m1
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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.
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m2
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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.
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Random.seed
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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.
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method
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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.
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nsim
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an optional positive integer specifying the number of simulations to
perform. Defaults to 1000.
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niterEM
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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).
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useGen
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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.
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