autoplot.pca_common {ggfortify} | R Documentation |
Autoplot PCA-likes
## S3 method for class 'pca_common' autoplot( object, data = NULL, scale = 1, x = 1, y = 2, variance_percentage = TRUE, ... )
object |
PCA-like instance |
data |
Joined to fitting result if provided. |
scale |
scaling parameter, disabled by 0 |
x |
principal component number used in x axis |
y |
principal component number used in y axis |
variance_percentage |
show the variance explained by the principal component? |
... |
other arguments passed to [ggbiplot()] |
autoplot(stats::prcomp(iris[-5])) autoplot(stats::prcomp(iris[-5]), data = iris) autoplot(stats::prcomp(iris[-5]), data = iris, colour = 'Species') autoplot(stats::prcomp(iris[-5]), label = TRUE, loadings = TRUE, loadings.label = TRUE) autoplot(stats::prcomp(iris[-5]), frame = TRUE) autoplot(stats::prcomp(iris[-5]), data = iris, frame = TRUE, frame.colour = 'Species') autoplot(stats::prcomp(iris[-5]), data = iris, frame = TRUE, frame.type = 't', frame.colour = 'Species') autoplot(stats::princomp(iris[-5])) autoplot(stats::princomp(iris[-5]), data = iris) autoplot(stats::princomp(iris[-5]), data = iris, colour = 'Species') autoplot(stats::princomp(iris[-5]), label = TRUE, loadings = TRUE, loadings.label = TRUE) #Plot PC 2 and 3 autoplot(stats::princomp(iris[-5]), x = 2, y = 3) #Don't show the variance explained autoplot(stats::princomp(iris[-5]), variance_percentage = FALSE) d.factanal <- stats::factanal(state.x77, factors = 3, scores = 'regression') autoplot(d.factanal) autoplot(d.factanal, data = state.x77, colour = 'Income') autoplot(d.factanal, label = TRUE, loadings = TRUE, loadings.label = TRUE)