LME fit from groupedData Object
The response variable and primary covariate in
are used to construct the fixed effects model formula. This formula
groupedData object are passed as the
data arguments to
lme.formula, together with any other
additional arguments in the function call. See the documentation on
lme.formula for a description of that function.
lme(fixed, data, random, correlation, weights, subset, method, na.action, control)
- a data frame inheriting from class
- this argument is included for consistency with the generic function. It is ignored in this method function.
- other arguments
- identical to the arguments in the generic
function call. See the documentation on
- an object of class
lmerepresenting the linear mixed-effects model fit. Generic functions such as
summaryhave methods to show the results of the fit. See
lmeObjectfor the components of the fit. The functions
random.effectscan be used to extract some of its components.
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/NLME/ 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(Orthodont) fm1 <- lme(Orthodont) summary(fm1)