Carry out one or more independent smoothingsplines mixedeffects model fits simultaneously
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object 
a 
tme 
for consistency with the generic function. Ignored in this case 
ind 
for consistency with the generic function. Ignored in this case 
verbose 
if 
lambda.mu 
in the case of carrying out a single model fit, either a smoothing parameter to
be used for the fixedeffect function or 
lambda.v 
in the case of carrying out a single model fit, either a smoothing parameter to
be used for the randomeffects functions or 
maxIter 
maximum number of iterations to be performed for the EM algorithm 
knots 
location of spline knots. If 
zeroIntercept 
experimental feature. If 
deltaEM 
convergence tolerance for the EM algorithm 
deltaNM 
(relative) convergence tolerance for the NelderMead optimisation 
criteria 
one of 
... 
additional arguments used when carrying out multiple fits, specifically

The default behaviour is to use an incidence matrix representation for the smoothingsplines. This
works well in most situations but may incur a high computational cost when the number of distinct
time points is large, as may be the case for irregularly sampled data. Alternatively, a basis
projection can be used by giving a vector of knots
of length (much) less than the number of
distinct time points.
In the case of a single model fit, an object of class sme
. For multiple model fits, a list
of such objects. See smeObject
for the components of the fit and plot.sme
for
visualisation options
Maurice Berk maurice@mauriceberk.com
Berk, M. (2012). Smoothingsplines Mixedeffects Models in R. Preprint
smeObject
, sme
, sme.list
, plot.sme
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