What is the function of correspondence analysis?
Correspondence Analysis, briefly CA, is one of the cousins of Principal Component Analysis. Both CA and PCA are multivariate techniques that help us to summarize the systematic patterns of variations in the data.
What is the use of correspondence analysis?
Correspondence Analysis (CA) is a multivariate graphical technique designed to explore relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with to the goal of examining associations amongst these variables.
How to use FactoMineR to plot PCa results?
First, we can load a few packages. require(FactoMineR)require(factoextra)require(ggplot2)require(tidyr)require(dplyr)require(MASS)require(reshape2)require(cowplot) Setting up the data The dataset I’ll be using is the ‘diamonds’ dataset, which contains data on almost 54 000 diamonds.
Which is true about rows and columns in FactoMineR?
Rows are individuals and columns are numeric variables scale.unit : a logical value. If TRUE, the data are scaled to unit variance before the analysis. This standardization to the same scale avoids some variables to become dominant just because of their large measurement units.
How does factoextra help in principal component analysis?
Note that, using factoextra package, the color or the transparency of variables can be automatically controlled by the value of their contributions, their cos2, their coordinates on x or y axis. This is helpful to highlight the most important variables in the determination of the principal components.
What are the supplementary qualitative variables in FactoMineR?
Supplementary continuous variables : Columns 11 and 12 corresponding respectively to the rank and the points of athletes. Supplementary qualitative variables : Column 13 corresponding to the two athlete-tic meetings (2004 Olympic Game or 2004 Decastar). This factor variables will be used to color individuals by groups.