| ggs_pcp {ggmcmc} | R Documentation |
Plot for model fit of binary response variables: percent correctly predicted
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)