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

View source: R/residuals.probit.R

Calculate residuals of `probit`

models.

1 2 |

`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 *i*th residual,
*y_i* is the *i*th 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 *i*th 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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