问题
I was wondering if anyone knows how to assign marks in spatstat so that they tend to cluster spatially? I have a set of lat long coordinates that I want to categorize into 4 groups. I have figured out how to randomly assign marks/groups to these points using the following code:
as.ppp(data, window ,marks=factor(sample(1:4,replace=TRUE)))
But I can't figure out how to assign the marks so that groups tend to occupy points closer to one another. As a further complication, I would also like the number of points within each group to be the same, specified number each time. Does anyone have any leads? Thanks in advance!
回答1:
Typically in spatstat we define models which describe/generate points at random locations and possibly with random marks. If I understand you correctly you have a fixed set of locations and you simply want to assign random marks. How many points do you have? If you don't have too many points a simple suggestion could be to generate a multivariate normally distributed variable and then take the n_1 lowest values for the first mark, the n_2 next values for the second mark, and so on. A simple example with 4 equal sized groups of points:
library(spatstat)
library(mvtnorm)
set.seed(42) # Make reproducible
X <- redwood # Example data
n <- npoints(redwood)
Xdist <- pairdist(X) # n x n matrix of distances in X
decay_rate <- 1 # Parameter for covariance sturcture
sigma <- exp(-decay_rate * Xdist)
m <- rmvnorm(1, rep(0, n), sigma)
breaks <- quantile(m, probs = c(0, .25, .5, .75, 1)) # breaks to cut marks in four equal sized groups
marks(X) <- cut(m, breaks = breaks, include.lowest=TRUE, labels = 1:4)
plot(X)
来源：https://stackoverflow.com/questions/39398911/generate-clustered-spatstat-marks