data(ITS1, meta)
# The argument for factors is a vector of length two; the first
# item is # Crop, which is a column from meta, and the second
# item is City, another # column from meta.
pcoa.plot(ITS1, meta=meta, rank="c",
factors=c(Crop="Crop", City="City"))
## Not run:
# # If you want to customize legend labels and plot the top 20
# # taxon groups at genus:
# pcoa.plot(ITS1, meta=meta, rank="g", main="PCoA plot",
# factors=c(Place="City",
# Harvest_Method="Harvestmethod"))
# # In black & white, using base graphics:
# pcoa.plot(ITS1, meta=meta, rank="c", factors=c(Plot="Plots"),
# ggplot=F, bw=T)
# pcoa.plot(ITS1, meta=meta, rank="c", factors=c(Plot="Plots"),
# ggplot=F, bw=T, dist.method="euc",
# stand.method="hell")
# # Focus on the samples: hide all groups and plot ellipses
# # for Crop:
# pcoa.plot(ITS1, meta=meta, rank="g",
# factors=c(Crop="Crop", City="City"),
# ellipse=1, sample.labels=FALSE, top=0)
# # Standardize the data before calculating distances:
# pcoa.plot(ITS1, meta=meta, rank="g", factors=c(City="City"),
# stand.method="chi.square",
# dist.method="euclidean")
# ## End(Not run)
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