调用rpoisspp(I)生成泊松点模式报错，非均匀点模式聚类检测求助

Hey there! Let’s work through this issue you’re facing with rpoisspp(I) when simulating nonhomogeneous Poisson processes after estimating intensity with density.ppp. I’ve run into similar snags before, so here are some practical steps to diagnose and fix the problem:

1. Verify the Intensity Object I #

• First, confirm I is a valid im class object (the standard output from density.ppp). Run class(I)—if it’s not im, your density estimation might have gone wrong. Double-check the arguments you passed to density.ppp (e.g., did you accidentally pass a point pattern instead of using the result of density.ppp?).
• Check for non-positive or NA values in the intensity surface:
  • Run range(I) to ensure all values are positive (since Poisson processes require positive intensity).
  • Run sum(is.na(I$v)) to check for missing values. If you find NA/negative values, this is almost certainly the issue. Try adjusting the bandwidth in density.ppp—use sigma = bw.diggle(your_point_pattern) to let spatstat select an optimal bandwidth, which often fixes boundary-related NA/negative values.

2. Inspect the Intensity Window #

• Plot the intensity surface with plot(I) to visualize it. Look for weird artifacts (like empty regions, extreme spikes, or a malformed window). The window associated with I (I$window) must be a valid owin object—run is.owin(I$window) to confirm. If the window is invalid, re-estimate the density using the original point pattern’s window explicitly (e.g., density.ppp(your_ppp, window=your_ppp$window)).

3. Update or Reinstall Spatstat #

• Older versions of spatstat have known bugs with rpoisspp and certain im objects. Run update.packages("spatstat") to get the latest stable version. If updating doesn’t help, try a fresh install:

  remove.packages("spatstat")
  install.packages("spatstat")
  

4. Test with a Minimal Example #

• Isolate the problem by testing rpoisspp with a simple, manually defined intensity surface. For example:

  # Create a simple positive intensity surface
  test_intensity <- im(function(x, y) 100 * exp(-(x^2 + y^2)), owin(c(-1,1), c(-1,1)))
  # Try simulating
  test_ppp <- rpoisspp(test_intensity)
  

  If this works, the issue is specific to your estimated intensity I—go back to step 1 to debug your density estimation. If this fails, there’s a problem with your spatstat installation or R environment.

5. Dig Into the Error Details #

• If you haven’t already, run traceback() immediately after getting the error. This will show you the exact line in spatstat’s code where the failure occurs, which can pinpoint the root cause (e.g., a check for positive intensity failing, or a window validation error). Sharing the full error message and traceback would help narrow things down even further if these steps don’t resolve it.

内容的提问来源于stack exchange，提问作者Harikesh Wong