residuals.polr: Residuals of a Binary or Ordered Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Calculates quartiles and random numbers according to the conditional distribution of residuals for the latent variable of a binary or ordinal regression, given the observed response value. See Details for an explanation.

Usage

1
2
## S3 method for class 'polr'
residuals(object, ...)

Arguments

object

the result of polr or of glm(,family=binomial) with binary data.

...

unused

Details

For binary and ordinal regression, the regression models can be described by introducing a latent response variable Z of which the observed response Y is a classified version, and for which a linear regression applies. The errors of this "latent regression" have a logistic distribution. Given the linearly predicted value eta[i], which is the fitted value for the latent variable, the residual for Z[i] can therefore be assumed to have a logistic distribution.

This function calculates quantiles and random numbers according to the conditional distribution of residuals for Z[i], given the observed y[i].

Value

a data.frame with the variables

median

medians of the conditional distributions

lowq

lower quartiles

uppq

upper quartiles

random

random numbers, drawn according to the conditional distributions

fit

linear predictor values

y

observed response values

Author(s)

Werner A. Stahel, ETH Zurich

References

See http://stat.ethz.ch/~stahel/regression

See Also

condquant, plot.regr

Examples

1
2
3
4
5
require(MASS)
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
house.resid <- residuals(house.plr)
head   (house.resid)
summary(house.resid)

regr0 documentation built on May 2, 2019, 4:52 p.m.