Description Usage Arguments Details Value Author(s) References Examples
Calculates proportional reduction in error (PRE) and expected proportional reduction in error (epre) from Herron (1999).
1 |
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 variance-covariance 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 (UW-Milwaukee, Department of Political Science)
Herron, M. 1999. Postestimation Uncertainty in Limited Dependent Variable Models. Political Analysis 8(1): 83–98.
1 2 3 4 |
Loading required package: car
Loading required package: carData
Loading required package: effects
lattice theme set by effectsTheme()
See ?effectsTheme for details.
Warning message:
no DISPLAY variable so Tk is not available
mod1: voteleft ~ male + age + retnat + poly(lrself, 2)
mod2: voteleft ~ 1
Analytical Results
PMC = 0.607
PCP = 0.882
PRE = 0.700
ePMC = 0.523
ePCP = 0.825
ePRE = 0.634
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