plsda_scores_plot {structToolbox} | R Documentation

## PLSDA scores plot

### 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])

[Package structToolbox version 1.2.0 Index]