get_pca: Performs a principal components analysis

In xiangpin/MicrobitaProcess: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

Performs a principal components analysis

Description

Performs a principal components analysis

Usage

get_pca(obj, ...)

## S3 method for class 'data.frame'
get_pca(obj, sampleda = NULL, method = "hellinger", ...)

## S3 method for class 'phyloseq'
get_pca(obj, method = "hellinger", ...)

Arguments

obj	phyloseq, phyloseq class or data.frame shape of data.frame is nrow sample * ncol feature.
...	additional parameters, seeprcomp.
sampleda	data.frame, nrow sample * ncol factors.
method	character, the standardization methods for community ecologists. see decostand.

Value

pcasample class, contained prcomp class and sample information.

Examples

## Not run: 
library(phyloseq)
data(GlobalPatterns)
subGlobal <- subset_samples(GlobalPatterns, 
         SampleType %in% c("Feces", "Mock", "Ocean", "Skin"))
pcares <- get_pca(subGlobal, method="hellinger")
pcaplot <- ggordpoint(pcares, biplot=TRUE, 
                      speciesannot=TRUE,
                      factorNames=c("SampleType"), ellipse=TRUE)

## End(Not run)

xiangpin/MicrobitaProcess documentation built on April 12, 2024, 9:03 p.m.