cltsk: Function calls 'ltsk' using cumulatively expanding time space...

Description Usage Arguments Details Value Author(s) References Examples

View source: R/cltsk.R

Description

Function calls ltsk using cumulatively expanding time space thresholds.

Usage

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cltsk(query, obs, th, nbins, xcoord = "x", ycoord = "y", tcoord = "t", 
	zcoord = "z", vth = NULL, vlen = NULL, llim = c(3, 3), 
	verbose = T, Large = 2000, future=T,cl = NULL)

Arguments

query

data frame containing query point (X,Y,T i.e. XY coordinates and time) where predictions are needed

obs

data frame containing sample data with XY coordinates, time and observed (measured) values

th

a priori chosen distance and time thresholds for neighbor search

nbins

a vector, number of distance and time bins for cumulative neighbor search and kriging.

xcoord

a character constant, the field name for x coordinate in both query and obs

ycoord

a character constant, the field name for y coordinate in both query and obs

tcoord

a character constant, the field name for time coordinate in both query and obs

zcoord

a character constant, the field name for data in obs

vth

thresholds for local spatiotemporal variogram (default 75% of the max lag difference)

vlen

numbers of bins for local spatiotemporal variogram(default, space 15, temporal for each day)

llim

lower limits for number of regions and intervals with observed data to calculate Kriging (default 3 spatial regions, 3 temporal intervals)

verbose

logical, whether print details information

Large

a numeric constant, upper limit of neighbor points, beyond which subsampling is performance

future

logical, whether including observed points in future relative to query points.

cl

a parallel cluster object (default number of cores in the local PC minue one), 0 means single core.

Details

Function performs automatic variogram estimation for each query location using the observed data within th thresholds. The estimated variogram is used for ordinary kriging, but using data in expanding local neighborhoods for ordinary kriging. For example, if predictions are needed at a given location for the past 30 days at an interval of 3 days,data within 3 days are used first, followed by 6 days and so on until data within 30 days. The same applies for distance thresholds.

Value

  1. krig Kriging estimates at each space and time neighborhood

  2. legend The legend for space and time neighborhood

Author(s)

Naresh Kumar (nkumar@med.miami.edu) Dong Liang (dliang@umces.edu)

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

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## load the data
data(ex)
data(epa_cl)
## apply log transformation
obs[,'pr_pm25'] = log(obs[,'pr_pm25'])
## run kriging
system.time(out <- cltsk(ex2.query[1:2,],obs,c(0.10,10),
  zcoord='pr_pm25',nbins=c(4,5),verbose=FALSE,cl=0))
table(out$flag)

ltsk documentation built on Dec. 6, 2019, 1:07 a.m.