lm.summaries {stats} | R Documentation |

All these functions are `methods`

for class `"lm"`

objects.

## 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, ...)

`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. |

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.

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

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`

.

##-- 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 2.2.1 Index]