatial call that produced the object.
tab	table of estimates.
critFin	selection criteria.
range	effective range.
ncens	number of censored/missing observations.
MaxIter	maximum number of iterations for the SAEM algorithm.

Note

The SAEM final estimates correspond to the estimates obtained at the last iteration of the algorithm.

To fit a regression model for non-censored data, just set ci as a vector of zeros.

Author(s)

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

References

Delyon B, Lavielle M, Moulines E (1999). “Convergence of a stochastic approximation version of the EM algorithm.” The Annals of Statistics, 27(1), 94–128.

Diggle P, Ribeiro P (2007). Model-based Geostatistics. Springer.

Lachos VH, Matos LA, Barbosa TS, Garay AM, Dey DK (2017). “Influence diagnostics in spatial models with censored response.” Environmetrics, 28(7).

Ordoñez JA, Bandyopadhyay D, Lachos VH, Cabral CRB (2018). “Geostatistical estimation and prediction for censored responses.” Spatial Statistics, 23, 109–123. doi:10.1016/j.spasta.2017.12.001.

Valeriano KL, Lachos VH, Prates MO, Matos LA (2021). “Likelihood-based inference for spatiotemporal data with censored and missing responses.” Environmetrics, 32(3).

See Also

EM.sclm, MCEM.sclm, predict.sclm

Examples

# Example 1: 8% of right-censored observations
set.seed(1000)
n = 50   # Test with another values for n
coords = round(matrix(runif(2*n,0,15),n,2), 5)
x = cbind(rnorm(n), rnorm(n))
data = rCensSp(c(4,-2), 1, 3, 0.50, x, coords, "right", 0.08)

fit = SAEM.sclm(y=data$y, x=x, ci=data$ci, lcl=data$lcl, ucl=data$ucl,
                coords, phi0=2, nugget0=1, type="exponential", M=10,
                pc=0.18)
fit

# Example 2: censored and missing observations
set.seed(123)
n = 200
coords = round(matrix(runif(2*n,0,20),n,2), 5)
x = cbind(runif(n), rnorm(n), rexp(n))
data = rCensSp(c(1,4,-1), 2, 3, 0.50, x, coords, "left", 0.05, 0,
               "matern", 3)
data$y[c(10,120)] = NA
data$ci[c(10,120)] = 1
data$ucl[c(10,120)] = Inf

fit2 = SAEM.sclm(y=data$y, x=x, ci=data$ci, lcl=data$lcl, ucl=data$ucl,
                 coords, phi0=2, nugget0=1, type="matern", kappa=3,
                 M=10, pc=0.18)
fit2$tab
plot(fit2)

---