Fit Linear Models by Generalized Least Squares

Fit linear models by Generalized Least Squares

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.

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.

Details

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

See also

gls, lm, lm.ridge