Description Usage Arguments Details Value Author(s) References Examples

Cross validation functions for local time space kriging

1 |

`nfold` |
integer, apply n-fold cross validation; if larger than number of observed data, apply leave-one-out cross validation |

`obs` |
data frame containing spatiotemporal locations and observed data |

`th` |
vector of length two; a priori chosen distance threshold and time lag for neighbor search |

`nbins` |
vector of length two; a priori chosen bins to divide distance threshold and time lag equally |

`part` |
vector of random digits between 1 and |

`zcoord` |
character constant, the field name for data in |

`...` |
other arguments that will be passed to |

Leave-one-out cross validation visits a data point, and predicts the value at that location by leaving out the observed value, and proceeds with the next data point. N-fold cross validation makes a partitions the data set in N parts. For all observations in a part, predictions are made based on the remaining N-1 parts; this is repeated for each of the N parts.

a matrix of the cross validation residual, each column corresponds to a given distance threshold and time lag; a data frame containing the summary statistics of the cross validation residuals, including number of non-missing kriging, the sum of square prediction errors and the mean square prediction errors. Each individual row is a combination of distance threshold and time lag.

Naresh Kumar (NKumar@med.miami.edu)

Dong Liang (dliang@umces.edu)

Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.

Kumar, N., et al. (2013). "Satellite-based PM concentrations and their application to COPD in Cleveland, OH." Journal of Exposure Science and Environmental Epidemiology 23(6): 637-646.

Liang, D. and N. Kumar (2013). "Time-space Kriging to address the spatiotemporal misalignment in the large datasets." Atmospheric Environment 72: 60-69.

1 2 3 4 5 6 7 8 9 | ```
## load the data
set.seed(123)
data(epa_cl)
ii= with(obs,which(amonth==5 & aday <13)) ## first week of Januray 2005;
x=obs[sample(ii,400),]
## apply log transformation
x[,'pr_pm25'] = log(x[,'pr_pm25'])
## run kriging
out <- ltsk.cv(nfold=10,obs=x,th=c(0.10,10),nbins=c(2,2),zcoord='pr_pm25',verbose=FALSE,cl=0)
``` |

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