NLME fit from nlsList Object

DESCRIPTION:

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.

USAGE:

nlme(model, data, fixed, random, groups, start, correlation, weights, subset, method, na.action, naPattern, control, verbose) 

REQUIRED ARGUMENTS:

model
an object inheriting from class nlsList, representing a list of nls fits with a common model.

OPTIONAL ARGUMENTS:

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.

VALUE:

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.

REFERENCES:

Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994). Time Series Analysis: Forecasting and Control (3rd Edition). San Francisco: Holden-Day.

Davidian, M. and Giltinan, D.M. (1995). Nonlinear Mixed Effects Models for Repeated Measurement Data. London: 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. (1990). "Nonlinear Mixed Effects Models for Repeated Measures Data." Biometrics, 46: 673-687.

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). New York: Springer-Verlag.

The model formulation and computational methods are described in Lindstrom and Bates (1990). The variance-covariance parametrizations are described in Pinheiro and Bates (1996). The different correlation structures available for the correlation argument are described in Box, Jenkins, and Reinsel (1994), Littel, Milliken, Stroup, and Wolfinger (1996), and Venables and Ripley (1997). The use of variance functions for linear and nonlinear mixed effects models is presented in detail in Davidian and Giltinan (1995).

SEE ALSO:

, , ,

EXAMPLES:

fm1 <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal), Soybean) 
fm2 <- nlme(fm1)