# lm.gls

From MASS v7.3-23
by Brian Ripley

##### Fit Linear Models by Generalized Least Squares

Fit linear models by Generalized Least Squares

- Keywords
- models

##### Usage

```
lm.gls(formula, data, W, subset, na.action, inverse = FALSE,
method = "qr", model = FALSE, x = FALSE, y = FALSE,
contrasts = NULL, ...)
```

##### Arguments

- formula
- a formula expression as for regression models, of the form
`response ~ predictors`

. See the documentation of`formula`

for other details. - data
- an optional data frame in which to interpret the variables occurring
in
`formula`

. - W
- a weight matrix.
- subset
- expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
- na.action
- a function to filter missing data.
- inverse
- logical: if true
`W`

specifies the inverse of the weight matrix: this is appropriate if a variance matrix is used. - method
- method to be used by
`lm.fit`

. - model
- should the model frame be returned?
- x
- should the design matrix be returned?
- y
- should the response be returned?
- contrasts
- a list of contrasts to be used for some or all of
- ...
- additional arguments to
`lm.fit`

.

##### Details

The problem is transformed to uncorrelated form and passed to
`lm.fit`

.

##### Value

- An object of class
`"lm.gls"`

, which is similar to an`"lm"`

object. There is no`"weights"`

component, and only a few`"lm"`

methods will work correctly. As from version 7.1-22 the residuals and fitted values refer to the untransformed problem.

##### See Also

*Documentation reproduced from package MASS, version 7.3-23, License: GPL-2 | GPL-3*

### Community examples

Looks like there are no examples yet.