PCA_function: Analysis: Principal components analysis

In AgroR: Experimental Statistics and Graphics for Agricultural Sciences

Analysis: Principal components analysis

Description

This function performs principal component analysis.

Usage

PCA_function(
  data,
  scale = TRUE,
  text = TRUE,
  pointsize = 5,
  textsize = 12,
  labelsize = 4,
  linesize = 0.6,
  repel = TRUE,
  ylab = NA,
  xlab = NA,
  groups = NA,
  sc = 1,
  font.family = "sans",
  theme = theme_bw(),
  label.legend = "Cluster",
  type.graph = "biplot"
)

Arguments

data	Data.frame with data set. Line name must indicate the treatment
scale	Performs data standardization (default is TRUE)
text	Add label (default is TRUE)
pointsize	Point size (default is 5)
textsize	Text size (default is 12)
labelsize	Label size (default is 4)
linesize	Line size (default is 0.8)
repel	Avoid text overlay (default is TRUE)
ylab	Names y-axis
xlab	Names x-axis
groups	Define grouping
sc	Secondary axis scale ratio (default is 1)
font.family	Font family (default is sans)
theme	Theme ggplot2 (default is theme_bw())
label.legend	Legend title (when group is not NA)
type.graph	Type of chart (default is biplot)

Details

The type.graph argument defines the graph that will be returned, in the case of "biplot" the biplot graph is returned with the first two main components and with eigenvalues and eigenvectors. In the case of "scores" only the treatment scores are returned, while for "cor" the correlations are returned. For "corPCA" a correlation between the vectors with the components is returned.

Value

The eigenvalues and eigenvectors, the explanation percentages of each principal component, the correlations between the vectors with the principal components, as well as graphs are returned.

Author(s)

Gabriel Danilo Shimizu

Examples

data(pomegranate)
medias=tabledesc(pomegranate)
PCA_function(medias)

AgroR documentation built on May 29, 2024, 4:18 a.m.