residuals.polr: Residuals of a Binary or Ordered Regression
In regr0: Building regression models

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

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.

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

object

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

...

unused

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].

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

Werner A. Stahel, ETH Zurich

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

condquant, plot.regr

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