I think the below is a solution:
library(raster)
# create some lines
cds1 - rbind(c(-180,-20), c(-140,55), c(10, 0), c(-140,-60))
cds2 - rbind(c(-10,0), c(140,60), c(160,0), c(140,-55))
cds3 - rbind(c(-125,0), c(0,60), c(40,5), c(15,-45))
lns - SpatialLines(list(Lines(list(Line(cds1)), 1),
I have a question regarding kernel density estimation in R. I have a
5-dimensional data, which consists of (x,y,z) locations, time of happening and
size of some events (for example earthquake). I wrote the following code in R
in order to find the 5D kernel density estimation:
library(ks)
Hello,
I'm performing an empirical orthogonal function analysis on sea level
pressure using the spacetime package, and I'm having trouble determining
which elements of the function output I should use to interpret the results.
It seems that most examples in the literature calculate empirical
