nlme.nlsList {nlme} | R Documentation |
If the random effects names defined in random
are a subset of
the lmList
object coefficient names, initial estimates for the
covariance matrix of the random effects are obtained (overwriting any
values given in random
). formula(fixed)
and the
data
argument in the calling sequence used to obtain
fixed
are passed as the fixed
and data
arguments
to nlme.formula
, together with any other additional arguments in
the function call. See the documentation on nlme.formula
for a
description of that function.
nlme(model, data, fixed, random, groups, start, correlation, weights, subset, method, na.action, naPattern, control, verbose)
model |
an object inheriting from class nlsList ,
representing a list of nls fits with a common model. |
data |
this argument is included for consistency with the generic function. It is ignored in this method function. |
random |
an optional one-sided linear formula with no conditioning
expression, or a pdMat object with a formula
attribute. Multiple levels of grouping are not allowed with this
method function. Defaults to a formula consisting of the right hand
side of formula(fixed) . |
other arguments |
identical to the arguments in the generic
function call. See the documentation on nlme . |
an object of class nlme
representing the linear mixed-effects
model fit. Generic functions such as print
, plot
and
summary
have methods to show the results of the fit. See
nlmeObject
for the components of the fit. The functions
resid
, coef
, fitted
, fixed.effects
, and
random.effects
can be used to extract some of its components.
Jose Pinheiro and Douglas Bates
The computational methods are described in Bates, D.M. and Pinheiro
(1998) and follow on the general framework of Lindstrom, M.J. and Bates,
D.M. (1988). The model formulation is described in Laird, N.M. and Ware,
J.H. (1982). The variance-covariance parametrizations are described in
<Pinheiro, J.C. and Bates., D.M. (1996). 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. (1997). The use of variance functions for linear
and nonlinear mixed effects models is presented in detail in Davidian,
M. and Giltinan, D.M. (1995).
Bates, D.M. and Pinheiro, J.C. (1998) "Computational methods for multilevel models" available in PostScript or PDF formats at http://franz.stat.wisc.edu/pub/NNLME/
Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994) "Time Series Analysis: Forecasting and Control", 3rd Edition, Holden-Day.
Davidian, M. and Giltinan, D.M. (1995) "Nonlinear Mixed Effects Models for Repeated Measurement Data", Chapman and Hall.
Laird, N.M. and Ware, J.H. (1982) "Random-Effects Models for Longitudinal Data", Biometrics, 38, 963-974.
Lindstrom, M.J. and Bates, D.M. (1988) "Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data", Journal of the American Statistical Association, 83, 1014-1022.
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. (1996) "Unconstrained Parametrizations for Variance-Covariance Matrices", Statistics and Computing, 6, 289-296.
Venables, W.N. and Ripley, B.D. (1997) "Modern Applied Statistics with S-plus", 2nd Edition, Springer-Verlag.
data(Soybean) fm1 <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal) | Plot, Soybean) fm2 <- nlme(fm1)