splendid_graphs: Discriminating graphs

In AlineTalhouk/splendid: SuPervised Learning ENsemble for Diagnostic IDentification

Discriminating graphs

Description

Graphs a discrimination_plot, reliability_plot, and roc_plot based on true classes and predicted class probabilities.

Usage

discrimination_plot(x, probs)

reliability_plot(x, probs)

roc_plot(x, probs)

Arguments

x	true class labels
probs	matrix of predicted class probabilities. Number of rows must equal length of x

Details

A discrimination_plot shows boxplots of the predicted probabilities for each class, separated by panels of the true class labels. The class prevalence is also drawn as a horizontal line for each panel.

A reliability_plot shows mean prediction vs. observed fraction on lowess smoother for each class. A line going thru the origin with slope of 1 serves as a reference for perfect reliability.

A roc_plot shows the multi-class ROC curves for each class using 1-Specificity vs. Sensitivity.

All plots can be called from within evaluation().

Value

ggplot objects for the desired plot

Author(s)

Dustin Johnson, Derek Chiu

References

http://onlinelibrary.wiley.com/doi/10.1002/sim.5321/abstract

Examples

data(hgsc)
class <- attr(hgsc, "class.true")
set.seed(1)
training.id <- sample(seq_along(class), replace = TRUE)
test.id <- which(!seq_along(class) %in% training.id)
mod <- classification(hgsc[training.id, ], class[training.id], "xgboost")
pred <- prediction(mod, hgsc, test.id, class = class)
discrimination_plot(class[test.id], attr(pred, "prob"))
reliability_plot(class[test.id], attr(pred, "prob"))
roc_plot(class[test.id], attr(pred, "prob"))

AlineTalhouk/splendid documentation built on Feb. 23, 2024, 9:37 p.m.