Plot PCA Projection

Plot the samples from a PCA projection in a 2-dimensional scatter plot.

Usage

plot_projection(
  data.prcomp,
  dims = 1:2L,
  classes = NULL,
  scores = NULL,
  col = NULL,
  samples = NULL,
  pt_cex = 2.5,
  ...
)

Arguments

data.prcomp

A prcomp class object. Typically the object returned by prcomp2().

dims

integer(2). Which dimensions to plot.

classes

Optional. A vector indicating the classes of samples used for coloring the points. Must be the same length as the number of samples.

scores

Optional. Statistical scores to pass through for the coloring of the points during plotting. If a training data set is passed and scores = NULL, then KS-distances (scores) will be calculated under the hood and used to determine point color. Can be either a single value (e.g. "red") or a vector of color values the same length as the number of observations. This overrides the point color assigned by classes above.

col

character(1). The color of the points. Can be either a single value (e.g. "red") or a vector of color values the same length as the number of observations. This parameter overrides the point colors determined by both the classes and scores parameters above.

samples

Optional. A vector of sample IDs to mark on the projection plot. Specified samples are marked with a hollow diamond (see pch()).

pt_cex

numeric(1). Character expansion for the points.

...

Additional arguments passed to plot_pca_dims().

See also

Other PCA plots: plot_pca_dims(), plot_rotation(), screeplot_auc()

Author

Stu Field, Michael R. Mehan

Examples

pca <- center_scale(pcapkg:::log10_ft(simdata), center = TRUE, scale = FALSE) |>
  feature_matrix() |>
  prcomp2()

# Define color of points
plot_projection(pca, col = "green")


# Use classes to define point colors
plot_projection(pca, classes = simdata$class_response)


# Mark specific samples
plot_projection(pca, samples = rownames(simdata)[1:5L])