Description Usage Arguments Value Note Author(s) References See Also Examples
This generic function fits a nonlinear mixedeffects model in the formulation described in Lindstrom and Bates (1990) but allowing for nested random effects. The withingroup errors are allowed to be correlated and/or have unequal variances.
1 2 
model 
a nonlinear model formula, with the response on the left
of a 
data 
an optional data frame containing the variables named in

fixed 
a twosided linear formula of the form

random 
optionally, any of the following: (i) a twosided formula
of the form 
groups 
an optional onesided formula of the form 
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

correlation 
an optional 
weights 
an optional 
subset 
an optional expression indicating the subset of the rows of

method 
a character string. If 
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 nlme
representing the nonlinear
mixedeffects 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.
The function does not do any scaling internally: the optimization will work best when the response is scaled so its variance is of the order of one.
JosÃ© Pinheiro and Douglas Bates bates@stat.wisc.edu
The model formulation and computational methods are described in
Lindstrom, M.J. and Bates, D.M. (1990). The variancecovariance
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. (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, HoldenDay.
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) "RandomEffects Models for Longitudinal Data", Biometrics, 38, 963974.
Littel, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996) "SAS Systems for Mixed Models", SAS Institute.
Lindstrom, M.J. and Bates, D.M. (1990) "Nonlinear Mixed Effects Models for Repeated Measures Data", Biometrics, 46, 673687.
Pinheiro, J.C. and Bates., D.M. (1996) "Unconstrained Parametrizations for VarianceCovariance Matrices", Statistics and Computing, 6, 289296.
Pinheiro, J.C., and Bates, D.M. (2000) "MixedEffects Models in S and SPLUS", Springer.
Venables, W.N. and Ripley, B.D. (2002) "Modern Applied Statistics with S", 4th Edition, SpringerVerlag.
nlmeControl
, nlme.nlsList
,
nlmeObject
, nlsList
,
nlmeStruct
,
pdClasses
,
reStruct
, varFunc
,
corClasses
, varClasses
1 2 3 4 5 6 7 8 
Nonlinear mixedeffects model fit by maximum likelihood
Model: height ~ SSasymp(age, Asym, R0, lrc)
Data: Loblolly
AIC BIC logLik
239.4856 251.6397 114.7428
Random effects:
Formula: Asym ~ 1  Seed
Asym Residual
StdDev: 3.650642 0.7188625
Fixed effects: Asym + R0 + lrc ~ 1
Value Std.Error DF tvalue pvalue
Asym 101.44960 2.4616951 68 41.21128 0
R0 8.62733 0.3179505 68 27.13420 0
lrc 3.23375 0.0342702 68 94.36052 0
Correlation:
Asym R0
R0 0.704
lrc 0.908 0.827
Standardized WithinGroup Residuals:
Min Q1 Med Q3 Max
2.23601930 0.62380854 0.05917466 0.65727206 1.95794425
Number of Observations: 84
Number of Groups: 14
Nonlinear mixedeffects model fit by maximum likelihood
Model: height ~ SSasymp(age, Asym, R0, lrc)
Data: Loblolly
AIC BIC logLik
238.9662 253.5511 113.4831
Random effects:
Formula: list(Asym ~ 1, lrc ~ 1)
Level: Seed
Structure: Diagonal
Asym lrc Residual
StdDev: 2.806185 0.03449969 0.6920003
Fixed effects: Asym + R0 + lrc ~ 1
Value Std.Error DF tvalue pvalue
Asym 101.85205 2.3239828 68 43.82651 0
R0 8.59039 0.3058441 68 28.08747 0
lrc 3.24011 0.0345017 68 93.91167 0
Correlation:
Asym R0
R0 0.727
lrc 0.902 0.796
Standardized WithinGroup Residuals:
Min Q1 Med Q3 Max
2.06072906 0.69785679 0.08721706 0.73687722 1.79015782
Number of Observations: 84
Number of Groups: 14
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