stat_ellipse() in MCA plot does not cover jittered points / extends far beyond the data
I am creating a Multiple Correspondence Analysis (MCA) plot in R using FactoMineR, factoextra, and ggplot2. The goal is to add confidence ellipses around the archetype categories in the MCA space.
The ellipses produced by stat_ellipse() do not match the distribution of the points:
- For some groups, the ellipse is much larger than the point cloud.
- For others, the ellipse fails to cover most of the actual points.
How can I generate ellipses in an MCA plot that accurately reflect the distribution of the points?
Code:
pacman::p_load(FactoMineR, factoextra, dplyr, gridExtra, tidyr)
# MCA with template as supplementary
mca_input <- all_df |> select(sector, type, template)
mca_res <- MCA(mca_input, quali.sup = 3, graph = FALSE)
# Extract coordinates
mca_coords <- as.data.frame(mca_res$ind$coord)
mca_coords$archetype <- all_df$template
# Test 1: Original variable associations (Fisher)
fish_type <- fisher.test(table(all_df$template, all_df$type), simulate.p.value = TRUE)
fish_sector <- fisher.test(table(all_df$template, all_df$sector), simulate.p.value = TRUE)
# Test 2: MCA dimensional separation (Kruskal-Wallis)
kw_dim1 <- kruskal.test(`Dim 1` ~ archetype, data = mca_coords)
kw_dim2 <- kruskal.test(`Dim 2` ~ archetype, data = mca_coords)
# Plot 1: MCA biplot
p1 <- ggplot() +
geom_hline(yintercept = 0, color = "grey50", linewidth = 0.5, linetype = "dashed") +
geom_vline(xintercept = 0, color = "grey50", linewidth = 0.5, linetype = "dashed") +
geom_jitter(data = mca_coords,
aes(x = `Dim 1`, y = `Dim 2`, color = archetype),
size = 3, alpha = 0.6, width = 0.03, height = 0.03) +
stat_ellipse(data = mca_coords,
aes(x = `Dim 1`, y = `Dim 2`, color = archetype),
level = 0.68, linewidth = 0.7) +
labs(title = "(A) Archetype Clustering in Feature Space",
x = paste0("Dim 1: Essential ↔ Non-essential (", round(mca_res$eig[1,2], 1), "%)"),
y = paste0("Dim 2: Retail/Commercial ↔ Industrial (", round(mca_res$eig[2,2], 1), "%)"),
color = "Archetype") +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position = "bottom")
p1
Dataset:
> dput(all_df)
structure(list(city = c("amsterdam", "ba", "berlin", "brisbane",
"cairo", "caracas", "dallas", "delhi", "dubai", "frankfurt",
"guangzhou", "istanbul", "johannesburg", "la", "lima", "london",
"madrid", "manchester", "melbourne", "milan", "mumbai", "munich",
"nairobi", "paris", "pune", "rio", "rome", "santiago", "shanghai",
"shenzhen", "sydney", "vienna", "almaty", "amsterdam", "ba",
"baku", "caracas", "chicago", "dallas", "johannesburg", "la",
"lima", "madrid", "manchester", "melbourne", "mexico", "milan",
"ny", "paris", "abu", "almaty", "amsterdam", "athens", "ba",
"baku", "beijing", "berlin", "brisbane", "cairo", "cape", "caracas",
"chicago", "dallas", "delhi", "dubai", "frankfurt", "guangzhou",
"hk", "istanbul", "jeddah", "johannesburg", "la", "lahore", "lima",
"london", "madrid", "manchester", "melbourne", "mexico", "milan",
"mumbai", "munich", "nairobi", "ny", "paris", "pune", "rio",
"riyadh", "rome", "santiago", "shanghai", "shenzhen", "sp", "sydney",
"vienna", "wash", "wuhan"), template = c("Chronic decline", "Resilient",
"Chronic decline", "Resilient", "Full recovery", "Resilient",
"Resilient", "Full recovery", "Full recovery", "Chronic decline",
"Partial recovery", "Chronic decline", "Chronic decline", "Full recovery",
"Resilient", "Chronic decline", "Full recovery", "Chronic decline",
"Partial recovery", "Chronic decline", "Full recovery", "Chronic decline",
"Full recovery", "Chronic decline", "Resilient", "Full recovery",
"Chronic decline", "Resilient", "Chronic decline", "Resilient",
