PCA2: Principal Component Analysis

In ggfacto: Graphs for Correspondence Analysis

Principal Component Analysis

Description

A user-friendly wrapper around PCA, made to work better with ggfacto functions like ggpca_cor_circle. All variables can be selected by many different expressions, in the way of the 'tidyverse'. No supplementary vars are to be provided here, since they can be added afterward.

Usage

PCA2(
  data,
  active_vars,
  wt,
  col.w = NULL,
  ind_name,
  scale.unit = TRUE,
  ind.sup = NULL,
  ncp = 5,
  graph = FALSE,
  ...
)

Arguments

data	The data frame.
active_vars	<tidy-select> The names of the active variables.
wt	The name of the row weight variable
col.w	The weights of the columns, as a numeric vector of the same length than 'active_vars.'
ind_name	Possibly, a variable with the names of the individuals.
scale.unit	A boolean, if 'TRUE' (value set by default) then data are scaled to unit variance.
ind.sup	A vector indicating the indexes of the supplementary individuals.
ncp	Number of dimensions kept in the results (by default 5).
graph	A boolean, set to 'TRUE' to display the base graph.
...	Additional arguments to pass to PCA.

Value

A 'res.pca' object, with all the data necessary to draw the PCA.

Examples

active_vars <- c("mpg", "cyl", "hp", "drat", "qsec")
res.pca <- PCA2(mtcars, tidyselect::all_of(active_vars) )

ggfacto documentation built on Oct. 3, 2024, 1:08 a.m.