metrics<-function(z){Q75=quantile(z,probs=c(0.75))

aboveq75=sum(z>Q75)

dh75=aboveq75/length(z)

return(dh75)}

DH75<-grid_metrics(lid,~metrics(Z),res = 30)

#dependencies
library(lidR)
library(ggplot2)
library(stats)

#specify file path
infile <- 'path_to_your_cloud.las'

#read in las cloud
las <- readLAS(infile)

#create density raster
density_plot <- grid_density(las, res = 10)

#calculate 75th percentile point density
Q75 = quantile(density_plot$point_density, probs = 0.75)

#filter raster based on value
density_plot_Q75 <- density_plot[density_plot$point_density > Q75]

#plot unfiltered raster
ggplot() +
  geom_raster(data = density_plot , aes(x = X, y = Y, fill = point_density)) +
  coord_quickmap()

#plot filtered raster
ggplot() +
  geom_raster(data = density_plot_Q75 , aes(x = X, y = Y, fill = point_density)) +
  coord_quickmap()