ltsk (version 1.1.2)

ltsk.cv: Local Time and Space Kriging Cross Validation, n-Fold or Leave-one-out

Description

Cross validation functions for local time space kriging

Usage

ltsk.cv(nfold, obs, th, nbins, part=NULL,zcoord = "z",...)

Value

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.

Arguments

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 nfold; if NULL, it was sampled with replacement from seq(1,nfold) of length nrow(obs)

zcoord

character constant, the field name for data in obs

...

other arguments that will be passed to cltsk

Author

Naresh Kumar ([e-mail])

Dong Liang ([e-mail])

Details

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.

References

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.

Examples

## 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)