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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