Correspondence Analysis inR, with Two- and Three-dimensional Graphics: ThecaPackage

Abstract

We describe an implementation of simple, multiple and joint correspondence analysis in R. The resulting package comprises two parts, one for simple correspondence analysis and one for multiple and joint correspondence analysis. Within each part, functions for computation, summaries and visualization in two and three dimensions are provided, including options to display supplementary points and perform subset analyses. Special emphasis has been put on the visualization functions that offer features such as different scaling options for biplots and three-dimensional maps using the rgl package. Graphical options include shading and sizing plot symbols for the points according to their contributions to the map and masses respectively.

Sex‐specific genetic diversity is shaped by cultural factors in Inner Asian human populations

“…For both mtDNA and Y chromosome haplogroup frequencies, the two first Principal Components are presented as Supporting Information 1A and B. Additionally, in a more traditional way, we performed Correspondence Analysis (ca package in R; Nenadic & Greenacre, 2007) on the haplogroup frequencies for both markers (Supporting Information 1C and D). Additionally, in a more traditional way, we performed Correspondence Analysis (ca package in R; Nenadic & Greenacre, 2007) on the haplogroup frequencies for both markers (Supporting Information 1C and D).…”

“…However, we analyzed only the first principal component (Table S3B), based on a Scree test (Cattell, 1966) (Supporting Information 2). Additionally, in a more traditional way, we performed Correspondence Analysis (ca package in R; Nenadic & Greenacre, 2007) on the haplogroup frequencies for both markers (Supporting Information 1C and D).…”

missMDA: A Package for Handling Missing Values in Multivariate Data Analysis

“…Greenacre and Pardo (2006) showed that it is also closely related to their "subset MCA" approach, in which one adds a new category for the missing values and then treats it as an additional element. Their strategy is implemented in the R package ca (Nenadic and Greenacre 2007) using the function mjca with argument subsetcol. Josse, Chavent, Liquet, and Husson (2012) provided a review of existing methods for handling missing values in MCA and suggested a new approach named regularized iterative MCA.…”

Classifying forest inventory data into species-based forest community types at broad extents: exploring tradeoffs among supervised and unsupervised approaches

“…All data manipulation and analysis was done using R 3.4.0 (R Core Team 2017) and the following contributed packages: ca (Nenadic and Greenacre 2007), class (Venables and Ripley 2002), factoextra (Kassambara and Mundt 2017), irr (Gamer et al 2012), labdsv (Roberts 2016), randomForest (Liaw and Wiener 2002), RColorBrewer (Neuwirth 2014), raster (Hijmans 2016), and tidyverse (Wickham 2017), and vegan (Oksanen et al 2016).…”