Residuals of probit models
Calculate residuals of probit models.
## S3 method for class 'probit' residuals( object, type = "deviance", ... )
object |
an object of class |
type |
the type of residuals which should be returned. The alternatives are: "deviance" (default), "pearson", and "response" (see details). |
... |
further arguments (currently ignored). |
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 ) / √{ \hat{y}_i ( 1 - \hat{y}_i ) }
Deviance residuals: r_i = √{ -2 \log( \hat{y}_i ) } if y_i = 1, r_i = - √{ -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 = Φ( x_i' \hat{β} ) is the estimated probability that y_i is one, Φ 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).
A numeric vector of the residuals.
Arne Henningsen
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.
probit, residuals,
residuals.glm, and probit-methods.
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