dzexpm: The estimation and prediction function for the skewed spatial...
In DZEXPM: Estimation and Prediction of Skewed Spatial Processes

Description Usage Arguments Details Value Author(s) References Examples

This function provides a way to combine the estimation and prediction procedure for the skewed spatial processes.

1	dzexpm(y_ful, x_ful, n_ful, n, u1, u2, theta, p, iter, loopiter)

`y_ful`	the full response variable of a dataset
`x_ful`	the full covariates of a dataset
`n_ful`	the full number of the spatial locations in a dataset
`n`	the number of spatial locations being employed to estimate and evaluate the spatial process
`u1`	horizontal coordinate of the spatial locations
`u2`	vertical coordinate of the spatial locations
`p`	is a useful parameter to capture the "skewness" of the process, which belongs in (0,1)
`theta`	the range parameter of the covariance kernel in the spatial process
`iter`	the MCMC iterations to update the parameters
`loopiter`	the burn in size

This function is generated according to the spatial model, Double Zero Expectile Normal Process with measurement error, which has good prediction performance. See the references for details.

A date.frame is returned which includes quantities "DIC", "coverage", "bias.med", "mpe.med" and "SD.med", where "DIC" stands for the Deviance Information Criterion (DIC), see Gelman et al. (2004), and "coverage", "bias.med", "mpe.med" and "SD.med" represent the median of prediction coverage, bias, mean prediction error and standard error over MCMC scheme iterations, respectively, for the predictive performance of the skewed spatial process.

Jiangyan Wang, Miao Yang and Anandamayee Majumdar

Wang, J., Yang, M. and Majumdar, A. (2018). Comparative Study and Sensitivity Analysis of Skewed Spatial Processes. Computational Statistics, 33,75-98.

Gelman, A., Carlin, J., Stern, H. and Rubin, D. (2004). Bayesian Data Analysis: Second Edition. Texts in Statistical Science. CRC Press. ISBN 1-58488-388-X. LCCN 2003051474. MR 2027492.

  real<- as.matrix(do.call(cbind, MaxTemp03))
  n_ful<- nrow(real)
  y_ful<- real[,2]
  x_ful<- real[,7]
  x_ful<- log(x_ful)
  x_ful<- scale(x_ful)
  u1<- real[,3]
  u2<- real[,4]
  theta<- 2/150000
  n<- 56
  p<- 0.1
 dzexpm(y_ful, x_ful, n_ful, n, u1, u2, theta, p, 80, 20)