# lm.summaries

##### Accessing Linear Model Fits

All these functions are `methods`

for class `"lm"`

objects.

- Keywords
- models, regression

##### Usage

```
# S3 method for lm
family(object, …)
```# S3 method for lm
formula(x, …)

# S3 method for lm
residuals(object,
type = c("working", "response", "deviance", "pearson",
"partial"),
…)

# S3 method for lm
labels(object, …)

##### Arguments

##### 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`

, `weights`

.

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

)
and studentized (`rstudent`

) residuals.

##### Examples

`library(stats)`

```
# NOT RUN {
##-- 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))
# }
```

*Documentation reproduced from package stats, version 3.5.0, License: Part of R 3.5.0*