Visualize Multiple Factor Analysis

Multiple factor analysis (MFA) is used to analyze a data set in which individuals are described by several sets of variables (quantitative and/or qualitative) structured into groups. fviz_mfa() provides ggplot2-based elegant visualization of MFA outputs from the R function: MFA [FactoMineR].

fviz_mfa_ind(): Graph of individuals
fviz_mfa_var(): Graph of variables
fviz_mfa_axes(): Graph of partial axes
fviz_mfa(): An alias of fviz_mfa_ind(res.mfa, partial = "all")
fviz_mfa_quali_biplot(): Biplot of individuals and qualitative variables

Usage

fviz_mfa_ind(
  X,
  axes = c(1, 2),
  geom = c("point", "text"),
  repel = FALSE,
  habillage = "none",
  palette = NULL,
  addEllipses = FALSE,
  col.ind = "blue",
  col.ind.sup = "darkblue",
  alpha.ind = 1,
  shape.ind = 19,
  col.quali.var.sup = "black",
  select.ind = list(name = NULL, cos2 = NULL, contrib = NULL),
  partial = NULL,
  col.partial = "group",
  ...
)

fviz_mfa_quali_biplot(
  X,
  axes = c(1, 2),
  geom = c("point", "text"),
  repel = FALSE,
  title = "Biplot of individuals and qualitative variables - MFA",
  ...
)

fviz_mfa_var(
  X,
  choice = c("quanti.var", "group", "quali.var", "quali.sup"),
  axes = c(1, 2),
  geom = c("point", "text"),
  repel = FALSE,
  habillage = "none",
  col.var = "red",
  alpha.var = 1,
  shape.var = 17,
  col.var.sup = "darkgreen",
  palette = NULL,
  select.var = list(name = NULL, cos2 = NULL, contrib = NULL),
  ...
)

fviz_mfa_axes(
  X,
  axes = c(1, 2),
  geom = c("arrow", "text"),
  col.axes = NULL,
  alpha.axes = 1,
  col.circle = "grey70",
  select.axes = list(name = NULL, contrib = NULL),
  repel = FALSE,
  ...
)

fviz_mfa(X, partial = "all", ...)

Arguments

X

an object of class MFA [FactoMineR].

axes

a numeric vector of length 2 specifying the dimensions to be plotted.

geom

a text specifying the geometry to be used for the graph. Allowed values are the combination of c("point", "arrow", "text"). Use "point" (to show only points); "text" to show only labels; c("point", "text") or c("arrow", "text") to show arrows and texts. Using c("arrow", "text") is sensible only for the graph of variables.

repel

logical; whether to use ggrepel to avoid overplotting text labels. The old jitter argument is kept for backward compatibility and is converted to repel = TRUE with a deprecation warning.

habillage

an optional factor variable for coloring the observations by groups. Default value is "none". If X is an MFA object from FactoMineR package, habillage can also specify the index of the factor variable in the data.

palette

the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty". Can be also a numeric vector of length(groups); in this case a basic color palette is created using the function palette.

addEllipses

logical value. If TRUE, draws ellipses around the individuals when habillage != "none".

col.ind, col.var, col.axes

color for individuals, variables and col.axes respectively. Can be a continuous variable or a factor variable. Possible values also include "cos2", "contrib", "coord", "x", and "y". In this case, the colors for individuals/variables are automatically controlled by their qualities ("cos2"), contributions ("contrib"), coordinates (x^2 + y^2 , "coord"), x values("x") or y values("y"). To use automatic coloring (by cos2, contrib, ....), make sure that habillage ="none".

col.ind.sup

color for supplementary individuals

alpha.ind, alpha.var, alpha.axes

controls the transparency of individual, variable, group and axes colors, respectively. The value can vary from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values also include "cos2", "contrib", "coord", "x", and "y". In this case, the transparency for individual/variable colors are automatically controlled by their qualities ("cos2"), contributions ("contrib"), coordinates (x^2 + y^2 , "coord"), x values("x") or y values("y"). To use this, make sure that habillage ="none".

shape.ind, shape.var

point shapes of individuals, variables, groups and axes

col.quali.var.sup

color for supplementary qualitative variables. Default is "black".

select.ind, select.var, select.axes

a selection of individuals/partial individuals/ variables/groups/axes to be drawn. Allowed values are NULL or a list containing the arguments name, cos2 or contrib:

name is a character vector containing individuals/variables to be drawn
cos2 if cos2 is in [0, 1], ex: 0.6, then individuals/variables with a cos2 > 0.6 are drawn. if cos2 > 1, ex: 5, then the top 5 individuals/variables with the highest cos2 are drawn.
contrib if contrib > 1, ex: 5, then the top 5 individuals/variables with the highest contributions are drawn
union: logical. When several of name/cos2/contrib are given, FALSE (default) combines them with AND (each condition further narrows the selection); TRUE combines them with OR (an element is kept if it matches any condition), e.g. named items plus the top-cos2 ones.

partial

list of the individuals for which the partial points should be drawn. (by default, partial = NULL and no partial points are drawn). Use partial = "all" to visualize partial points for all individuals.

col.partial

color for partial individuals. By default, points are colored according to the groups.

...

Arguments to be passed to the function fviz()

title

the title of the graph

choice

the graph to plot. Allowed values include one of c("quanti.var", "quali.var", "quali.sup", "group") for plotting quantitative variables, qualitative variables, supplementary qualitative variables and group of variables, respectively.

col.var.sup

color for supplementary variables.

col.circle

a color for the correlation circle. Used only when X is a PCA output.

Value

a ggplot2 plot

References

https://www.datanovia.com/learn/

Author

Fabian Mundt f.mundt@inventionate.de

Alboukadel Kassambara alboukadel.kassambara@gmail.com

Examples

# Compute Multiple Factor Analysis
library("FactoMineR")
data(wine)
res.mfa <- MFA(wine, group=c(2,5,3,10,9,2), type=c("n",rep("s",5)),
               ncp=5, name.group=c("orig","olf","vis","olfag","gust","ens"),
               num.group.sup=c(1,6), graph=FALSE)
               
# Eigenvalues/variances of dimensions
fviz_screeplot(res.mfa)

# Group of variables
fviz_mfa_var(res.mfa, "group")

# Quantitative variables
fviz_mfa_var(res.mfa, "quanti.var", palette = "jco", 
  col.var.sup = "violet", repel = TRUE)

# Graph of individuals colored by cos2
fviz_mfa_ind(res.mfa, col.ind = "cos2", 
  gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
  repel = TRUE)

# Partial individuals
fviz_mfa_ind(res.mfa, partial = rownames(wine)[1:3], col.partial = "black") 
#> Ignoring unknown labels:
#> • colour : "Groups"

# Partial axes
fviz_mfa_axes(res.mfa)



# Graph of categorical variable categories
# ++++++++++++++++++++++++++++++++++++++++
data(poison)
res.mfa <- MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
               name.group=c("desc","desc2","symptom","eat"),
               num.group.sup=1:2, graph=FALSE)

# Plot of qualitative variables
fviz_mfa_var(res.mfa, "quali.var")

# Plot of supplementary qualitative variables
fviz_mfa_var(res.mfa, "quali.sup")

 
 

# Biplot of categorical variable categories and individuals
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 # Use repel = TRUE to avoid overplotting
grp <- as.factor(poison[, "Vomiting"])
fviz_mfa_quali_biplot(res.mfa, repel = FALSE, col.var = "#E7B800",
   habillage = grp, addEllipses = TRUE, ellipse.level = 0.95)