R: Accessing Linear Model Fits

lm.summaries {stats} | R Documentation

## Accessing Linear Model Fits

### Description

All these functions are `methods` for class `"lm"` objects.

### Usage

    ## S3 method for class 'lm':
    family(object, ...)

    ## S3 method for class 'lm':
    formula(x, ...)

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

    ## S3 method for class 'lm':
    labels(object, ...)

    weights(object, ...)

### Arguments

`object, x` | an object inheriting from class `lm`, usually the result of a call to `lm` or `aov`.
`...` | further arguments passed to or from other methods.
`type` | the type of residuals which should be returned.

### Details

The generic accessor functions `coef`, `effects`, `fitted` and `residuals` can be used to extract various useful features of the value returned by `lm`.

The working and response residuals are ‘observed - fitted’. The deviance and pearson residuals are weighted residuals, scaled by the square root of the weights used in fitting. The partial residuals are a matrix with each column formed by omitting a term from the model. In all these, zero weight cases are never omitted (as opposed to the standardized `rstudent` residuals, and the `weighted.residuals`).

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`.

The `"lm"` method for generic `labels` returns the term labels for estimable terms, that is the names of the terms with an least one estimable coefficient.

### References

Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

### See Also

The model fitting function `lm`, `anova.lm`.

`coef`, `deviance`, `df.residual`, `effects`, `fitted`, `glm` for generalized linear models, `influence` (etc on that page) for regression diagnostics, `weighted.residuals`, `residuals`, `residuals.glm`, `summary.lm`.

influence.measures for deletion diagnostics, including standardized (`rstandard`) and studentized (`rstudent`) residuals.

### Examples

    ##-- Continuing the  lm(.) example:
    coef(lm.D90)# the bare coefficients

    ## The 2 basic regression diagnostic plots [plot.lm(.) is preferred]
    plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's
    abline(h=0, lty=2, col = 'gray')

    qqnorm(residuals(lm.D90))

[Package stats version 2.6.2 Index]