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

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 = repel,
  title = "Biplot of individuals and qualitative variables - MFA", ...)

fviz_mfa_var(X, choice = c("quanti.var", "group", "quali.var"), 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
a boolean, whether to use ggrepel to avoid overplotting text labels or not.
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 include also : "cos2", "contrib", "coord", "x" or "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 variate from 0 (total transparency) to 1 (no transparency). Default value is 1. Possible values include also : "cos2", "contrib", "coord", "x" or "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 cos2 are drawn
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", "group") for plotting quantitative variables, 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

http://www.sthda.com/english/

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 = "all")
# 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")
# 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)