pre: Proportional and Expected Proportional Reductions in Error

In davidaarmstrong/damisc: Dave Armstrong's Miscellaneous Functions

Proportional and Expected Proportional Reductions in Error

Description

Calculates proportional reduction in error (PRE) and expected proportional reduction in error (epre) from Herron (1999).

Usage

pre(mod1, mod2 = NULL, data = NULL, sim = FALSE, R = 2500, ...)

Arguments

mod1	A model of class glm (with family binomial), polr or multinom for which (e)PRE will be calculated.
mod2	A model of the same class as mod1 against which proportional reduction in error will be measured. If NULL, the null model will be used.
data	The data used to fit the models.
sim	A logical argument indicating whether a parametric bootstrap should be used to calculate confidence bounds for (e)PRE. See Details for more information.
R	Number of bootstrap samples to be drawn if sim=TRUE.
...	Other arguments to be passed down.

Details

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.

Value

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 or the proportion in the modal category in the observed data if mod2 is NULL
epcp	The expected percent correctly predicted by model 1
epmc	The expected percent correctly predicted by model 2 or by the null model if mod2 is NULL.
pre.sim	A vector of bootstrapped PRE values if sim=TRUE
epre.sim	A vector of bootstrapped ePRE values if sim=TRUE

Author(s)

Dave Armstrong

References

Herron, M. 1999. Postestimation Uncertainty in Limited Dependent Variable Models. Political Analysis 8(1): 83–98.

Examples

data(france)
left.mod <- glm(voteleft ~ male + age + retnat + 
	poly(lrself, 2), data=france, family=binomial)
pre(left.mod)

davidaarmstrong/damisc documentation built on Oct. 1, 2023, 3:05 p.m.