residuals.probit {sampleSelection}R Documentation

Residuals of probit models

Description

Calculate residuals of probit models.

Usage

   ## S3 method for class 'probit':
   residuals( object, type = "deviance", ... )

Arguments

object an object of class probit.
type the type of residuals which should be returned. The alternatives are: "deviance" (default), "pearson", and "response" (see details).
... further arguments (currently ignored).

Details

The residuals are calculated with following formulas:

Response residuals: r_i = y_i - hat{y}_i

Pearson residuals: r_i = ( y_i - hat{y}_i ) / sqrt{ hat{y}_i ( 1 - hat{y}_i ) }

Deviance residuals: r_i = sqrt{ -2 log( hat{y}_i ) } if y_i = 1, r_i = - sqrt{ -2 log( 1 - hat{y}_i ) } if y_i = 0

Here, r_i is the ith residual, y_i is the ith response, hat{y}_i = Phi( x_i' hat{β} ) is the estimated probability that y_i is one, Phi is the cumulative distribution function of the standard normal distribution, x_i is the vector of regressors of the ith observation, and hat{β} is the vector of estimated coefficients.

More details are available in Davison & Snell (1991).

Value

A numeric vector of the residuals.

Author(s)

Arne Henningsen

References

Davison, A. C. and Snell, E. J. (1991) Residuals and diagnostics. In: Statistical Theory and Modelling. In Honour of Sir David Cox, edited by Hinkley, D. V., Reid, N. and Snell, E. J., Chapman & Hall, London.

See Also

probit, residuals, residuals.glm.


[Package sampleSelection version 0.6-4 Index]