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How much weight to give to relative abundances; a value
between 0 and 1, inclusive. Setting alpha=1 is equivalent to
normalized_unifrac().
A numeric matrix of count data where each column is a
feature, and each row is a sample. Any object coercible with
as.matrix() can be given here, as well as phyloseq, rbiom,
SummarizedExperiment, and TreeSummarizedExperiment objects. For
optimal performance with very large datasets, see the guide in
vignette('performance').
How many parallel processing threads should be used. The
default, n_cpus(), will use all logical CPU cores.
The maximum number of observations to consider "rare".
Default: 10.
Precision of the returned values, in number of decimal
places. E.g. the default digits=3 could return 6.392.
Which combinations of samples should distances be
calculated for? The default value (NULL) calculates all-vs-all.
Provide a numeric or logical vector specifying positions in the
distance matrix to calculate. See examples.
Scaling factor for the magnitude of differences between
communities (\(p\)). Default: 1.5
The value to add to all counts in counts to prevent
taking log(0) for unobserved features. The default, NULL, selects
the smallest non-zero value in counts.
Normalize the incoming counts. Options are:
norm = "percent" - Relative abundance (sample abundances sum to 1).
norm = "binary" - Unweighted presence/absence (each count is either 0 or 1).
norm = "clr" - Centered log ratio.
norm = "none" - No transformation.
Default: 'percent', which is the expected input for these formulas.
If your samples are in the matrix's rows, set to 1L. If
your samples are in columns, set to 2L. Ignored when counts is a
phyloseq, rbiom, SummarizedExperiment, or
TreeSummarizedExperiment object. Default: 1L
A phylo-class object representing the phylogenetic tree for
the OTUs in counts. The OTU identifiers given by colnames(counts)
must be present in tree. Can be omitted if a tree is embedded with
the counts object or as attr(counts, 'tree').