"Partial recovery", "Chronic decline", "Resilient", "Chronic decline",
"Resilient", "Resilient", "Resilient", "Full recovery", "Resilient",
"Chronic decline", "Resilient", "Resilient", "Full recovery",
"Chronic decline", "Partial recovery", "Full recovery", "Chronic decline",
"Resilient", "Chronic decline", "Chronic decline", "Partial recovery",
"Chronic decline", "Full recovery", "Resilient", "Resilient",
"Resilient", "Chronic decline", "Resilient", "Partial recovery",
"Chronic decline", "Resilient", "Partial recovery", "Resilient",
"Full recovery", "Full recovery", "Chronic decline", "Partial recovery",
"Full recovery", "Chronic decline", "Chronic decline", "Chronic decline",
"Partial recovery", "Partial recovery", "Resilient", "Chronic decline",
"Full recovery", "Chronic decline", "Full recovery", "Full recovery",
"Chronic decline", "Resilient", "Chronic decline", "Partial recovery",
"Resilient", "Chronic decline", "Resilient", "Full recovery",
"Full recovery", "Full recovery", "Resilient", "Chronic decline",
"Resilient", "Resilient", "Partial recovery", "Chronic decline",
"Partial recovery", "Resilient"), type = c("non-essential", "mix",
"non-essential", "mix", "mix", "mix", "mix", "mix", "non-essential",
"non-essential", "non-essential", "non-essential", "mix", "mix",
"non-essential", "non-essential", "mix", "non-essential", "mix",
"non-essential", "non-essential", "non-essential", "mix", "non-essential",
"non-essential", "mix", "non-essential", "mix", "non-essential",
"non-essential", "mix", "non-essential", "essential", "non-essential",
"mix", "essential", "mix", "mix", "mix", "non-essential", "mix",
"essential", "mix", "non-essential", "mix", "non-essential",
"non-essential", "mix", "non-essential", "mix", "mix", "non-essential",
"mix", "mix", "mix", "essential", "non-essential", "mix", "non-essential",
"non-essential", "essential", "mix", "mix", "mix", "non-essential",
"non-essential", "non-essential", "mix", "non-essential", "non-essential",
"non-essential", "mix", "mix", "mix", "non-essential", "mix",
"non-essential", "mix", "mix", "non-essential", "mix", "non-essential",
"non-essential", "non-essential", "mix", "mix", "mix", "non-essential",
"mix", "essential", "non-essential", "non-essential", "mix",
"non-essential", "non-essential", "non-essential", "mix"), sector = c("Commercial",
"Commercial", "Commercial", "Commercial", "Commercial", "Commercial",
"Commercial", "Commercial", "Commercial", "Commercial", "Commercial",
"Commercial", "Commercial", "Commercial", "Commercial", "Commercial",
"Commercial", "Commercial", "Commercial", "Commercial", "Commercial",
"Commercial", "Commercial", "Commercial", "Commercial", "Commercial",
"Commercial", "Commercial", "Commercial", "Commercial", "Commercial",
"Commercial", "Retail", "Retail", "Retail", "Retail", "Retail",
"Retail", "Retail", "Retail", "Retail", "Retail", "Retail", "Retail",
"Retail", "Retail", "Retail", "Retail", "Retail", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial", "Industrial", "Industrial", "Industrial",
"Industrial", "Industrial")), class = "data.frame", row.names = c(NA,
-97L))
Session Info:
R version 4.5.2 (2025-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26200)
Matrix products: default
LAPACK version 3.12.1
locale:
[1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C LC_TIME=English_United States.utf8
time zone: Europe/Bucharest
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyr_1.3.1 gridExtra_2.3 dplyr_1.1.4 factoextra_1.0.7 ggplot2_4.0.1 FactoMineR_2.12
loaded via a namespace (and not attached):
[1] utf8_1.2.6 sandwich_3.1-1 generics_0.1.4 lattice_0.22-7 digest_0.6.38 magrittr_2.0.4
