pca_scores_plot: PCA scores plot

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

Plots a 2d scatter plot of the selected components

Usage

pca_scores_plot(
  components = c(1, 2),
  points_to_label = "none",
  factor_name,
  ellipse = "all",
  label_filter = character(0),
  label_factor = "rownames",
  label_size = 3.88,
  ...
)

Arguments

components	(numeric) The components selected for plotting. The default is c(1, 2).
points_to_label	(character) Points to label. Allowed values are limited to the following: "none": No samples labels are displayed. "all": The labels for all samples are displayed. "outliers": Labels for for potential outlier samples are displayed. The default is "none".
factor_name	(character) The name of a sample-meta column to use.
ellipse	(character) Plot ellipses. Allowed values are limited to the following: "all": Hotelling T2 95% ellipses are plotted for all groups and all samples. "group": Hotelling T2 95% ellipses are plotted for all groups. "none": Ellipses are not included on the plot. "sample": A Hotelling T2 95% ellipse is plotted for all samples (ignoring group). The default is "all".
label_filter	(character) Labels are only plotted for the named groups. If zero-length then all groups are included. The default is character(0).
label_factor	(character) The column name of sample_meta to use for labelling samples on the plot. "rownames" will use the row names from sample_meta. The default is "rownames".
label_size	(numeric) The text size of labels. Note this is not in Font Units. The default is 3.88.
...	Additional slots and values passed to struct_class.

Value

A pca_scores_plot object.

Examples

D = iris_DatasetExperiment()
M = mean_centre() + PCA()
M = model_apply(M,D)
C = pca_scores_plot(factor_name = 'Species')
chart_plot(C,M[2])

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.