nlme (version 3.1-38)

simulate.lme: simulate lme models

Description

The model m1 is fit to the data. Using the fitted values of the parameters, nsim new data vectors from this model are simulated. Both m1 and m2 are fit by maximum likelihood (ML) and/or by restricted maximum likelihood (REML) to each of the simulated data vectors.

Usage

simulate.lme(m1, m2, Random.seed, method, nsim, niterEM, useGen)

Arguments

m1

an object inheriting from class lme, representing a fitted linear mixed-effects model, or a list containing an lme model specification. If given as a list, it should contain components fixed, data, and <

m2

an lme object, or a list, like m1 containing a second lme model specification. This argument defines the alternative model. If given as a list, only those parts of the specification that change between model m1<

Random.seed

an optional vector to seed the random number generator so as to reproduce a simulation. This vector should be the same form as the .Random.seed object.

method

an optional character array. If it includes "REML" the models are fit by maximizing the restricted log-likelihood. If it includes "ML" the log-likelihood is maximized. Defaults to c("REML", "ML"), in which

nsim

an optional positive integer specifying the number of simulations to perform. Defaults to 1000.

niterEM

an optional integer vector of length 2 giving the number of iterations of the EM algorithm to apply when fitting the m1 and m2 to each simulated set of data. Defaults to c(40,200).

useGen

an optional logical value. If TRUE, numerical derivatives are used to obtain the gradient and the Hessian of the log-likelihood in the optimization algorithm in the ms function. If FALSE, the default algo

Value

• an object of class simulate.lme with components null and alt. Each of these has components ML and/or REML which are matrices. An attribute called Random.seed contains the seed that was used for the random number generator.

See Also

lme

Examples

data(Orthodont)
orthSim <-
 simulate.lme(m1 = list(fixed = distance ~ age, data = Orthodont,
                        random = ~ 1 | Subject),
              m2 = list(random = ~ age | Subject))