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 nlsList
nlme(model, data, fixed, random, groups, start, correlation, weights,
subset, method, na.action, naPattern, control, verbose)
"nlsList"
,
representing a list of nls
fits with a common model.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)
.~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.).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.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.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 homoscedastic
within-group errors.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."REML"
the model is fit by
maximizing the restricted log-likelihood. If "ML"
the
log-likelihood is maximized. Defaults to "ML"
.NA
s. The default action (na.fail
) causes
nlme
to print an error message and terminate if there are any
incomplete observations.nlmeControl
.
Defaults to an empty list.TRUE
information on
the evolution of the iterative algorithm is printed. Default is
FALSE
.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.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
mixed effects models is presented in detail in 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. 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. (2002) "Modern Applied Statistics with
S", 4th Edition, Springer-Verlag.nlme
, lmList
,
nlmeObject
fm1 <- nlsList(SSasymp, data = Loblolly)
fm2 <- nlme(fm1, random = Asym ~ 1)
summary(fm1)
summary(fm2)
Run the code above in your browser using DataCamp Workspace