fviz_mfa: Visualize Multiple Factor Analysis

Description

Graph of individuals/quantitative variables/qualitative variables/group/partial axes from the output of Multiple Factor Analysis (MFA).

fviz_mfa_ind(): Graph of individuals
fviz_mfa_ind_starplot(): Star graph of individuals
fviz_mfa_quanti_var(): Graph of quantitative variables
fviz_mfa_quali_var(): Graph of qualitative variables
fviz_mfa_quali_biplot(): Biplot of individuals and qualitative variables
fviz_mfa_group(): Graph of the groups representation
fviz_mfa_axes(): Graph of partial axes
fviz_mfa(): An alias of fviz_mfa_ind_starplot()

Usage

fviz_mfa_ind(X, axes = c(1, 2), geom = c("point", "text"), label = "all", invisible = "none", labelsize = 4, pointsize = 2, habillage = "none", addEllipses = FALSE, ellipse.level = 0.95, ellipse.type = "norm", ellipse.alpha = 0.1, col.ind = "blue", col.ind.sup = "darkblue", alpha.ind = 1, shape.ind = 19, repel = FALSE, axes.linetype = "dashed", select.ind = list(name = NULL, cos2 = NULL, contrib = NULL), title = "Individuals factor map - MFA", jitter = list(what = "label", width = NULL, height = NULL), ...)
fviz_mfa_quanti_var(X, axes = c(1, 2), geom = c("arrow", "text"), label = "all", invisible = "none", labelsize = 4, pointsize = 2, col.var = "red", alpha.var = 1, shape.var = 17, col.quanti.sup = "blue", col.quali.sup = "darkgreen", col.circle = "grey70", select.var = list(name = NULL, cos2 = NULL, contrib  = NULL), axes.linetype = "dashed", title = "Quantitative Variable categories - MFA", repel = FALSE, jitter = list(what = "label", width = NULL, height = NULL))
fviz_mfa_quali_var(X, axes = c(1, 2), geom = c("point", "text"), label = "all", invisible = "none", labelsize = 4, pointsize = 2, col.var = "red", alpha.var = 1, shape.var = 17, col.quanti.sup = "blue", col.quali.sup = "darkgreen", repel = FALSE, select.var = list(name = NULL, cos2 = NULL, contrib = NULL), axes.linetype = "dashed", title = "Qualitative Variable categories - MFA", jitter = list(what = "label", width = NULL, height = NULL))
fviz_mfa_quali_biplot(X, axes = c(1, 2), geom = c("point", "text"), label = "all", invisible = "none", labelsize = 4, pointsize = 2, habillage = "none", addEllipses = FALSE, ellipse.level = 0.95, col.ind = "blue", col.ind.sup = "darkblue", alpha.ind = 1, col.var = "red", alpha.var = 1, col.quanti.sup = "blue", col.quali.sup = "darkgreen", shape.ind = 19, shape.var = 17, select.var = list(name = NULL, cos2 = NULL, contrib = NULL), select.ind = list(name = NULL, cos2 = NULL, contrib = NULL), axes.linetype = "dashed", title = "MFA factor map - Biplot", arrows = c(FALSE, FALSE), repel = FALSE, jitter = list(what = "label", width = NULL, height = NULL), ...)
fviz_mfa_ind_starplot(X, axes = c(1, 2), geom = c("point", "text"), label = "all", invisible = "none", legend.partial.title = NULL, labelsize = 4, pointsize = 2, linesize = 0.5, repel = FALSE, habillage = "none", addEllipses = FALSE, ellipse.level = 0.95, ellipse.type = "norm", ellipse.alpha = 0.1, col.ind = "black", col.ind.sup = "darkblue", col.partial = "black", alpha.ind = 1, shape.ind = 19, alpha.partial = 1, select.ind = list(name = NULL, cos2 = NULL, contrib = NULL), select.partial = list(name = NULL, cos2 = NULL, contrib = NULL), axes.linetype = "dashed", title = "Individuals factor map - MFA", jitter = list(what = "label", width = NULL, height = NULL), ...)
fviz_mfa_group(X, axes = c(1, 2), geom = c("point", "text"), alpha.group = 1, shape.group = 17, label = "all", invisible = "none", labelsize = 4, pointsize = 2, col.group = "blue", col.group.sup = "darkgreen", repel = FALSE, select.group = list(name = NULL, cos2 = NULL, contrib = NULL), title = "MFA - Groups Representations", jitter = list(what = "label", width = NULL, height = NULL), ...)
fviz_mfa_axes(X, axes = c(1, 2), geom = c("arrow", "text"), label = "all", invisible = "none", labelsize = 4, pointsize = 2, col.axes = "red", alpha.axes = 1, col.circle = "grey70", select.axes = list(name = NULL, contrib = NULL), axes.linetype = "dashed", title = "MFA - Partial Axes Representations", arrows = c(FALSE, FALSE), repel = FALSE, jitter = list(what = "label", width = NULL, height = NULL), ...)
fviz_mfa(X, ...)

