R: Fit Linear Models by Generalized Least Squares

lm.gls {MASS} | R Documentation

## Fit Linear Models by Generalized Least Squares

### Description

Fit linear models by Generalized Least Squares

### 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

`gls`, `lm`, `lm.ridge`

[Package MASS version 7.3-18 Index]