pre {DAMisc}  R Documentation 
Calculates proportional reduction in error (PRE) and expected proportional reduction in error (epre) from Herron (1999).
pre(mod1, mod2 = NULL, sim = FALSE, R = 2500)
mod1 
A model of class 
mod2 
A model of the same class as 
sim 
A logical argument indicating whether a parametric bootstrap
should be used to calculate confidence bounds for (e)PRE. See

R 
Number of bootstrap samples to be drawn if 
Proportional reduction in error is calculated as a function of correct and
incorrect predictions (and the probabilities of correct and incorrect
predictions for ePRE). When sim=TRUE
, a parametric bootstrap will be
used that draws from the multivariate normal distribution centered at the
coefficient estimates from the model and using the estimated
variancecovariance matrix of the estimators as Sigma. This matrix is used
to form R
versions of XB and predictions are made for each of the
R
different versions of XB. Confidence intervals can then be created
from the bootstrap sampled (e)PRE values.
An object of class pre
, which is a list with the following
elements:
pre 
The proportional reduction in error 
epre 
The expected proportional reduction in error 
m1form 
The formula for model 1 
m2form 
The formula for model 2 
pcp 
The percent correctly predicted by model 1 
pmc 
The percent correctly predicted by model 2 
epcp 
The expected percent correctly predicted by model 1 
epmc 
The expected percent correctly predicted by model 2 
pre.sim 
A vector of bootstrapped PRE values if 
epre.sim 
A vector of bootstrapped ePRE values if 
Dave Armstrong
Herron, M. 1999. Postestimation Uncertainty in Limited Dependent Variable Models. Political Analysis 8(1): 83–98.
data(france)
left.mod < glm(voteleft ~ male + age + retnat +
poly(lrself, 2), data=france, family=binomial)
pre(left.mod)