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.
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
random
with values suitable for a call to
lme. This argument
defines the null model.
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 and
m2 need to be specified.
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 case both methods are used.
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 algorithm in
ms for functions that
do not incorporate gradient and Hessian attributes is used. Default
depends on the
pdMat classes used in
m1 and
m2:
if both are standard classes (see
pdClasses) then
defaults to
TRUE, otherwise defaults to
FALSE.
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:
EXAMPLES:
orthSim <-
simulate.lme(m1 = list(fixed = distance ~ age, data = Orthodont,
random = ~ 1 | Subject),
m2 = list(random = ~ age | Subject))