Arguments

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 both types.

label

a text specifying the elements to be labelled. Default value is "all". Allowed values are "none" or the combination of c("ind", "ind.sup","var", "quali.sup", "quanti.sup"). "ind" can be used to label only active individuals. "ind.sup" is for supplementary individuals. "var" is for active variable categories. "quali.sup" is for supplementary qualitative variable categories. "quanti.sup" is for quantitative supplementary variables.

invisible

a text specifying the elements to be hidden on the plot. Default value is "none". Allowed values are the combination of c("ind", "ind.sup","var", "quali.sup", "quanti.sup").

labelsize

font size for the labels

pointsize

the size of points

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.

addEllipses

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

ellipse.level

the size of the concentration ellipse in normal probability.

ellipse.type

Character specifying frame type. Possible values are 'convex' or types supporeted by stat_ellipse including one of c("t", "norm", "euclid").

ellipse.alpha

Alpha for ellipse specifying the transparency level of fill color. Use alpha = 0 for no fill color.

col.ind, col.partial, col.var, col.group, col.group.sup, col.axes

color for individuals, partial individuals, variables, groups and axes, respectively. 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.partial, alpha.var, alpha.group, alpha.axes

controls the transparency of individual, partial 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, shape.group

point shapes of individuals, variables, groups and axes

repel

a boolean, whether to use ggrepel to avoid overplotting text labels or not.

axes.linetype

linetype of x and y axes.

select.ind, select.partial, select.var, select.group, 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

title

the title of the graph

jitter

a parameter used to jitter the points in order to reduce overplotting. It's a list containing the objects what, width and height (i.e jitter = list(what, width, height)).

what: the element to be jittered. Possible values are "point" or "p"; "label" or "l"; "both" or "b"
width: degree of jitter in x direction
height: degree of jitter in y direction

...

Arguments to be passed to the function fviz_mfa_quali_biplot()

col.quanti.sup, col.quali.sup

a color for the quantitative/qualitative supplementary variables.

col.circle

a color for the correlation circle.

arrows

Vector of two logicals specifying if the plot should contain points (FALSE, default) or arrows (TRUE). First value sets the rows and the second value sets the columns.

legend.partial.title

the title of the partal groups legend.

linesize

size of partial point connecting line.

Value

a ggplot2 plot

References

http://www.sthda.com

Examples

Run this code

# Multiple Factor Analysis
# ++++++++++++++++++++++++
# Install and load FactoMineR to compute MFA
# install.packages("FactoMineR")
library("FactoMineR")
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)

# Graph of individuals
# ++++++++++++++++++++

# Default plot
# Use repel = TRUE to avoid overplotting (slow if many points)
# Color of individuals: col.ind = "#2E9FDF"
fviz_mfa_ind(res.mfa, repel = TRUE, col.ind = "#2E9FDF")+
theme_minimal()
   
