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2026-06-23 18:00:14 +00:00
parent 9447e0f2c2
commit 2a7bfb9ffb
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42
reference/ggplot_pca.md
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@@ -8,13 +8,30 @@ base R [`biplot()`](https://rdrr.io/r/stats/biplot.html) function.
## Usage
``` r
ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE,
labels = NULL, labels_textsize = 3, labels_text_placement = 1.5,
groups = NULL, ellipse = TRUE, ellipse_prob = 0.68,
ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2,
points_alpha = 0.25, arrows = TRUE, arrows_colour = "darkblue",
arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE,
arrows_alpha = 0.75, base_textsize = 10, ...)
ggplot_pca(
x,
choices = 1:2,
scale = 1,
pc.biplot = TRUE,
labels = NULL,
labels_textsize = 3,
labels_text_placement = 1.5,
groups = NULL,
ellipse = TRUE,
ellipse_prob = 0.68,
ellipse_size = 0.5,
ellipse_alpha = 0.5,
points_size = 2,
points_alpha = 0.25,
arrows = TRUE,
arrows_colour = "darkblue",
arrows_size = 0.5,
arrows_textsize = 3,
arrows_textangled = TRUE,
arrows_alpha = 0.75,
base_textsize = 10,
...
)
```
## Source
@@ -68,12 +85,11 @@ the changes made based on the source code were:
- pc.biplot:
If true, use what [Gabriel
(1971)](#reference+biplot.princomp.Rd+R+3AGabriel+3A1971) refers to as
a “principal component biplot”, with `lambda = 1` and observations
scaled up by sqrt(n) and variables scaled down by sqrt(n). Then inner
products between variables approximate covariances and distances
between observations approximate Mahalanobis distance.
If true, use what Gabriel (1971) refers to as a "principal component
biplot", with `lambda = 1` and observations scaled up by sqrt(n) and
variables scaled down by sqrt(n). Then inner products between
variables approximate covariances and distances between observations
approximate Mahalanobis distance.
- labels: