This package is optimized to the needs of scientists within the social sciences. The soc.ca package produces specific and class specific multiple correspondence analysis on survey-like data. Soc.ca is optimized to only give the most essential statistical output sorted so as to help in analysis. Seperate functions exists for near publication-ready plots and tables.
We are in debt to the work of others, especially Brigitte Le Roux and Henry Rouanet for the mathematical definitions of the method and their examples. Furthermore this package was initially based on code from the ca package written by Michael Greenacre and Oleg Nenadic.
If you are looking for features that are absent in soc.ca, it may be available in some of these packages for correspondence analysis: ca, anacor and FactoMineR.
Le Roux, Brigitte, and Henry Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.
Le Roux, Brigitte, and Henry Rouanet. 2004. Geometric Data Analysis from Correspondence Analysis to Structured Data Analysis. Dordrecht: Kluwer Academic Publishers.
data(taste) # Create a data frame of factors containing all the active variables taste <- taste[which(taste$Isup == 'Active'), ] attach(taste) active <- data.frame(TV, Film, Art, Eat) sup <- data.frame(Gender, Age, Income) detach(taste) # Runs the analysis result <- soc.mca(active, sup)
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