MCA2: Multiple Correspondence Analysis

In ggfacto: Graphs for Correspondence Analysis

Multiple Correspondence Analysis

Description

A user-friendly wrapper around MCA, made to work better with ggfacto functions like ggmca. 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 in ggmca.

Usage

MCA2(data, active_vars, wt, excl, ncp = 5, graph = FALSE, ...)

Arguments

data	The data frame.
active_vars	<tidy-select>
wt	<tidy-select>
excl	A character vector of regular expressions to exclude "junk" categories. Any level of an active variable with any of the detected patterns is not taken into account in the calculation of axes (which is called specific multiple correspondence analysis).
ncp	The number of axes to keep. Default to 5.
graph	By default no graph is made, since the result can be ploted with ggmca.
...	Additionnal arguments to pass to MCA.

Value

A 'res.mca' object, with all the data necessary to draw the MCA.

Examples

 data(tea, package = "FactoMineR")
res.mca <- MCA2(tea, active_vars = 1:18)

res.mca %>%
  ggmca(tea, sup_vars = c("SPC"), ylim = c(NA, 1.2), text_repel = TRUE) %>%
  ggi() #to make the graph interactive

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