interpret: Interpret the Axes of an Analysis

In ggfacto: Graphs for Correspondence Analysis

Interpret the Axes of an Analysis

Description

One table to read the axes of a factorial analysis, whatever the analysis:

• a multiple correspondence analysis: per axis, the active levels contributing more than the mean contribution, the positive side facing the negative one, and the spread between the two sides in percent of each question's contribution (Brigitte Le Roux and Henri Rouanet, Geometric data analysis, Kluwer, 2004; Brigitte Le Roux, Analyse geometrique des donnees multidimensionnelles, Dunod, 2014);

• a correspondence analysis: the same for the row points and the column points, each margin against its own mean contribution, since each sums to 100 different number of points;

• a principal component analysis: each active variable's mean and spread, then, per axis, its coordinate — which under 'scale.unit' IS its correlation with the axis —, its contribution and its cos2.

The eigenvalues of the axes travel under the table, with Benzecri's modified rate for an MCA. 'mca_interpret()' and 'pca_interpret()' are the same tables, for one analysis each.

Usage

interpret(res, ...)

mca_interpret(
  res.mca,
  axes = 1:5,
  complete = FALSE,
  min_contrib = NULL,
  color = TRUE,
  eig = TRUE,
  n_axes = 8L,
  lang = NULL,
  type = NULL,
  spread = NULL
)

pca_interpret(
  res.pca,
  axes = 1:3,
  color = TRUE,
  eig = TRUE,
  n_axes = 8L,
  lang = NULL
)

Arguments

res	An analysis made with multiple_correspondence_analysis, correspondence_analysis or principal_component_analysis (or with FactoMineR::MCA(), CA() or PCA(), or GDAtools::speMCA() or csMCA()).
...	The arguments below. A correspondence analysis takes one more, 'vars': the two margins' names, as in 'vars = c("CSER", "PR2017")'. By default, the names correspondence_analysis kept; after a bare FactoMineR::CA(), which keeps none, the table says “Rows” and “Columns”.
res.mca, res.pca	The analysis, for 'mca_interpret()' and 'pca_interpret()'.
axes	The axes to interpret, as an integer vector. By default, the first five of an MCA, two of a CA, three of a PCA.
complete	For an MCA or a CA, set to TRUE for the fuller summary: each side of the axis gains the point's coordinate and its cos2, and the table gains the spread between the two sides.
min_contrib	For an MCA or a CA, the contribution threshold, in percent. NULL (the default) is the mean contribution of the point's own set; 0 keeps every point.
color	Set to FALSE to build the table with no colour measure, and no data bar under the eigenvalues.
eig	The eigenvalues travel under the table. Set to FALSE in a document that already shows them, or that prints the summary several times to comment it column by column.
n_axes	How many axes the eigenvalue table prints. When some are left out, an ellipsis row states how many the cloud has.
lang	NULL (the session's language), "en" or "fr".
type	Deprecated. The output format is now options(tabxplor.print), or an explicit tab_md / tab_html call — see [ggfacto_summary].
spread	Deprecated. Folded into complete.

Value

A tabxplor table — see [ggfacto_summary] for how it prints.

See Also

[ggfacto_summary], [benzecri_mrv()].

Examples

# ONE option decides how every tabxplor table prints, an interpretation table included.
# In a script it goes once, at the top, beside the library() calls.
options(tabxplor.print = "html")

data(tea, package = "FactoMineR")
res.mca <- multiple_correspondence_analysis(tea, 1:18)
interpret(res.mca)
interpret(res.mca, axes = 1:2, complete = TRUE)

# a correspondence analysis draws the STRUCTURE of a crosstab's deviations and says nothing of
# their size, so the crosstab is asked for beside it, never instead of it:
crosstab <- tabxplor::tab(forcats::gss_cat, race, marital)
interpret(correspondence_analysis(crosstab))
tabxplor::tab(forcats::gss_cat, race, marital, pct = "row", color = "contrib", test = TRUE)

cars <- dplyr::rename(mtcars[1:7], weight = wt)
interpret(principal_component_analysis(cars, 1:7))

ggfacto documentation built on Sept. 23, 2026, 1:08 a.m.