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capscale-phyloseq-methods

Constrained Analysis of Principal Coordinates, capscale.


Description

See capscale for details. A formula is main input.

Usage

capscale.phyloseq(physeq, formula, distance, ...)

## S4 method for signature 'phyloseq,formula,dist'
capscale.phyloseq(physeq, formula,
  distance, ...)

## S4 method for signature 'phyloseq,formula,character'
capscale.phyloseq(physeq, formula,
  distance, ...)

Arguments

physeq

(Required). Phylogenetic sequencing data (phyloseq-class). The data on which you want to perform the ordination.

formula

(Required). A formula, specifying the input. No need to directly access components. capscale.phyloseq understands where to find the abundance table (LHS) and sample_data (RHS) from within the phyloseq object.

distance

(Required). A character string, specifying the name of the dissimilarity (or distance) method supported by the phyloseq distance function. Alternatively, a pre-computed dist-object can be provided here, in which case it supersedes any use of the otu_table in your phyloseq object.

Note that capscale with Euclidean distances will be identical to rda in eigenvalues and in site, species, and biplot scores (except for possible sign reversal). However, it makes no sense to use capscale with Euclidean distances, since direct use of rda is much more efficient (and supported in the ordinate function with method=="RDA") Even with non-Euclidean dissimilarities, the rest of the analysis will be metric and linear.

...

(Optional). Additional named arguments passed to capscale.

Value

Ordination object defined by capscale.

See Also

Examples

# See other examples at
# http://joey711.github.io/phyloseq/plot_ordination-examples
data(GlobalPatterns)
GP = prune_taxa(names(sort(taxa_sums(GlobalPatterns), TRUE)[1:50]), GlobalPatterns)
ordcap = ordinate(GP, "CAP", "bray", ~SampleType)
plot_ordination(GP, ordcap, "samples", color="SampleType")

phyloseq

Handling and analysis of high-throughput microbiome census data

v1.34.0
AGPL-3
Authors
Paul J. McMurdie <joey711@gmail.com>, Susan Holmes <susan@stat.stanford.edu>, with contributions from Gregory Jordan and Scott Chamberlain
Initial release
2019-04-23

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