Krig.replicates {fields} R Documentation

## Collapse repeated spatial locations into unique locations

### Description

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

### Usage

```Krig.replicates(out, x, y, Z, weights=rep( 1, length(y)), verbose = FALSE)
```

### Arguments

 `out` A list with components `x`, `y`, `weights`, and possibily `Z`. `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.

### 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 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.

### 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. `ZM` Values of the covariates at the unique lcoations. `uniquerows` Index for unique rows of `x`. `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

### 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])

```

[Package fields version 8.4-1 Index]