# Source additional functions that will be used later in this
# Chapter. Our scripts assume that files to be read are in
# the working directory.
source("hcoplot.R")
source("triplot.rda.R")
source("plot.lda.R")
source("polyvars.R")
source("screestick.R")
# Load the Doubs data. The file Doubs.Rdata is assumed to be in
# the working directory
load("Doubs.RData")
# Remove empty site 8
spe <- spe[-8, ]
env <- env[-8, ]
spa <- spa[-8, ]
# Set aside the variable 'dfs' (distance from the source) for
# later use
dfs <- env[, 1]
# Remove the 'dfs' variable from the env data frame
env2 <- env[, -1]
# Recode the slope variable (slo) into a factor (qualitative)
# variable to show how these are handled in the ordinations
slo2 <- rep(".very_steep", nrow(env))
slo2[env$slo <= quantile(env$slo)[4]] <- ".steep"
slo2[env$slo <= quantile(env$slo)[3]] <- ".moderate"
slo2[env$slo <= quantile(env$slo)[2]] <- ".low"
slo2 <- factor(slo2,
levels = c(".low", ".moderate", ".steep", ".very_steep"))
table(slo2)
# Create an env3 data frame with slope as a qualitative variable
env3 <- env2
env3$slo <- slo2
# Subset 1: Physiography (upstream-downstream gradient)
envtopo <- env2[, c(1 : 3)]
names(envtopo)
# Subset 2: Water quality
envchem <- env2[, c(4 : 10)]
names(envchem)
# Hellinger-transform the species data set
spe.hel <- decostand(spe, "hellinger")
# Create two subsets of explanatory variables
6.3 Redundancy Analysis (RDA)
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