| 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.
## S3 method for class 'nlsList':
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. |
fixed |
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). |
groups |
an optional one-sided formula of the form ~g1
(single level of nesting) or ~g1/.../gQ (multiple levels of
nesting), specifying the partitions of the data over which the random
effects vary. g1,...,gQ must evaluate to factors in
data. The order of nesting, when multiple levels are present,
is taken from left to right (i.e. g1 is the first level,
g2 the second, etc.). |
start |
an optional numeric vector, or list of initial estimates
for the fixed effects and random effects. If declared as a numeric
vector, it is converted internally to a list with a single component
fixed, given by the vector. The fixed component
is required, unless the model function inherits from class
selfStart, in which case initial values will be derived from a
call to nlsList. An optional random component is used to specify
initial values for the random effects and should consist of a matrix,
or a list of matrices with length equal to the number of grouping
levels. Each matrix should have as many rows as the number of groups
at the corresponding level and as many columns as the number of
random effects in that level. |
correlation |
an optional corStruct object describing the
within-group correlation structure. See the documentation of
corClasses for a description of the available corStruct
classes. Defaults to NULL, corresponding to no within-group
correlations. |
weights |
an optional 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
within-group errors. |
subset |
an optional expression indicating the subset of the rows of
data that should be used in the fit. This can be a logical
vector, or 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. |
method |
a character string. If "REML" the model is fit by
maximizing the restricted log-likelihood. If "ML" the
log-likelihood is maximized. Defaults to "ML". |
na.action |
a function that indicates what should happen when the
data contain NAs. The default action (na.fail) causes
nlme to print an error message and terminate if there are any
incomplete observations. |
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 nlmeControl.
Defaults to an empty list. |
verbose |
an optional logical value. If TRUE information on
the evolution of the iterative algorithm is printed. Default is
FALSE. |
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 Jose.Pinheiro@pharma.novartis.com and Douglas Bates bates@stat.wisc.edu
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.
fm1 <- nlsList(SSasymp, data = Loblolly) fm2 <- nlme(fm1, random = Asym ~ 1) summary(fm1) summary(fm2)