Collapse repeated spatial locations into unique locations

In case that several observations are available for a single spatial location find the group means and replicate variability

Usage

Krig.replicates(out = NULL, x, y, XMat = NULL, weights = rep(1,
                 length(y)), digits = 8, verbose = FALSE)

Arguments

out: A list with components x, y, weights, and possibily XMat.
x: Spatial locations.
y: Spatial observations
XMat: Spatial covariates.
weights: Weights proportional to reciprocal varainces of observations.
digits: Number of significant digits to consider in determing a replicate location.
verbose: If TRUE print out details for debugging.

Details

This function figures out which locations are the same and within the function fast.1way use tapply to find replicate group means and standard deviations. NOTE: it is assumed the XMat covariates are unique at the locations. Currently these functions can not handle a model with common spatial locations but different values for the XMat covariates.

Value

A list with components:

yM: Data at unique locations and where more than one observation is available this is the mean of the replicates.
xM: Unique spatial locations.
weightsM: Weights matching the unique lcoations proportional to reciprocal variances This is found as a combination of the original weights at each location.
XMatM: Values of the covariates at the unique lcoations.
uniquerows: Index for unique rows of x.
tauHat.rep, tauHat.pure.error: Standard deviation of pure error estimate based on replicate groups (and adjusting for possibly different weights.)
rep.info: Integer tags indicating replicate groups.

Author

Douglas Nychka

Examples


#create  some spatial replicates
 set.seed( 123)
 x0<- matrix( runif(10*2), 10,2)
 x<-  x0[ c(rep(1,3), 2:8, rep( 9,5),10) , ]
 y<-  rnorm( 16)
 
 out<- Krig.replicates( x=x, y=y)
# compare 
# out$yM[1] ;  mean( y[1:3])
# out$yM[9] ; mean( y[11:15])
# mean( y[ out$rep.info==9])