glm.summaries {stats} | R Documentation |

## Accessing Generalized Linear Model Fits

### Description

These functions are all `methods`

for class `glm`

or
`summary.glm`

objects.

### Usage

```
## S3 method for class 'glm'
family(object, ...)
## S3 method for class 'glm'
residuals(object, type = c("deviance", "pearson", "working",
"response", "partial"), ...)
```

### Arguments

`object` |
an object of class |

`type` |
the type of residuals which should be returned.
The alternatives are: |

`...` |
further arguments passed to or from other methods. |

### Details

The references define the types of residuals: Davison & Snell is a good reference for the usages of each.

The partial residuals are a matrix of working residuals, with each column formed by omitting a term from the model.

How `residuals`

treats cases with missing values in the original
fit is determined by the `na.action`

argument of that fit.
If `na.action = na.omit`

omitted cases will not appear in the
residuals, whereas if `na.action = na.exclude`

they will appear,
with residual value `NA`

. See also `naresid`

.

For fits done with `y = FALSE`

the response values are computed
from other components.

### References

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

Hastie, T. J. and Pregibon, D. (1992)
*Generalized linear models.*
Chapter 6 of *Statistical Models in S*
eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

McCullagh P. and Nelder, J. A. (1989)
*Generalized Linear Models.*
London: Chapman and Hall.

### See Also

`glm`

for computing `glm.obj`

, `anova.glm`

;
the corresponding *generic* functions, `summary.glm`

,
`coef`

, `deviance`

,
`df.residual`

,
`effects`

, `fitted`

,
`residuals`

.

influence.measures for deletion diagnostics, including
standardized (`rstandard`

)
and studentized (`rstudent`

) residuals.

*stats*version 4.4.0 Index]