get_pcoa {MicrobiotaProcess} | R Documentation

## performs principal coordinate analysis (PCoA)

### Description

performs principal coordinate analysis (PCoA)

### Usage

    get_pcoa(obj, ...)

    ## Default S3 method:
    get_pcoa(
      obj,
      distmethod = "euclidean",
      taxa_are_rows = FALSE,
      sampleda = NULL,
      tree = NULL,
      method = "hellinger",
      ...
    )

    ## S3 method for class 'dist'
    get_pcoa(
      obj,
      distmethod,
      data = NULL,
      sampleda = NULL,
      method = "hellinger",
      ...
    )

    ## S3 method for class 'phyloseq'
    get_pcoa(obj, distmethod = "euclidean", ...)

### Arguments

`obj` | phyloseq, the phyloseq class or dist class.
`...,` | additional parameter, see also `get_dist`.
`distmethod` | character, the method of distance, see also `distance`
`taxa_are_rows` | logical, if feature of data is column, it should be set FALSE.
`sampleda` | data.frame, nrow sample * ncol factor, default is NULL.
`tree` | phylo, the phylo class, default is NULL, when use unifrac method, it should be required.
`method` | character, the standardization method for community ecologists, default is hellinger, if the data has be normlized, it shound be set NULL.
`data` | data.frame, numeric data.frame nrow sample * ncol features.

### Value

pcasample object, contained prcomp or pcoa and sampleda (data.frame).

### Author(s)

Shuangbin Xu

### Examples

    library(phyloseq)
    data(GlobalPatterns)
    subGlobal <- subset_samples(GlobalPatterns,
                  SampleType %in% c("Feces", "Mock", "Ocean", "Skin"))
    #pcoares <- get_pcoa(subGlobal,
    #                   distmethod="euclidean",
    #                   method="hellinger")
    # pcoaplot <- ggordpoint(pcoares, biplot=FALSE,
    #                        speciesannot=FALSE,
    #                        factorNames=c("SampleType"),
    #                        ellipse=FALSE)

[Package MicrobiotaProcess version 1.0.5 Index]