地理加权回归(GWR)带宽校准命令无报错却未生成对象求助

Let’s break down why your gwr.sel command is finishing instantly without creating the bw object—here are the most likely culprits and fixes:

• Your sample size is way too small for adaptive bandwidth
  Looking at your map object, you only have 10 spatial points. Adaptive bandwidth (adapt=T) in GWR needs enough data to calculate meaningful local neighborhood sizes for each point; with just 10 observations, the algorithm can’t compute valid weights and likely bails silently. GWR typically requires at least 30-50 points for stable bandwidth calibration, and adaptive mode needs even more. If you previously ran this code with a larger dataset, that’s exactly why it worked before!

• Check for problematic variable types or sparse categories
  Take a close look at your factor variables:

  • PestContro has a category "900" with only 1 observation, and diss has "0" with just 1 point. Sparse factor levels can break local regression calculations.
  • Double-check if variables like PestContro should actually be numeric instead of factors—if they represent continuous values (like pesticide control intensity), converting them with as.numeric(as.character(map$PestContro)) might help.
  • Run summary(map@data) to rule out missing values (even if gwr.sel drops NA rows, losing more points from an already small dataset will kill the process).

• Test with fixed bandwidth first to isolate the issue
  Switch to fixed bandwidth mode to see if the function works at all:

  bw_fixed <- gwr.sel(
    T_cub ~ diss + V_code + PestContro + elevation + Shape_Area + gadash + mataim + plot_p_a + total_spra,
    data = map,
    adapt = FALSE,
    gweight = gwr.Gauss,
    verbose = TRUE
  )
  

  If this returns a bandwidth value, the problem is definitely tied to adaptive bandwidth and your tiny sample size. If it still fails, move on to checking your model and environment.

• Verify package version and model validity

  • Since this worked before, did you update the spgwr package recently? Run packageVersion("spgwr") to check, and try reinstalling the package with install.packages("spgwr") (restart R afterward) to roll back or fix any version-related bugs.
  • First confirm your model works with ordinary linear regression:

    lm_test <- lm(T_cub ~ diss + V_code + PestContro + elevation + Shape_Area + gadash + mataim + plot_p_a + total_spra, data = map@data)
    summary(lm_test)
    

    If this throws errors, you’ve found the root cause (e.g., perfect multicollinearity, invalid variable types).

• Quick sanity check for spatial data
  Run plot(map) to visualize your points—make sure there are no extreme outliers in coordinates that might break distance calculations. While you said this worked before, it’s worth ruling out accidental data corruption.

内容的提问来源于stack exchange，提问作者Helena Furman