ggs_pcp: Plot for model fit of binary response variables: percent...

In ggmcmc: Tools for Analyzing MCMC Simulations from Bayesian Inference

Description

Plot a histogram with the distribution of correctly predicted cases in a model against a binary response variable.

Usage

ggs_pcp(D, outcome, threshold = "observed", bins = 30)

Arguments

D	Data frame whith the simulations. Notice that only the fitted / expected posterior outcomes are needed, and so either the previous call to ggs() should have limited the family of parameters to only pass the fitted / expected values. See the example below.
outcome	vector (or matrix or array) containing the observed outcome variable. Currently only a vector is supported.
threshold	numerical bounded between 0 and 1 or "observed", the default. If "observed", the threshold of expected values to be considered a realization of the event (1, succes) is computed using the observed value in the data. Otherwise, a numerical value showing which threshold to use (typically, 0.5) can be given.
bins	integer indicating the total number of bins in which to divide the histogram. Defaults to 30, which is the same as geom_histogram()

Value

A ggplot object

Examples

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

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