9.7 Análise de Correspondências Múltiplas (MCA)

9.7.1 `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)

9.7.2 `FactoMineR::MCA`

(Lê, Josse, and Husson 2008)

 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)

9.7.3 Visualizando

(D. A. Fife, Brunwasser, and Merkle 2022) e (D. Fife 2024) apresentam o pacote flexplavaan, que introduz novos gráficos e reformula gráficos existentes que forneçam os recursos necessários para avaliar modelos de variáveis latentes.

library(flexplavaan)
# flexplavaan::flexplavaan()

Referências

Fife, Dustin. 2024. Flexplavaan: Visualizing Latent Variable Models Using Flexplot. https://github.com/dustinfife/flexplavaan.

Fife, Dustin A, Steven M Brunwasser, and Edgar C Merkle. 2022. “Seeing the Impossible: Visualizing Latent Variable Models with Flexplavaan.” Psychological Methods. https://doi.org/10.31234/osf.io/qm7kj.

Lê, Sébastien, Julie Josse, and François Husson. 2008. “FactoMineR: A Package for Multivariate Analysis.” Journal of Statistical Software 25 (1): 1–18. https://doi.org/10.18637/jss.v025.i01.

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/.