Se leen los datos saeraq.Rdata
, que provienen del fichero raq.dat
utilizado en (A. Field, Miles, and Field 2012).
load( "saeraq.RData" )
indicesPreg <- grep( "^Q", colnames( df ) )
df[ , indicesPreg ] <- as.data.frame( lapply( df[ , indicesPreg ], as.numeric ) )
# library( FactoMineR )
pca <- PCA( df, quali.sup = c( 1:3, 5:6 ), quanti.sup = 4,
scale.unit = TRUE, graph = FALSE )
# library( factoextra )
fviz_screeplot( pca, addlabels = TRUE ) +
ggtitle("") +
scale_x_discrete( name = "Componentes principales" ) +
scale_y_continuous( name = "Autovalor y varianza explicada" ) +
theme_minimal()
fviz_pca_var( pca, axes = c( 1, 2 ), geom = "text", alpha.var = "contrib",
labelsize = 2 ) +
ggtitle("") +
theme_minimal()
fviz_pca_biplot( pca, pointsize = 1.5, labelsize = 2.5, #alpha.var="contrib",
habillage = df$sexo, addEllipses = TRUE,
ellipse.level = 0.95, select.var = list( contrib = 25 ) ) +
ggtitle("") +
theme_minimal()
Field, Andy, Jeremy Miles, and Zoe Field. 2012. Discovering Statistics Using R. 1st edition. Sage Publications Ltd.
Servicio de Apoyo Estadístico; alvarohv@um.es, elvira@um.es, antoniojose.peran@um.es, anabelen.marin4@um.es, amaurandi@um.es↩
doc:T4_analisisPCA.Rmd↩