Before computing transformation matrix

... it might be good to compute a trend surface of the square differences between the two sets of coordinates, at the sites of the good coordinates.

## continued from above example:
## compute square difference:
c$sqdiff <- with(c, sqrt((nx-x)^2 + (ny-y)^2))

##
library(gstat)

##
coordinates(c) <- c('nx', 'ny')
coordinates(d) <- c('x', 'y')
gridded(d) <- TRUE\
x <- gstat(sqdiff ~ 1, data=c, degree=2)
x.trend <- predict(x, newdata=d)
spplot(x.trend, "var1.pred")

Reply

The content of this field is kept private and will not be shown publicly.
  • Allowed HTML tags: <div> <img> <a> <em> <strong> <cite> <code> <ul> <ol> <li> <dl> <dt> <dd>
  • Lines and paragraphs break automatically.

More information about formatting options

CAPTCHA
This question is for testing whether you are a human visitor and to prevent automated spam submissions.
Image CAPTCHA
Copy the characters (respecting upper/lower case) from the image.