plsda_scores_plot: PLSDA scores plot

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

Description

A scatter plot of the selected PLSDA scores.

Usage

plsda_scores_plot(
  components = c(1, 2),
  points_to_label = "none",
  factor_name,
  ...
)

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.
...	Additional slots and values passed to struct_class.

Details

This object makes use of functionality from the following packages:

• pls

• ggplot2

Value

A plsda_scores_plot object.

References

Mevik B, Wehrens R, Liland K (2020). pls: Partial Least Squares and Principal Component Regression. R package version 2.7-3, https://CRAN.R-project.org/package=pls.

Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.

Examples

D = iris_DatasetExperiment()
M = mean_centre()+PLSDA(factor_name='Species')
M = model_apply(M,D)

C = plsda_scores_plot(factor_name='Species')
chart_plot(C,M[2])

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