# PCA of iris data
iris_pca <- ordinate(iris, cols = 1:4, prcomp, scale = TRUE)
# row-principal predictive biplot
iris_pca %>%
ggbiplot(axis.type = "predictive") +
theme_bw() +
scale_color_brewer(type = "qual", palette = 2) +
geom_cols_axis(aes(label = name, center = center, scale = scale)) +
geom_rows_point(aes(color = Species), alpha = .5) +
ggtitle("Predictive biplot of Anderson iris measurements")
# with two calibrated axes
iris_pca %>%
ggbiplot(axis.type = "predictive") +
theme_bw() +
scale_color_brewer(type = "qual", palette = 2) +
geom_origin() +
stat_cols_rule(
subset = c(2, 4), fontface = "bold", text.fontface = "plain",
aes(label = name, center = center, scale = scale)
) +
geom_rows_point(aes(color = Species), alpha = .5) +
expand_limits(x = c(-5, 5), y = c(-5, 5)) +
ggtitle("Predictive biplot of Anderson iris measurements")
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