# Load the data
# 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, ]
## Q-mode dissimilarity and distance measures for
## (semi-)quantitative data
# Percentage difference (aka Bray-Curtis) dissimilarity matrix
# on raw species data
spe.db <- vegdist(spe) # method = "bray" (default)
head(spe.db)
# Percentage difference (aka Bray-Curtis) dissimilarity matrix
# on log-transformed abundances
spe.dbln <- vegdist(log1p(spe))
head(spe.dbln)
# Chord distance matrix
spe.dc <- dist.ldc(spe, "chord")
# Alternate, two-step computation in vegan:
spe.norm <- decostand(spe, "nor")
spe.dc <- dist(spe.norm)
head(spe.dc)
# Hellinger distance matrix
spe.dh <- dist.ldc(spe) # Hellinger is the default distance
# Alternate, two-step computation in vegan:
spe.hel <- decostand(spe, "hel")
spe.dh <- dist(spe.hel)
head(spe.dh)
# Log-chord distance matrix
spe.logchord <- dist.ldc(spe, "log.chord")
# Alternate, three-step computation in vegan:
spe.ln <- log1p(spe)
spe.ln.norm <- decostand(spe.ln, "nor")
spe.logchord <- dist(spe.ln.norm)
head(spe.logchord)
Hint Type ?log1p to see what you have done to the data prior to building the second
percentage difference dissimilarity matrix. Why use log1p rather than log?
3.3 Q Mode: Computing Dissimilarity Matrices Among Objects
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