8.7 Análise de Correspondências Múltiplas (MCA)

8.7.1 MASS::mca

(Venables and Ripley 2002)

(farms.mca <- MASS::mca(farms, abbrev = TRUE))

## Call:
## MASS::mca(df = farms, abbrev = TRUE)
## 
## Multiple correspondence analysis of 20 cases of 4 factors
## 
## Correlations 0.806 0.745  cumulative % explained 26.87 51.71

plot(farms.mca)

8.7.2 FactoMineR::MCA

(Husson et al. 2023)

 library(FactoMineR)
 data(tea)
 res.mca <- FactoMineR::MCA(tea,quanti.sup=19,quali.sup=20:36)

 plot(res.mca,invisible=c("var","quali.sup","quanti.sup"),cex=0.7)

 plot(res.mca,invisible=c("ind","quali.sup","quanti.sup"),cex=0.8)

 plot(res.mca,invisible=c("quali.sup","quanti.sup"),cex=0.8)

Referências

Husson, Francois, Julie Josse, Sebastien Le, and Jeremy Mazet. 2023. FactoMineR: Multivariate Exploratory Data Analysis and Data Mining. http://factominer.free.fr.

Venables, W. N., and B. D. Ripley. 2002. Modern Applied Statistics with s. Fourth. New York: Springer. https://www.stats.ox.ac.uk/pub/MASS4/.