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 `glm`, typically the result of a call to `glm`.
`type` | the type of residuals which should be returned. The alternatives are: `"deviance"` (default), `"pearson"`, `"working"`, `"response"`, and `"partial"`.
`...` | 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`.

### 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`.

[Package stats version 2.3.1 Index]