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plot.iNEXT <- function (x, type = 1, se = TRUE, show.legend = TRUE, show.main = TRUE, 
  col = NULL, xlab = "", ylab = "", ...) 
{
  if (class(x) != "iNEXT") 
    stop("invalid object class")
  TYPE <- c(1, 2, 3)
  SPLIT <- c("none", "order", "site", "both")
  if (is.na(pmatch(type, TYPE)) | pmatch(type, TYPE) == -1) 
    stop("invalid plot type")
  type <- pmatch(type, 1:3)

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Species	SPAD502	SPAD502plus
Psychotria rubra	50.4	49
Psychotria rubra	46.8	49.5
Psychotria rubra	54.9	56.6
Solanum diphyllum	65.6	63.6
Litsea sp.	45.6	42.7
Mallotus paniculatus	51.6	52.4
Machilus zuihoensis	43.8	44.1
Machilus zuihoensis	46.8	47.6
Ficus ampelas	54.9	56.9

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	species	WSG_obs	WSG_data	source
1	Acacia	0.539	0.9	a
2	Adansonia	0.21	0.5	b
3	Albizia	0.581	0.69	a
4	Azadirachta	0.521	0.7	a
5	Bauhinia	0.469	0.619	c
6	Bombax	0.335	0.27	d
7	Bridelia	0.557	0.62	e
8	Butea	0.377	0.465	c
9	Cassia	0.665	0.746	c

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# Mia Momberg - reanalysis of JEcol dataset

# importing libraries
library (readxl)
library (tidyverse)
library (modEvA)
library (car)

# importing data from Dryad repository
url <- 'https://datadryad.org/stash/downloads/file_stream/573456'

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# Pseudoreplication issue

# 1600 quadrats sampled in 8 grids (each grid 200 quadrats)
# what happens if we consider quadrats as independent units?

DEP <- rnorm (1600)
EXP <- rnorm (1600)
GRID <- rep (1:8, each = 200)
boxplot (DEP ~ GRID)

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# Diversity along elevation gradient - pseudoreplication issue

# Elevation 200-1000 m asl, five elevation zones
# Richness (number of species) between 1-100 species

seed <- 56654

par (mfrow = c(2,2))

# Scenario 1: 5 replicate at each zone, each replicate at different mountain (random diversity)

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# Euclidean vs Hellinger vs Bray-Curtis distances and double zero problem
library (vegan)
library (pals)
vltava.spe <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/vltava-spe.txt', row.names = 1)

prop_dz <- function (com)
  (nrow (com)-designdist (x = com, method = "A+B-J", terms = "binary"))/nrow (com)  # calculates proportion of double zeros

D_eucl <- dist (vltava.spe, method = 'euclidean')
D_hell <- dist (decostand (vltava.spe, method = 'hell'), method = 'euclidean')

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# Hellinger distance

library (vegan)
png (width = 6, height = 6, units = 'in', res = 300)
par (mar = c(5,5,2,2))
plot (0:1, 0:1, xlab = list ('Species 1', cex = 1.5), ylab = list ('Species 2', cex = 1.5), axes = F, xlim = c(0, 1.3), ylim = c(0,1.3), type = 'n')
par (xpd = NA)
arrows (x0 = 0, x1 = 1.3, y0 = 0, y1 = 0)
arrows (x0 = 0, x1 = 0, y0 = 0, y1 = 1.3)

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# Individual examples
# 1) Both w1 and w2 similarly related to y
set.seed (1234)
# Generate two random variables N(0,1)
x1 <- rnorm (100, 0, 1)
x2 <- rnorm (100, 0, 1)
X <- cbind (x1, x2)
# Generate correlation matrix to make variables correlated at r = 0.8
R <- matrix (c(sqrt (.8), sqrt (.2), sqrt (0.2), sqrt (.8)), ncol = 2)
W <- X%*%R

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scale_override <- function(which, scale) {
  if(!is.numeric(which) || (length(which) != 1) || (which %% 1 != 0)) {
    stop("which must be an integer of length 1")
  }
  
  if(is.null(scale$aesthetics) || !any(c("x", "y") %in% scale$aesthetics)) {
    stop("scale must be an x or y position scale")
  }
  
  structure(list(which = which, scale = scale), class = "scale_override")