plsda_roc_plot: PLSDA ROC plot

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

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, ...)

Arguments

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

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