% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/performance_pcp.R
\name{performance_pcp}
\alias{performance_pcp}
\title{Percentage of Correct Predictions}
\usage{
performance_pcp(model, ci = 0.95, method = "Herron", verbose = TRUE)
}
\arguments{
\item{model}{Model with binary outcome.}

\item{ci}{The level of the confidence interval.}

\item{method}{Name of the method to calculate the PCP (see 'Details').
Default is \code{"Herron"}. May be abbreviated.}

\item{verbose}{Toggle off warnings.}
}
\value{
A list with several elements: the percentage of correct predictions
  of the full and the null model, their confidence intervals, as well as the
  chi-squared and p-value from the Likelihood-Ratio-Test between the full and
  null model.
}
\description{
Percentage of correct predictions (PCP) for models
  with binary outcome.
}
\details{
\code{method = "Gelman-Hill"} (or \code{"gelman_hill"}) computes the
  PCP based on the proposal from \cite{Gelman and Hill 2017, 99}, which is
  defined as the proportion of cases for which the deterministic prediction
  is wrong, i.e. the proportion where the predicted probability is above 0.5,
  although y=0 (and vice versa) (see also \cite{Herron 1999, 90}).
  \cr \cr
  \code{method = "Herron"} (or \code{"herron"}) computes a modified version
  of the PCP (\cite{Herron 1999, 90-92}), which is the sum of predicted probabilities,
  where y=1, plus the sum of 1 - predicted probabilities, where y=0, divided
  by the number of observations. This approach is said to be more accurate.
  \cr \cr
  The PCP ranges from 0 to 1, where values closer to 1 mean that the model
  predicts the outcome better than models with an PCP closer to 0. In general,
  the PCP should be above 0.5 (i.e. 50\%), the closer to one, the better.
  Furthermore, the PCP of the full model should be considerably above
  the null model's PCP.
  \cr \cr
  The likelihood-ratio test indicates whether the model has a significantly
  better fit than the null-model (in such cases, p < 0.05).
}
\examples{
data(mtcars)
m <- glm(formula = vs ~ hp + wt, family = binomial, data = mtcars)
performance_pcp(m)
performance_pcp(m, method = "Gelman-Hill")
}
\references{
\itemize{
  \item Herron, M. (1999). Postestimation Uncertainty in Limited Dependent Variable Models. Political Analysis, 8, 83–98.
  \item Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press, 99
}
}