[7] grid_4.5.2 estimability_1.5.1 RColorBrewer_1.1-3 mvtnorm_1.3-3 fastmap_1.2.0 Matrix_1.7-4
[13] ggrepel_0.9.6 Formula_1.2-5 survival_3.8-3 multcomp_1.4-29 purrr_1.2.0 scales_1.4.0
[19] TH.data_1.1-5 isoband_0.2.7 codetools_0.2-20 abind_1.4-8 cli_3.6.5 rlang_1.1.6
[25] scatterplot3d_0.3-44 splines_4.5.2 leaps_3.2 withr_3.0.2 tools_4.5.2 multcompView_0.1-10
[31] coda_0.19-4.1 DT_0.34.0 flashClust_1.01-2 vctrs_0.6.5 R6_2.6.1 zoo_1.8-14
[37] lifecycle_1.0.4 emmeans_2.0.0 car_3.1-3 htmlwidgets_1.6.4 MASS_7.3-65 cluster_2.1.8.1
[43] pkgconfig_2.0.3 pillar_1.11.1 gtable_0.3.6 glue_1.8.0 Rcpp_1.1.0 tibble_3.3.0
[49] tidyselect_1.2.1 rstudioapi_0.17.1 dichromat_2.0-0.1 farver_2.1.2 xtable_1.8-4 htmltools_0.5.8.1
[55] carData_3.0-5 labeling_0.4.3 compiler_4.5.2 S7_0.2.1
Solution
My solution was to use convex hull instead of ellipses:
library(ggforce)
p1 <- ggplot(mca_coords, aes(x = `Dim 1`, y = `Dim 2`, color = archetype)) +
geom_hline(yintercept = 0, color = "grey50", linewidth = 0.5, linetype = "dashed") +
geom_vline(xintercept = 0, color = "grey50", linewidth = 0.5, linetype = "dashed") +
geom_mark_hull(aes(fill = archetype), alpha = 0.15, concavity = 1,
expand = unit(2, "mm"), radius = unit(2, "mm")) +
geom_jitter(size = 3, alpha = 0.6, width = 0.03, height = 0.03) +
labs(title = "(A) Archetype Clustering in Feature Space",
x = paste0("Dim 1: Essential ↔ Non-essential (", round(mca_res$eig[1,2], 1), "%)"),
y = paste0("Dim 2: Retail/Commercial ↔ Industrial (", round(mca_res$eig[2,2], 1), "%)"),
color = "Archetype",
fill = "Archetype") +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position = "bottom") + theme(panel.grid = element_blank(),
legend.position = "bottom",
axis.line = element_line(linewidth = 1))
p1
- How to find correct projection of the shapefile data for leaflet map in r?
- How to use regex in R to shorten strings by one space
- R CRAN Check fail when using parallel functions
- Replacing several rows of data in a column efficiently using condition in a pipeline
- Iterate over vectors from an imported dataframe row-wise
- Split the legend into three rows with headings for a nested facet_wrap
- Why replace works with a boolean mask?
- Not able properly rasterize polygon dataset using terra::rasterize()
- Less than symbol ("<") in cloze schoice in Canvas (html select dropdown) shows as &lt;
- R/exams docx output does not include images
- How to have primitive discard additional arguments
- Functions Causing Objects to Disappear
- How to repel texts equidistantly on a secondary y-axis of a facet_grid?
- How can I add some text to a ggplot2 graph in R outside the plot area?
- Extracting Exact p-values from the "did" Package Output in R
- How can I set column types of several columns in empty R data frame?
- Adjust vertical line based on maximum value in graph plotly
- Combining Two Dataframes Based on Multiple Conditions & Removing Rows that Don't Match
- independent sample t-test in R using test statistics (mean, std. dev, count)
- How do I load a package without installing it in R?
- survcheck: 'x' must be an array of at least two dimensions
- Insert variable names and their specific values between the last column of facets and the strips of a facet_grid in R
- Use a recorded plot for officer
- Errors when using RStudio's Git tools
- Orientation of an irregular window changes the distance r over which secondary summary statistics are estimated
- Elegant way to check for missing packages and install them?
- How to create tmap labels along lines with rotated crs?
- How to access the value of a reactive output inside the browser console with jQuery?
- Convert excel column names to numbers
- How to adjust a node width in a Sankey plot in R?