pca_scores_plot {structToolbox} | R Documentation |
2d scatter plot of principal component scores.
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, ... )
components |
The principal components to plot ( |
points_to_label |
"none", "all", or "outliers" will be labelled on the plot. |
factor_name |
The sample_meta column name to use for colouring the points. You can provide up to two factors for this plot. |
ellipse |
"all" will plot all ellipses, "group" will only plot group ellipses, "none" will not plot any ellipses and "sample" will plot ellipse for all samples (ignoring group). |
label_filter |
Only include labels for samples in the group specified by label_filter. If zero length then all labels will be included. |
label_factor |
The sample_meta column to use for labelling the samples. If 'rownames' then the rownames will be used. |
label_size |
The text size of the labels.NB ggplot units, not font size units. Default 3.88. |
... |
additional slots and values passed to struct_class |
struct object
D = iris_DatasetExperiment() M = mean_centre() + PCA() M = model_apply(M,D) C = pca_scores_plot(factor_name = 'Species') chart_plot(C,M[2])