CrossStempCens {StempCens} | R Documentation |
Cross-Validation in spatio-temporal model with censored/missing responses
Description
This function performs cross-validation in spatio-temporal model with censored/missing responses, which measure the performance of the predictive model on new test dataset. The cross-validation method for assessing the model performance is validation set approach (or data split).
Usage
CrossStempCens(Pred.StempCens, yObs.pre)
Arguments
Pred.StempCens |
an object of class |
yObs.pre |
a vector of the observed responses, the test data. |
Value
Bias |
bias prediction error. |
Mspe |
mean squared prediction error. |
Rmspe |
root mean squared prediction error. |
Mae |
mean absolute error. |
Author(s)
Katherine L. Valeriano, Victor H. Lachos and Larissa A. Matos
See Also
Examples
set.seed(400)
# Parameter values
beta <- c(-1,1.50)
phi <- 5
rho <- 0.6
tau2 <- 0.80
sigma2 <- 2
# Coordinates and covariates
coords <- matrix(round(runif(14, 0, 10), 9), ncol=2) # Coordinates without repetitions
time <- as.matrix(seq(1, 5)) # Time index without repetitions
x <- cbind(rexp(35, 2), rnorm(35, 2, 1))
# Data
data <- rnStempCens(x, time, coords, beta, phi, rho, tau2, sigma2,
type.S="gaussian", kappa=0, cens="left", pcens=0.2)
# Splitting the dataset
train <- data[1:32,]
test <- data[33:35,]
# Estimation
x <- cbind(train$x1, train$x2)
cc <- train$ci
est_teste <- EstStempCens(y=train$yObs, x, cc=train$ci, time=train$time, coord=train[, 1:2],
LI=train$lcl, LS=train$ucl, init.phi=3.5, init.rho=0.5,
init.tau2=1, type.Data="unbalanced", method="nlminb", kappa=0,
type.S="gaussian", IMatrix=TRUE, M=20, perc=0.25,
MaxIter=300, pc=0.20)
# Prediction
xPre <- cbind(test$x1, test$x2)
pre_teste <- PredStempCens(est_teste, test[,1:2], test$time, xPre)
class(pre_teste)
# Cross-validation
cross_teste <- CrossStempCens(pre_teste, test$yObs)
cross_teste$Mspe # MSPE
[Package StempCens version 1.2.0 Index]