ggs_rocplot: Receiver-Operator Characteristic (ROC) plot for models with...

In ggmcmc: Tools for Analyzing MCMC Simulations from Bayesian Inference

Description

Receiver-Operator Characteristic (ROC) plot for models with binary outcomes

Usage

ggs_rocplot(D, outcome, fully_bayesian = FALSE)

Arguments

D	Data frame whith the simulations. Notice that only the posterior outcomes are needed, and so either the previous call to ggs() should have limited the family of parameters to pass to the predicted outcomes.
outcome	vector (or matrix or array) containing the observed outcome variable. Currently only a vector is supported.
fully_bayesian	logical, false by default. When not fully Bayesian, it uses the median of the predictions for each observation by iteration. When TRUE the function plots as many ROC curves as iterations. It uses a a lot of CPU and needs more memory. Use it with caution.

Value

A ggplot object

Examples

data(binary)
ggs_rocplot(ggs(s.binary, family="mu"), outcome=y.binary)

Example output

Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: tidyr
Loading required package: ggplot2
Loading required namespace: coda

ggmcmc documentation built on Feb. 10, 2021, 5:10 p.m.