Fit Linear Model Using Generalized Least Squares
This function fits a linear model using generalized least squares. The errors are allowed to be correlated and/or have unequal variances.
gls(model, data, correlation, weights, subset, method, na.action, control, verbose) ## S3 method for class 'gls' update(object, model., ..., evaluate = TRUE)
object |
an object inheriting from class |
model |
a two-sided linear formula object describing the
model, with the response on the left of a |
model. |
Changes to the model – see |
data |
an optional data frame containing the variables named in
|
correlation |
an optional |
weights |
an optional |
subset |
an optional expression indicating which subset of the rows of
|
method |
a character string. If |
na.action |
a function that indicates what should happen when the
data contain |
control |
a list of control values for the estimation algorithm to
replace the default values returned by the function |
verbose |
an optional logical value. If |
... |
some methods for this generic require additional arguments. None are used in this method. |
evaluate |
If |
José Pinheiro and Douglas Bates bates@stat.wisc.edu
The different correlation structures available for the
correlation
argument are described in Box, G.E.P., Jenkins,
G.M., and Reinsel G.C. (1994), Littel, R.C., Milliken, G.A., Stroup,
W.W., and Wolfinger, R.D. (1996), and Venables, W.N. and Ripley,
B.D. (2002). The use of variance functions for linear
and nonlinear models is presented in detail in Carroll, R.J. and Ruppert,
D. (1988) and Davidian, M. and Giltinan, D.M. (1995).
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, Holden-Day.
Carroll, R.J. and Ruppert, D. (1988) "Transformation and Weighting in Regression", Chapman and Hall.
Davidian, M. and Giltinan, D.M. (1995) "Nonlinear Mixed Effects Models for Repeated Measurement Data", Chapman and Hall.
Littel, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996) "SAS Systems for Mixed Models", SAS Institute.
Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer, esp. pp. 100, 461.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, Springer-Verlag.
# AR(1) errors within each Mare fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, correlation = corAR1(form = ~ 1 | Mare)) # variance increases as a power of the absolute fitted values fm2 <- update(fm1, weights = varPower())
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