Hey there, let's tackle this memory allocation error you're hitting when running 1000 kriging simulations with gstat. That "cannot allocate vector of size 1.1 Mb" message might seem odd at first, but when you multiply thousands of simulations across a grid of points, the in-memory data piles up fast. Here's how you can leverage the ff package to fix this, plus some other practical optimizations:

Good news: gstat has native compatibility with ff objects for simulation outputs. By adding the ff = TRUE parameter to your krige() call, you'll store the simulation results in disk-backed ff vectors instead of loading everything into RAM. This keeps your memory usage low while still letting you work with the results like regular data.

Here's your adjusted code:

library(sp)
library(gstat)
library(ff)

data(meuse)
coordinates(meuse) = ~x+y
data(meuse.grid)
gridded(meuse.grid) = ~x+y
m <- vgm(.59, "Sph", 874, .04)

# Run simulations with ff-backed output to avoid memory overload
x <- krige(log(zinc)~1, meuse, meuse.grid, model = m, nsim=1000, ff = TRUE)

When you use ff=TRUE, columns like sim1 through sim1000 are stored as ff objects. You can still access individual values or subsets (e.g., x@data$sim1[1:10]) without loading the entire dataset into memory.

Before diving into ff, trim down your input data's memory usage to give yourself more breathing room:

• Drop unused columns: If meuse.grid has columns you don't need for the simulation, remove them first with meuse.grid@data <- meuse.grid@data[, c("x", "y")] (adjust based on your actual needs)
• Check data types: Ensure your coordinate and response variables are stored as numeric (not integer or other heavier types). For example, convert with meuse$x <- as.numeric(meuse$x) if needed.

If even ff-backed simulations are pushing your limits, split the 1000 simulations into smaller batches. Process each batch separately, save it to disk, then combine the results later. This way, you never have all 1000 simulations in memory at once:

# Define batch settings
total_sims <- 1000
batch_size <- 100
n_batches <- total_sims / batch_size

# Store each batch's results here
sim_batches <- list()

for (batch_num in 1:n_batches) {
  # Run one batch of simulations
  current_batch <- krige(log(zinc)~1, meuse, meuse.grid, model = m, nsim=batch_size, ff = TRUE)
  
  # Rename columns to avoid duplicate "sim1" labels across batches
  sim_col_indices <- grep("^sim", colnames(current_batch@data))
  new_col_names <- paste0("sim", ((batch_num - 1)*batch_size + 1):(batch_num*batch_size))
  colnames(current_batch@data)[sim_col_indices] <- new_col_names
  
  sim_batches[[batch_num]] <- current_batch@data
}

# Combine all batches into a single ff-backed data frame
combined_sims <- do.call(cbind, sim_batches)

# Attach the combined simulations back to the grid spatial object
final_sim_output <- meuse.grid
final_sim_output@data <- combined_sims

ff stores data in temporary files on your disk. When you're finished analyzing the simulations, clean up these files to free up space:

# Loop through all simulation columns and delete their ff files
for (col in grep("^sim", colnames(final_sim_output@data), value = TRUE)) {
  delete(final_sim_output@data[[col]])
}

内容的提问来源于stack exchange，提问作者Mohammad