Returns the graphs of the individuals and categories and the graph with the variables. The plots may be improved using the argument autolab, modifying the size of the labels or selecting some elements thanks to the plot.MCA function.

Oct 13, 2012 · In R, there are several functions from different packages that allow us to apply Multiple Correspondence Analysis. In this post I’ll show you 5 different ways to perform MCA using the following functions (with their corresponding packages in parentheses):

May 29, 2024 · Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Performs also Specific Multiple Correspondence Analysis with supplementary categories and supplementary categorical variables.

Correspondence analysis (CA) is a multivariate analysis method that is built upon PCA. CA is best when use to analyze nominal data or qualitative data (as opposed to quantitative). CA takes Contingency table as its Input.

Sep 24, 2017 · In the current chapter, we demonstrate how to compute and visualize multiple correspondence analysis in R software using FactoMineR (for the analysis) and factoextra (for data visualization). Additionally, we’ll show how to reveal the most important variables that contribute the most in explaining the variations in the data set.

MCA has the ability to separate non-linear relationships creating individual clusters from, for example, a horse shoe shaped cluster. Binning with blue and pink has created groups of “no days” and “several days and higher.”

# h.b005.ctr.lv.2 <- PrettyBarPlot2(signed.ctr.lv[,2], threshold = 1 / NROW(signed.ctr.lv), font.size = 5, color4bar = gplots::col2hex(col4Levels$color4Levels), # we need hex code main = 'MCA: Variable Level Contributions (Signed)', ylab = 'Contributions', ylim = c(1.2*min(signed.ctr.lv), 1.2*max(signed.ctr.lv)) ) print(h.b005.ctr.lv.2)

Returns the graphs of the individuals and categories and the graph with the variables. The plots may be improved using the argument autolab, modifying the size of the labels or selecting some elements thanks to the plot.MCA function.

Multiple Correspondence Analysis (MCA) is an extension of simple CA to analyse a data table containing more than two categorical variables. fviz_mca () provides ggplot2-based elegant visualization of MCA outputs from the R functions: MCA [in FactoMineR], acm [in ade4], and expOutput/epMCA [in ExPosition].

Apr 21, 2017 · Multiple correspondence analysis (MCA) is a multivariate data analysis and data mining tool for finding and constructing a low-dimensional visual representation of variable associations among groups of categorical variables.