factoextra (version 2.2.0)

fviz_contrib: Visualize the contributions of row/column elements

Description

This function can be used to visualize the contribution of rows/columns from the results of Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), Factor Analysis of Mixed Data (FAMD), and Multiple Factor Analysis (MFA) functions.

Read more: Principal Component Analysis (PCA) in R: Compute, Visualize & Interpret.

Usage

fviz_contrib(
  X,
  choice = c("row", "col", "var", "ind", "quanti.var", "quali.var", "group",
    "partial.axes"),
  axes = 1,
  fill = "steelblue",
  color = "steelblue",
  sort.val = c("desc", "asc", "none"),
  top = Inf,
  xtickslab.rt = 45,
  ggtheme = theme_minimal(),
  display = c("bar", "heatmap"),
  ...
)
fviz_pca_contrib(
  X,
  choice = c("var", "ind"),
  axes = 1,
  fill = "steelblue",
  color = "steelblue",
  sortcontrib = c("desc", "asc", "none"),
  top = Inf,
  ...
)

Value

a ggplot2 plot

Arguments

X

an object of class PCA, CA, MCA, FAMD, MFA and HMFA [FactoMineR]; prcomp and princomp [stats]; dudi, pca, coa and acm [ade4]; ca [ca package].

choice

allowed values are "row" and "col" for CA; "var" and "ind" for PCA or MCA; "var", "ind", "quanti.var", "quali.var" and "group" for FAMD, MFA and HMFA.

axes

a numeric vector specifying the dimension(s) of interest.

fill

a fill color for the bar plot.

color

an outline color for the bar plot.

sort.val

a string specifying whether the value should be sorted. Allowed values are "none" (no sorting), "asc" (for ascending) or "desc" (for descending).

top

a numeric value specifying the number of top elements to be shown.

xtickslab.rt

rotation angle for x axis tick labels. Default is 45 degrees.

ggtheme

function, ggplot2 theme name. The default is set by each function's ggtheme argument; see the function usage for the actual default. Set ggtheme = NULL to skip applying a ggpubr theme, so the plot keeps ggplot2 default theme or the theme set globally via theme_set(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....

display

how to display the values. "bar" (default) draws the usual barplot of the cos2/contribution summed over axes. "heatmap" draws a grid with one tile per element and dimension, filled by the per-dimension cos2/contribution and labelled with its value, so several dimensions can be read at once. With "heatmap", elements are ordered by their (unweighted) total over the requested axes and top keeps the leading ones; the bar-specific sort.val and color arguments are ignored, and fill sets the high end of the white-to-colour gradient.

...

other arguments to be passed to the function ggpar.

sortcontrib

see the argument sort.val

Functions

• fviz_pca_contrib(): deprecated function. Use fviz_contrib()

Author

Alboukadel Kassambara

Details

The function fviz_contrib() creates a barplot of row/column contributions. A reference dashed line is also shown on the barplot. This reference line corresponds to the expected value if the contribution were uniform.

For a given dimension, any row/column with a contribution above the reference line could be considered as important in contributing to the dimension.

References

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

See Also

fviz_cos2, get_pca. Online tutorial: Principal Component Analysis (PCA) in R: Compute, Visualize & Interpret.

Examples

# \donttest{
# Principal component analysis
# ++++++++++++++++++++++++++
data(decathlon2)
decathlon2.active <- decathlon2[1:23, 1:10]
res.pca <- prcomp(decathlon2.active,  scale = TRUE)

# variable contributions on axis 1
fviz_contrib(res.pca, choice="var", axes = 1, top = 10 )

# Change theme and color
fviz_contrib(res.pca, choice="var", axes = 1,
         fill = "lightgray", color = "black") +
         theme_minimal() +
         theme(axis.text.x = element_text(angle=45))

# Variable contributions on axis 2
fviz_contrib(res.pca, choice="var", axes = 2)
# Variable contributions on axes 1 + 2
fviz_contrib(res.pca, choice="var", axes = 1:2)

# Heat-grid of contributions across several dimensions
fviz_contrib(res.pca, choice = "var", axes = 1:3, display = "heatmap")

# Contributions of individuals on axis 1
fviz_contrib(res.pca, choice="ind", axes = 1)

if (FALSE) {
# Correspondence Analysis
# ++++++++++++++++++++++++++
# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data("housetasks")
res.ca <- CA(housetasks, graph = FALSE)

# Visualize row contributions on axes 1
fviz_contrib(res.ca, choice ="row", axes = 1)
# Visualize column contributions on axes 1
fviz_contrib(res.ca, choice ="col", axes = 1)

# Multiple Correspondence Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
              quali.sup = 3:4, graph=FALSE)

# Visualize individual contributions on axes 1
fviz_contrib(res.mca, choice ="ind", axes = 1)
# Visualize variable category contributions on axes 1
fviz_contrib(res.mca, choice ="var", axes = 1)

# Multiple Factor Analysis
# ++++++++++++++++++++++++
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)

# Visualize individual contributions on axes 1
fviz_contrib(res.mfa, choice ="ind", axes = 1, top = 20)
# Visualize categorical variable category contributions on axes 1
fviz_contrib(res.mfa, choice ="quali.var", axes = 1)
}

 # }