gls(model, data, correlation, weights, subset, method, na.action, control, verbose)
+
operators, on the right.
model
,
correlation
,
weights
, and
subset
. By default the variables are taken from the environment in which
gls
is called.
corStruct
object describing the within-group correlation structure. See the documentation of
corClasses
for a description of the available
corStruct
classes. If a grouping variable is to be used, it must be specified in the
form
argument to the
corStruct
constructor. Defaults to
NULL
, corresponding to uncorrelated errors.
varFunc
object or one-sided formula describing the within-group heteroscedasticity structure. If given as a formula, it is used as the argument to
varFixed
, corresponding to fixed variance weights. See the documentation on
varClasses
for a description of the available
varFunc
classes. Defaults to
NULL
, corresponding to homoscesdatic errors.
data
that should be used in the fit. This can be a logical vector, a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
"REML"
the model is fit by maximizing the restricted log-likelihood. If
"ML"
the log-likelihood is maximized. Defaults to
"REML"
.
NA
s. The default action (
na.fail
) causes
gls
to print an error message and
terminate if there are any incomplete observations.
glsControl
.
Defaults to an empty list.
TRUE
information on the evolution
of the iterative algorithm is printed.
Default is
FALSE
.
gls
representing
the linear model fit.
Generic functions such as
print
,
plot
,
and
summary
have methods to show the results of the fit.
See
glsObject
for the components of the fit.
The functions
resid
,
coef
,
and
fitted
can be used to extract some of its components.
Box, G. E. P., Jenkins, G. M., and Reinsel G. C. (1994). Time Series Analysis: Forecasting and Control (3rd Edition). San Francisco: Holden-Day.
Carrol, R. J. and Ruppert, D. (1988). Transformation and Weighting in Regression. New York: Chapman and Hall.
Davidian, M. and Giltinan, D. M. (1995). Nonlinear Mixed Effects Models for Repeated Measurement Data. London: Chapman and Hall.
Littel, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. (1996). SAS Systems for Mixed Models. Cary, North Carolina: SAS Institute, Inc.
Venables, W. N. and Ripley, B. D. (1997). Modern Applied Statistics with S-PLUS (2nd Edition). New York: Springer-Verlag.
The different correlation structures available for the
correlation
argument are described in
Box, Jenkins, and Reinsel (1994),
Littel, Milliken, Stroup, and Wolfinger (1996),
and Venables and Ripley (1997).
The use of variance functions for linear and nonlinear models is presented
in detail in Carrol and Ruppert (1988), and Davidian and Giltinan (1995).
# 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 with a power of the absolute fitted values: fm1 <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, weights=varPower())