Krig.replicates {fields} | R Documentation |
In case that several observations are available for a single spatial location find the group means and replicate variability
Krig.replicates(out, x, y, Z, weights=rep( 1, length(y)), verbose = FALSE)
out |
A list with components |
x |
Spatial locations. |
y |
Spatial observations |
Z |
Spatial covariates. |
weights |
Weights proportional to reciprocal varainces of observations. |
verbose |
If TRUE print out details for debugging. |
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 Z covariates are unique at the locations. Currently these functions
can not handle a model with common spatial locations but different values for the Z covariates.
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. |
ZM |
Values of the covariates at the unique lcoations. |
uniquerows |
Index for unique rows of |
shat.rep, shat.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. |
Douglas Nychka
#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])