## Not run: 
# # 1. Control automatically the color of individuals 
#    # using the "cos2" or the contributions "contrib"
#    # cos2 = the quality of the individuals on the factor map
# # 2. To keep only point or text use geom = "point" or geom = "text".
# # 3. Change themes: http://www.sthda.com/english/wiki/ggplot2-themes
# 
# fviz_mfa_ind(res.mfa, col.ind = "cos2")+
#  theme_minimal()
# 
# # Change gradient color
# # Use repel = TRUE to avoid overplotting (slow if many points)
# fviz_mfa_ind(res.mfa, col.ind="cos2", repel = TRUE) + 
#       scale_color_gradient2(low = "white", mid = "#2E9FDF", 
#       high= "#FC4E07", midpoint=0.4, space = "Lab")+
#  theme_minimal()
# ## End(Not run)

# Color individuals by groups, add concentration ellipses
# Remove labels: label = "none".
grp <- as.factor(poison[, "Vomiting"])
p <- fviz_mfa_ind(res.mfa, label="none", habillage=grp,
       addEllipses=TRUE, ellipse.level=0.95)
print(p)
 
 ## Not run: 
# # Change group colors using RColorBrewer color palettes
# # Read more: http://www.sthda.com/english/wiki/ggplot2-colors
# p + scale_color_brewer(palette="Paired") +
#     scale_fill_brewer(palette="Paired") +
#      theme_minimal()
#  ## End(Not run)
     
# Change group colors manually
# Read more: http://www.sthda.com/english/wiki/ggplot2-colors
p + scale_color_manual(values=c("#999999", "#E69F00"))+
 scale_fill_manual(values=c("#999999", "#E69F00"))+
 theme_minimal()  
 
 ## Not run: 
# # Select and visualize some individuals (ind) with select.ind argument.
#  # - ind with cos2 >= 0.4: select.ind = list(cos2 = 0.4)
#  # - Top 20 ind according to the cos2: select.ind = list(cos2 = 20)
#  # - Top 20 contributing individuals: select.ind = list(contrib = 20)
#  # - Select ind by names: select.ind = list(name = c("44", "38", "53",  "39") )
#  
# # Example: Select the top 20 according to the cos2
# fviz_mfa_ind(res.mfa, select.ind = list(cos2 = 20))
# ## End(Not run)

# Graph of qantitative variable categories
# ++++++++++++++++++++++++++++++++++++++++
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)
               
# Default plot
fviz_mfa_quanti_var(res.mfa, col.var = "#FC4E07")+
theme_minimal()
   
## Not run: 
# # Control variable colors using their contributions
# fviz_mfa_quanti_var(res.mfa, col.var = "contrib")+
#  scale_color_gradient2(low = "white", mid = "blue",
#            high = "red", midpoint = 20) +
#  theme_minimal()
#  
# # Select variables with select.var argument
#    # You can select by contrib, cos2 and name 
#    # as previously described for ind
# # Select the top 10 contributing variables
# fviz_mfa_quanti_var(res.mfa, select.var = list(contrib = 10))
# ## End(Not run)
 
# 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
# Control variable colors using their contributions
fviz_mfa_quali_var(res.mfa, col.var = "contrib")+
 scale_color_gradient2(low = "white", mid = "blue",
           high = "red", midpoint = 2) +
 theme_minimal()
 
 
 ## Not run: 
# # Select the top 10 contributing variable categories
# fviz_mfa_quali_var(res.mfa, select.var = list(contrib = 10))
# ## End(Not run)


# 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)+
 theme_minimal()

               
# Graph of partial individuals (starplot)
# +++++++++++++++++++++++++++++++++++++++
fviz_mfa_ind_starplot(res.mfa, col.partial = "group.name")+
scale_color_brewer(palette = "Dark2")+
theme_minimal()

## Not run: 
# # Select the partial points of the top 5
# # contributing individuals
# fviz_mfa_ind_starplot(res.mfa, 
#                       select.partial = list(contrib = 2)) +
#                       theme_minimal()
#                       
# # Change colours of star segments
# fviz_mfa_ind_starplot(res.mfa, select.partial = list(contrib = 5), 
#                       col.partial = "group.name") +
#                       scale_color_brewer(palette = "Dark2") +
#                       theme_minimal() 
# ## End(Not run)

 
# Graph of groups (correlation square)
# ++++++++++++++++++++++++++++++++++++
fviz_mfa_group(res.mfa)

#' # Graph of partial axes
# ++++++++++++++++++++++++
fviz_mfa_axes(res.mfa)

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