View overlap.vs.cor.R
library(entropy) | |
library(ggplot2) | |
olaps <- function(sp1, sp2){ | |
sp1 <- sp1/sum(sp1) | |
sp2 <- sp2/sum(sp2) | |
plot.table <- data.frame(species = c(rep("sp1", length(sp1)), rep("sp2", length(sp2))), | |
env = c(seq(1:length(sp1)), seq(1:length(sp2))), |
View thin.max.R
# Function to rarefy point data in any number of dimensions. The goal here is to | |
# take a large data set and reduce it in size in such a way as to approximately maximize the | |
# difference between points. For instance, if you have 2000 points but suspect a lot of | |
# spatial autocorrelation between them, you can pass in your data frame, the names (or indices) | |
# of the lat/lon columns, and the number 200, and you get back 200 points from your original data | |
# set that are chosen to be as different from each other as possible given a randomly chosen | |
# starting point | |
# Input is: | |
# |