gnls | R Documentation |
This function fits a nonlinear model using generalized least squares. The errors are allowed to be correlated and/or have unequal variances.
gnls(model, data, params, start, correlation, weights, subset,
na.action, naPattern, control, verbose)
model |
a two-sided formula object describing the
model, with the response on the left of a |
data |
an optional data frame containing the variables named in
|
params |
an optional two-sided linear formula of the form
|
start |
an optional named list, or numeric vector, with the
initial values for the parameters in |
correlation |
an optional |
weights |
an optional |
subset |
an optional expression indicating which subset of the rows of
|
na.action |
a function that indicates what should happen when the
data contain |
naPattern |
an expression or formula object, specifying which returned values are to be regarded as missing. |
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 |
an object of class gnls
, also inheriting from class gls
,
representing the nonlinear model fit. Generic functions such as
print
, plot
and summary
have methods to show the
results of the fit. See gnlsObject
for the components of the
fit. The functions resid
, coef
, and fitted
can be
used to extract some of its components.
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 Carrol, R.J. and Rupert,
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.
Carrol, R.J. and Rupert, 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.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, Springer-Verlag.
Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.
corClasses
,
gnlsControl
, gnlsObject
,
gnlsStruct
,
predict.gnls
,
varClasses
,
varFunc
# variance increases with a power of the absolute fitted values
fm1 <- gnls(weight ~ SSlogis(Time, Asym, xmid, scal), Soybean,
weights = varPower())
summary(fm1)
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