plsda_roc_plot: PLSDA ROC plot

In computational-metabolomics/structtoolbox: Data processing & analysis tools for Metabolomics and other omics

PLSDA ROC plot

Description

A Receiver Operator Characteristic (ROC) plot for PLSDA models computed by adjusting the threshold for assigning group labels from PLS predictions.

Usage

plsda_roc_plot(factor_name, ycol = 1, ...)

Arguments

factor_name	(character) The name of a sample-meta column to use.
ycol	(character, numeric, integer) The column of the Y block to be plotted. The default is 1.
...	Additional slots and values passed to struct_class.

Details

This object makes use of functionality from the following packages:

• pls

• ggplot2

Value

A plsda_roc_plot object. This object has no output slots. See chart_plot in the struct package to plot this chart object.

Inheritance

A plsda_roc_plot object inherits the following struct classes:

⁠[plsda_roc_plot]⁠ >> ⁠[chart]⁠ >> ⁠[struct_class]⁠

References

Liland K, Mevik B, Wehrens R (2023). pls: Partial Least Squares and Principal Component Regression. R package version 2.8-3, 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

M = plsda_roc_plot(
      factor_name = "V1",
      ycol = 1)

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

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

computational-metabolomics/structtoolbox documentation built on July 2, 2024, 10:46 p.m.