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, ...)
- a formula expression as for regression models, of the form
response ~ predictors. See the documentation of
formulafor other details.
- an optional data frame in which to interpret the variables occurring
- a weight matrix.
- expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
- a function to filter missing data.
- logical: if true
Wspecifies the inverse of the weight matrix: this is appropriate if a variance matrix is used.
- method to be used by
- should the model frame be returned?
- should the design matrix be returned?
- should the response be returned?
- a list of contrasts to be used for some or all of
- additional arguments to
The problem is transformed to uncorrelated form and passed to
- 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.
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